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SPECIAL REPORT:SMART FACTORY/IIOT

JUNE 2021

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SMART FACTORY/IIOT SPECIAL REPORT JUNE 2021 1

CONTENTS

FEATURES 2 Modern HMI Software Propels

Industry 4.0

6 Quality 4.0: How Wireless IoT Sensor Networks are Reshaping Manufacturing

11 Cost-Effective Printed Sensors for the IoT/IIoT

16 Machine Health and Asset Monitoring in Industrial Applications: A Look at Sensor Technologies

20 Why Traceability is an Essential Foundation for IIoT-Enabled Manufacturing Systems

APPLICATION BRIEFS23 Renishaw and Hartford Combine to

Deliver Smart Factory Solutions

25 Immersive Mixed Reality: Moving Automation Technologies to the Cloud

ON THE COVERAs connected smart devices are implemented around the world, human-machine interface (HMI) software is providing graphical, connectivity, and IT-based features manufacturers require. These advances are improving machine performance and enabling greater profitability. See page 2.

(Image: ThinkHubStudio/Shutterstock.com)

Modern HMI Software Propels

Industry 4.0

Unified HMI software pairs the best of IT and OT, easing compatibility and enhancing connectivity across the enterprise.

SMART FACTORY/IIOT SPECIAL REPORT2 JUNE 2021

Human-machine interface (HMI) software is continually improving, now providing IT and operations technology

(OT) capabilities. Once confined to the role of machine and process visualization and control, modern unified HMI software now delivers better user interfaces, containerization, and remote device management — all wrapped up in a cybersecure package.

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Figure 1. Siemens WinCC Unified HMI devices provide support for multitouch gesture recognition, along with Web technologies like HTML5, SVGs, and JavaScript. (Credit: Siemens) T

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Modern User InterfaceThe new wave of HMI software,

running on dedicated de vices or PCs, is more attuned with the sleek development and runtime environments of modern smartphones than it is with the clunky interfaces of antiquated predecessors. Support for multitouch gestures — and native Web technologies like HTML5, scalable vector graphics (SVGs), and JavaScript — is increasingly commonplace (Figure 1).

This functionality gives developers the ability to customize and animate HMIs and the move from pixel- to vector-based graphics greatly improves on-screen aesthetics and machine visualization.

Configuration and UsageModern unified HMIs ship with pre-

installed apps for viewing documents, watching instructional media clips, and securely accessing external Web-based T

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systems. Additionally, native and third-party apps are available for purchase to:• Perform advanced production

algorithms and calculations• Connect to data from multiple sources

over multiple protocols including MQTT• Visualize historical data• Automate workflows• Manage inventory• Analyze machine and motor drive

health for predictive maintenance• Create notifications and send alerts

Machine builders can use these and other pre-built apps or they can develop their own apps and open application programming interfaces. To increase compatibility, unified HMIs utilize a docker engine — running each app in its own container — greatly simplifying the management of application versions and dependencies (Figure 2).

Unified HMI software also enables users to establish and monitor production key performance

indicators and to include this data in business process reporting.

Secure Device and App Management

In addition to new IT capabilities, unified HMI software utilizes common development and runtime environments across all visualization devices — control-room computers, smartphones, tablets, panel HMIs, and other platforms — increasing development efficiency on the OT side. These visualization interfaces share a common library of application objects, SVGs, and scripts, reducing the time and money required to bring additional devices online.

Management of these devices is made simple through a Web-based interface independent of an OT automation project file, enabling storage of apps and licenses on servers. Furthermore, ad min istrators can remotely deploy or update apps, apply security patches, and manage content of all unified HMIs across an enterprise (Figure 3).

Apps run in the background full time, with their hooks in the docker, independently of the runtime layer executing the classical automation project, so a change in app configuration does not impact HMI runtime. Commu-nication among devices running unified HMI software is encrypted and HMIs can be configured for automatic system backup to prevent data loss.

Software for the FutureAs an increasing number of

enterprise-connected smart devices are implemented around the world, unified HMI software is providing modern graphical, connectivity, and IT-based features while maintaining the robustness manufacturers require from industrial HMI runtime software. These advances are improving machine performance, enabling greater profitability, and fueling connected enterprises through the Industry 4.0 revolution.

This article was written by Ramey Miller, HMI/Edge product marketing manager for Siemens Industry (Norcross, GA). For more information, visit http://info.hotims.com/79410-280.

Docker Engine

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SIMOTIONTraceConnector

Flow Creator

Figure 2. Each app has its own container, hosted by a unified HMI’s docker. (Credit: Siemens)

Figure 3. The right industrial apps — like these available from Siemens — ease connectivity between the cloud and plant-floor devices. (Credit: Siemens)

Modern HMI Software


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Quality 4.0:

How Wireless IoT Sensor Networks are Reshaping Manufacturing Quality control is fundamental

in every industry but in manufacturing it’s hyper-critical. Volatile market

demand and high material and production costs — with the mission-critical nature of end products — impel manufacturers to pursue nothing but first-rate quality and a minimal rejection rate. With the Internet of Things (IoT) gradually hitting its stride across the manufacturing industry, quality management is an area with transformational opportunities.

The Quality Management Challenge at a Glance

Effective quality management relies on the ability to constantly monitor and control a host of machine and process parameters that impact product quality. To ensure product properties are consistent and up to par, equipment recalibration is constantly performed as process drifts and other changes in the production line crop up. Yet, with the growing complexity of tooling systems and manufacturing processes, many process

variables are left unattended due to the limits of bulky wired networks.

While ideal for high-throughput, time-sensitive automation tasks, wired communications lack the flexibility and affordability needed to capture telemetry data at scale and beyond the machine level. Typically, factors like environmental conditions, despite their major influence on quality variability, are often not studied and controlled. For example, in auto manufacturing, unfavorably low room temperature can reduce the W

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Figure 1. Simple IoT network architecture. (Image courtesy of BehrTech)

quality of 3D-printed components by causing them to cool too quickly.

What’s more, designed in the last century, the majority of wire-driven industrial systems aren’t intended for data exchange beyond the factory floor. This creates disconnected islands of data that aren’t available to enhance production efficiency and throughput. Instead, process optimization and quality management often depend on reactive, manual post-production inspection. Besides expensive human intervention, this introduces significant

quality variability and associated costs, while making it challenging to trace the root cause of quality issues.

Enter Industry 4.0: Proactive Quality Management

The pressing quest for improved process visibility speaks to the tremendous potential of IoT and its counterpart, Industry 4.0, for proactive quality management.

Wireless IoT networks capture a large number of granular critical datapoints along the production line; for example,

pressure, vibration, temperature, and humidity. With potentially thousands of sensors installed onsite, data is collected as frequently as every 10 – 20 seconds and sent via a base station to the user’s preferred backend system, whether on-premises or in the cloud. Using a remote IoT platform, all sensor data is consolidated for real-time monitoring, actionable insights, and process automation. Alerts can be triggered immediately when any off-spec conditions among running equipment and processes arise. This W

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offers manufacturers unprecedented control over their operations and product outputs. Beyond reactive, end-of-run quality inspection, IoT data empowers a proactive quality assurance approach to diagnose and prevent defects much earlier in the process for peak production throughput and repeatability. This also leads to reduced costs and waste. Concurrently, it provides valuable insights for achieving and maintaining best practices.

Five Leading Applications for Proactive Quality Management

1. Condition Monitoring and Predictive Maintenance

IoT sensors capture and communicate key health and operational metrics like pressure, vibration, temperature, humidity, and voltage of numerous machines and equipment across the entire industry complex (condition monitoring). Besides generating an insightful picture of current production processes and asset performance, these massive data flows power analytical models to proactively predict an impending issue and schedule demand-based inspection and repair (predictive maintenance). For example, high humidity in the gearbox diminishes the performance of rotary components, resulting in corrosion, impaired product quality, or even machine breakdown. Excessive vibration of motors and pumps suggest possible mounting defects, shaft misalignment, and bearing wear. With predictive

maintenance, failures can be prevented ahead of time, thereby maximizing asset utilization and reducing costly losses due to downtime.

2. Environmental MonitoringAmbient conditions can play a

significant role in production and quality management. With the help of environmental sensors that measure temperature, humidity, and air quality, plant operators can remotely monitor and control optimal environments for various factory-wide processes from their command center. For instance, maintaining ideal air pressure differential prevents dust infiltration in the manufacturing area, thereby securing product quality in the pharmaceutical and microelectronics industries. Gluing and painting processes in automotive production can be improved with optimal humidity level. Likewise, accurate temperature monitoring of processing and storage facilities can ensure product safety in the food industry.

3. Asset Tracking and ManagementIoT sensors attached to individual

assets such as tools, machinery, and vehicles, capture and report detailed information about current conditions as well as where and how they are being used. By having a holistic, real-time picture of cross-site assets, operators can quickly pinpoint underutilized equipment, diagnose impending issues and bottlenecks, and easily mobilize tools and parts. Ultimately, the application of IoT for asset management enables organizations to optimize maintenance

activities and asset useful life, while eliminating error-prone manual records and excessive orders.

4. Remote Pipeline and Tank Monitoring

Tanks and pipelines are critical assets in many process industries. Overflow or leakage of chemical products and gases not only leads to production losses but also causes serious damage to the environment and threatens public safety. Implementing level, vibration, flow rate, and pressure sensors, businesses can keep an eye on the structural health of their widely distributed tanks and pipelines round the clock, while simultaneously reducing manual checks. Alerts are issued about potential spills, leaks, or ruptures that could lead to disasters. Alerts about low levels of material in tanks can also be issued for timely refilling to improve productivity.

5. Facility ManagementIoT enables digitized management

and protection of critical plant facilities. IoT-enabled elevators, smoke detectors, fire alarms, and other facility resources across the entire factory can periodically send data on their battery health or “alive” status. This helps manufacturers cut down on time-consuming manual inspection, while being able to quickly respond to any issues that could interrupt the production line.

Future-Proof Wireless Connectivity for Quality 4.0

With data acquisition an inherent challenge in most industrial environ-

Condition Monitoring &Predictive Maintenance

EnvironmentalMonitoring

Asset Tracking &Management

Remote Pipeline& Tank Monitoring

FacilityManagement

Figure 2. Quality management applications. (Image courtesy of BehrTech)

Wireless IoT Sensor Networks

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ments, IoT deployments can often appear to be overwhelmingly complex, expensive, and intimidating. It is predicted that there will be 36.8 billion active IIoT devices by 2025, up from 17.7 billion today. As more companies look to capitalize on new IoT applications, it’s important to consider the long-term reliability, integrability, and manageability of the communication network as it scales to accommodate thousands of connected endpoints. The reality is, it all boils down to choosing the right IoT connectivity for the right business case.

Wireless instrumentation isn’t necessarily new to manufacturing but crucial requirements in terms of range, power, and ease of integration limit the viable options; for example, industrial monitoring applications could require millions of messages a day to be sent from thousands of sensors. This demands a highly scalable and power efficient solution to avoid frequent battery replacement and disposal that can quickly inflate total cost of ownership. Likewise, vast, structurally dense industrial facilities require reliable wireless communication that can travel a long distance and negotiate physical obstructions. The traditional design of manufacturing facilities also creates challenges. Wireless solutions

must be able to integrate with legacy equipment such as PLCs to break down data silos and provide access to previously inaccessible information.

Legacy wireless technologies can’t keep up with the range, power, and cost requirements in IoT sensor networks. Traditional cellular connectivity (e.g. 3G, LTE, etc.) and wireless local area networks (Wi-Fi) are too expensive and power hungry for transmitting small amounts of data from a large number of sensor devices. Other solutions like Bluetooth, Zigbee, and Z-Wave have highly constrained physical range and even though many of them employ a mesh topology to extend their coverage, multi-hop relaying is power-consuming, while entailing complex network planning and management. As such, mesh networks are suitable for medium-range applications at best.

Low-power wide area networks (LPWAN) are unique in that they overcome these pitfalls and deliver an efficient, affordable, and easy-to-deploy solution for massive-scale IoT networks. The appeal of LPWAN is derived from its two signature features: long range and low power consumption. While Wi-Fi and Bluetooth can only communicate over tens or a hundred meters at best, an LPWAN is able to transmit signals up to 15 km in rural areas and up to 5 km in

urban, structurally dense areas. On top of that, lightweight, power-optimized protocols reduce transceiver costs while enabling a very long battery life for sensor nodes.

It's important to note that quality of service varies across LPWAN technologies. This is mainly due to two reasons: their operations in the license-free spectrum and the use of simple asynchronous communication, typically pure ALOHA (a node accesses the channel and sends a message whenever there is data to send). While bringing significant power benefits, uncoordinated transmissions in asynchronous networks greatly increase the chance

of packet collisions and data loss. As wireless IoT deployments and radio traffic in the license-free sub-GHz bands rapidly grow, legacy LPWANs potentially come with serious quality of service (QoS) and scalability challenges caused by co-channel interference. In the same regard, the standardization and reliable mobility support are other critical factors not to be overlooked.

Wrapping UpThe ability to identify hidden

patterns, predict future issues, forecast usage and costs, and derive insights from IoT sensor data will reshape the industrial process forever. While the sector has been adopting communication technology for some time, new wireless connectivity like LPWAN is helping to bring vastly more data points online at a much lower price tag. Amidst compounding industry challenges, IoT implementation can be a turning point to take quality management and operational efficiency to the next level and stay on top of the competition.

This article was written by Wolfgang Thieme, Chief Product Officer, BehrTech (North York, ON, Canada), For more information, contact Mr. Thieme at wthieme@ behrtech.com or visit http://info.hotims.com/79411-160.

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Figure 3. Comparison of wireless technologies. (Image courtesy of BehrTech)

Wireless IoT Sensor Networks

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for the IoT/IIoT

Sensors are the heart of the IoT — and printed organic sensors can be used in ways that others cannot. They are lightweight,

flexible, stretchable, and soft, so they can bend, twist, or conform to any surface. They can be laminated onto fabric or provide a soft interaction between a robot and an object. New applications are evolving as these sensors become more sophisticated, reliable, and inexpensive. They can be productively used in areas such as communication, information processing, security, medicine, biomedical research, and environmental health. They are more sustainable since they are chemically synthesized rather than using materials that are mined from the earth. That means they can be used to make biodegradable or recyclable devices. Three of the more common types of printed organic sensors are for pressure, temperature, or gas.

Roll-to-Roll Sensor PrintingA relatively young company, started

in 2008, InnovationLab (Heidelberg, Germany) has developed a roll-to-roll system to print a wide variety of sensor types using specially designed inks. The sensors can be synthesized for a particular application using piezoresistive, piezoelectric, or capacitive technologies. Key to this

approach is that it can be used to prototype small quantities and once the design has been finalized the process can be immediately transferred to industrial scale and mass produced, both inexpensively and at high speed. That is accomplished by using standard roll-to-roll label-printing machines modified to implement known printing techniques

such as inkjet or screen printing with functional rather than graphical inks.

Printed Sensor ArraysInnovationLab prints the sensors in

the form of a matrix onto a flexible film

With accelerated time from design to production, specialized foil sensor arrays will be expanding to many new areas.

Figure 1. Industrial production of printed sensors with a Gallus RCS 430 at Heidelberger Druckmaschinen AG. (Photo courtesy of InnovationLab)

Cost-EffectivePrinted Sensors


— typically, PET, PEN, or TPU — with the thickness of the film on the order of microns. The sensors are arranged in a matrix of up to a million per square meter. The matrix of sensors functions something like pixels in an image, so pressure sensors, for example, can sense not just the presence of pressure but also dynamically sense its amplitude and location.

The array is formed by printing silver stripes on the plastic substrate — first a layer of horizontal stripes (rows), then an array of sensors, and finally a layer of vertical stripes (columns). Each printed sensor is at the junction of a row and a column and can therefore be identified as a node at a unique location of a matrix. For pressure sensing, the sensors are force sensitive resistors (FSR), which change their electrical conductivity as a function of the applied pressure. You can therefore use

that property to deduce the spatial distribution of the forces on the foil.

You can use that, for example, to identify different objects based on their pressure patterns. Dr. Florian Ullrich, business developer at InnovationLab, demonstrated that by observing the pattern, you can tell whether a bottle is standing or lying. The data of just how much pressure is at which exact location can even be used to identify different objects based upon their pressure “footprints.”

Dr. Alexey Sizov, Head of System Integration and Product Development at InnovationLab, explained that “If the rows of our matrix are driven, we can read the voltage levels at the columns. We use a multiplexer to switch the rows and columns so that each pixel is read in the range of microseconds and sequentially output to a high-speed ADC.”

The data outputs could be sent via CAN bus, USB, or wirelessly to a computer for visualization and analytics and also via a gateway to a cloud.

IoT ApplicationsThe data from the Inno-

vationLab printed sensor array can be sent by CAN bus, USB, or wirelessly directly to visualization and analytics software residing in a computer or via a gateway to be networked with other IoT

devices and/or sent for analysis to a cloud. For each particular application, you decide which data you want to send. The outputs of a number of sensors can also be connected in series to a single electronics processor. That may not be as powerful as utilizing individual electronics for each sensor matrix. On the other hand, if you are designing for a specific application, it could simplify things.

There is a great range of possible applications; for example, pressure sensors can be “tuned” to respond to a wide range of forces from a few grams to a couple of hundred kilograms. And they can be printed at densities of up to a million per square meter.

Smart beds. One application that could not practically be addressed with conventional sensors is a smart bed. First, the printed sensor array could have a large enough area to cover the surface of a bed. The sensors are so thin that a person sleeping on them would not know they are there. It could be printed on thermal plastic polyurethane (TPU), which has good elasticity, is transparent, and resistant to oil, sweat, grease, and abrasion and has a nice feel to it. You could even buy it with a thermal transfer foil so you could iron or laminate it onto a bedsheet.

An important use for this bed monitoring would be locating pressure ulcers (bedsores) for a hospital patient, which are a major source of serious side effects during hospital stays. These are formed when a patient lies in one position for an extended period of time. But they can be treated, or even prevented, if they are detected early. Printed pressure sensors can be designed to be sensitive enough to detect and localize pressure points on the body and to measure their time duration. These are the factors that would indicate in advance whether an ulcer is starting to develop. In a hospital or nursing home, the data from a number of beds could be networked, transmitted to a server for analysis, and then pushed to nurses’ smartphones or to a monitor at a nurse’s station — in real time — to determine when a

Figure 2. Matrix of printed sensors. (Photo courtesy of InnovationLab)

Figure 3. A smart sensor mat with printed sensors. (Photo courtesy of InnovationLab)



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patient should be turned. It could also be stored for later analysis and record-keeping. Although these factors are different for each individual, without real-time information all patients would have to be turned at the lower limit, perhaps every half hour. That would be an unnecessary waste of nurses’ time and energy.

Smart carpets. Another IoT application arose because of the social distancing requirements for COVID-19. InnovationLab produced a “smart carpet” mat, which they installed in a large supermarket to promote social distancing and control the number of customers allowed into the store at any one time. Because of the high density of the sensor matrix — more than 8,000 sensors spaced at 1 cm — and their sensitivity, the data sent to a processor is able to distinguish between a person and a shopping cart. And because it is lightweight and flexible and connects wirelessly, it can be rolled up and carried by hand to different locations.

Smart factory. By lining stocking shelves with sensor arrays, you can track the fill levels of the stock in a factory, see it on a central screen, then order prior to a shortage. With an increased level of integration, you could autonomously generate a refill order. This would pay for itself by keeping production running without downtime.

Further sensor types could detect fill levels, touch events, temperature, moisture, and gases.

Smart warehouse. Sensor foils placed throughout a warehouse could be used to keep track of all of the goods stored there; for example, picking places where goods are exchanged. A person might receive an online order to pick up three items from shelf A and two from shelf B. Weight-sensing foil underneath the piles of goods would output a signal confirming that the proper number of items were removed from the correct locations. For this application, the foil

outputs would be connected by CAN bus to central processing electronics that sends out the digital data.

Automotive charging stations. If someone driving a hybrid or all-electric vehicle needs an immediate recharge, pressure sensors at each parking spot could send out information about which, if any, places are available.

Battery health monitoring. Battery cells expand and contract during charge/recharge cycles. A sensor coating could detect that and use the information to balance cell use, prevent overcharging, measure temperature, and by these means optimize the battery life.

Car seat monitor. The foil can be integrated into a car seat to measure the force profile of a person and analyze it to sense a person’s sitting position. By training the AI, you could even identify which regular driver is sitting in the car and adjust the seat and steering wheel positions appropriately. The information can also provide a basis for various driver assistant and safety systems, e.g. seat belt reminders and emergency call systems. Sensors can detect whether a children’s seat is located on the passenger side and will automatically deactivate the airbag.

From R&D to ProductionIn order to develop and then produce

printed sensing foils for a particular

application, one needs to go through a series of stages. InnovationLab has facilities for developing and then commercializing printed sensor products. They provide R&D services and a roll-to-roll press to produce pilot runs. For the final production run, they have a partnership with Heidelberger Druckmaschinen AG, the world market leader in the manufacturing of printing presses, whose factory is located nearby.

InnovationLab has a highly modified Gallus RCS 330 printing press that supports prototyping and

pilot production of up to one million (finger-sized) sensors per day. The press can accommodate substrate widths up to 33 cm and unlimited length. It can use screen, offset, flexo, or gravure as well as an option for inkjet printing. Heidelberg’s production site features a more highly developed Gallus RCS 430 printing press that is solely used for the industrial production of printed sensors, run in a three-shift operation.

A critical piece of the design process is developing the right ink for each application. For that, InnovationLab partners with major suppliers such as BASF SE.

Why Printed Sensors?First of all, we’ve outlined some of

the unique applications for the variety of sensors that can be printed on plastic foils. Their key characteristics are their flexibility, lightness, and low cost. By using a roll-to-roll printing process on a modified standard press, you can go right from a pilot run to mass production with minimal effort and expense. Once the initial costs have been paid off, as the production quantity increases, the cost per foil is vastly reduced, mostly determined only by material costs and printing speed.

This article was written by Ed Brown, Editor of Sensor Technology. For more information, visit http://info.hotims.com/76507-160.

Figure 4. Flexible printed sensors can be processed on ultra-thin and flexible substrates allowing them to be integrated into densely packed system applications such as car seats. (Photo courtesy of InnovationLab)




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Use Cases

Precision CNC

Conveyer Belt

Pulley

Crane

Hydraulic Hose

Wellhead

Steam trap

Mixing tank

Vibrational Data

MCSA

Axial Flow

Noise Monitoring

Air Flow

Liquid Flow

Pressure Data

Liquid Level

Temperature Data

Humidity Data

Seismic Data

Temp Sensor LPWAN

WirelessHART

ISA 100.11a

Cellular

Zigbee

Camera

HumiditySensor

PressureSensor

Level Sensor

Gas Sensor

ProximitySensor

AcousticSensor

ChemicalSensor

Accelerometer

Current Sensor

IndustrialApplication

Asset Monitoring

Process Monitoring/Predictive Maintenance

Machine HealthMonitoring

PotentialEquipment

Data PotentialSensors

PotentialProtocols

Machine Health & Asset Monitoring in Industrial Applications:A Look at Sensor Technologies

Figure 1. Industrial applications and their respective data and technologies.

The data gained from monitoring remote equipment is critical to the functionality of any industrial process.

Often, this data is handled by a Supervisory Control and Data Acquisition (SCADA) control system often via an Ethernet and TCP/IP network over a bus, star, or tree topology. Industrial Internet of Things (IIoT) systems are often augmenting and in some cases, replacing these legacy systems to allow for a wireless network of nodes connected to a gateway that leads back to the cloud for more complex data processing and analytics. Regardless of the use of wired or wireless technologies, the underlying sensors used in these processes provide the backbone

16 JUNE 2021 SMART FACTORY/IIOT SPECIAL REPORT

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for the data required for assessing and analyzing plant equipment.

This article provides a birds-eye view of industrial machine health and asset monitoring applications as well as an overview of some of the commonly used sensor technologies.

Machine Health and Asset Monitoring Applications in the IIoT

Remote industrial machine health and asset monitoring applications span a massive range of industry verticals with a variety of sensor types used in tandem with wireless protocols to achieve real-time or quasi-real-time data transmissions. In the more traditional SCADA architecture, sensor/ actuator nodes connect to industrial I/O modules — often programmable logic controllers (PLCs) or remote terminal units (RTUs). These I/O modules send sensor data to and from nodes based on feedback from supervisory computers — often Human Ma chine Interfaces (HMI) — gather and disseminate data based upon human input.

In the Industrial Wireless Sensor Net-work (IWSN), a number of sensor nodes wirelessly connect to a gateway in a point-to-multipoint (PtMP) topology via a licensed/unlicensed band and particular wireless protocol. In industrial applications, this can vary from in dustry-specific protocols such as Wire lessHART, to cellular-based networks, to more commercial protocols such as Zigbee. This bypasses the wiring of separate I/O modules found in the SCADA architecture, compressing this hierarchy to simplified data transfers from sensor nodes, to a gateway/base station, to a centralized cloud-based platform to perform more complicated analytics.

The applications of IWSNs for ma-chine condition monitoring include in dustrial positioning equipment and motors/drives as well as asset monitoring applications (Figure 1). Inductive motors, for instance, are found in a huge range of machine equipment, from precision CNC machines to large industrial cranes, pulleys, and conveyor belts. Any faults in these machines can

degrade mechanical accuracy or even cause a failure and factory downtime, directly diminishing valuable plant operational time with the additional cost of repair time. Some common mechanical failures for motors are: rotor bar cracking, short winding fault, air gap variations, and bearing faults.

Accelerometers are most commonly leveraged for vibration data analysis — most mechanical faults in rotating machines lead to a detectable in-crease in vibration levels. Additional measurements include Motor Current Sig nature Analysis (MCSA) where distortions in the current waveforms from a motor can extrapolate the particular fault based upon the amplitude of the peak and the frequency at which the peak occurs. This method of measurement is often accomplished by means of a clip-on current transformer (CT).

Aside from accelerometers and current sensors, temperature, humidity, pressure, and level sensors are leveraged often in IWSNs. In asset monitoring applications, for instance, tracking the tank fill level for chemical, food, and pharmaceutical mixing tanks is paramount in ensuring ingredients are put in at precise values. In these cases, pressure sensors can be used or various liquid level sensors can be used to measure the fill level of the tank. Airflow or liquid flow monitoring can be accomplished using both pressure and liquid sensors as well in industrial air filtration systems or in commercial HVAC systems. In water treatment and management facilities, filters exhibit pressure differentials at the influent (input) and effluent (output) lines where performance and clogging can be tracked and detected by pressure sensors.

A number of underlying fundamental principles (optical, electromagnetic, radar, mechanical, ultrasonic, acous tic, etc.) can be leveraged to accomplish the same sensing outcome. This variety can be found for level, humidity, and temperature sensors. The choice of technology is a balance among price, accuracy, form factor, ease

of installation/calibration, response rate, and continuous or discrete monitoring. The next sections will touch upon some of the commonly leveraged sensors in IWSNs.

A Look at Commonly Used Sensors

Accelerometers – As stated earlier, accelerometers are a cornerstone component for the monitoring of machine equipment for vibrational data. This occurs by collecting parameters such as acceleration, deceleration, and shock from voltage data. This is turned

Shunt Resistor

Current Transformer

Rogowski Coil

Hall Effect Sensor: Open-Loop

Hall Effect Sensor: Open-Loop Amplifier

Amplifier

Vout

VoutLoadResistor

Magnetic Core

Magnetic Core

Drive Current

Drive Current

OutputCurrent

Magnetic Core

BurdenResistor Output

VoltageAnd

OutputCurrent

Air Core

Secondary Winding

Amplifier Integrator

Return wire

Current Carrying Conductor




Current

Figure 2. Common current sensor topologies.


into vibrodiagnostics in either the time-domain or frequency-domain. In the time-domain analysis, the collection and distribution of signal samples allow for the noticeable change in machine behavior over time. One simple form of time-domain vibration analysis involves defining “alarm limits” with the Root Mean Square (RMS) velocity of the machine’s housing (ISO 2372 standard).

Time-domain analysis generally has the setback of the inability to catch faults earlier, as more data needs to be collected to note an observable difference; however, time waveforms have the major benefits of classifying an event that is transient or intermittent. In the frequency-domain, the various faults yield apparent differences in spectral power content (i.e., peaks in vibration velocity at various frequencies) that allow for better fault isolation. While time-domain analysis is often leveraged to examine issues that are either already known or exhibit very specific patterns that are searched for, frequency-domain analysis allows for a broader survey of machine operation where identifying faults is far more apparent. Multi-axis accelerometers are particularly valuable, as they are able to collect data in both the axial and radial directions. Accel erometers can follow one of these fundamental principles: capacitive, piezoelectric, or piezoresistive.

The most commonly used are capacitive accelerometers where a spring-suspended proof mass shifts into unbalance under acceleration stress. This displacement is then registered by electrodes with a change in capacitance that ultimately yields an acceleration rate and acceleration direction. Piezoelectric accelerometers also use a proof mass; however, shifts in the proof mass instead cause shear stress to the piezoelectric material that translates directly to an electrically output. Similar to the pressure and level sensors listed in the previous sensors, an accelerometer

can also exploit the piezoresistive principle using a proof mass and strain gauges to yield an acceleration result.

Current Sensor – Current sensor industrial applications can include MCSA analysis for machine equipment, smart metering, and in applications involving power supplies (e.g., inverter control, uninterruptible power supplies, welding, etc.). Current sensors leverage one of four basic principles: Ohm’s law, Faraday’s law, Faraday’s effect, or magnetic field sensing.

A resistive-shunt-type current sensor would leverage Ohm’s law and consists of a resistive element that acts in series to the current-carrying conductor whose current value is desired. This way, some of the current passes through the element, causing a voltage drop that is proportional to the current flowing through it.

Figure 2 illustrates an overview of various sensor technologies. Current transformers (CT) exploit Faraday’s law of induction. The transformer involves multiple windings around a magnetic core of high magnetic permeability. The primary winding, or the current-carrying conductor, can either be of a few turns or simply a line passing through the core. The AC flowing through the primary winding concentrates the magnetic flux lines within the core, or flux concentrator, which in turn induces a current within the secondary winding that is directly proportional to the current within

the primary winding, offering a measurement of the current flow.

A rogowski coil uses the same principle, instead with a core with a magnetic permeability similar to air. The in duced voltage within the secondary winding is proportional to the time-derivative of the desired current. Therefore, the secondary winding in a rogowski coil is terminated with an op-amp integrator circuit.

Hall-effect magnetic field sensors are also leveraged in either an open-loop or closed-loop architecture. The Hall Effect simply describes the perpendicular voltage vector that is generated in the presence of a current and magnetic field flowing through a strip of metal. An open-loop configuration looks similar to the current transformer in that the current-carrying conductor passes through the center of a magnetic core of high magnetic permeability. A Hall effect sensor is placed within a gap in the core, creating a voltage that is proportional to the current. This voltage, however, requires an amplifier, as the output voltage is small.

A closed-loop configuration instead involves a compensation coil, or secondary winding, that produces a field that opposes the current in the current-carrying conductor so that no magnetic field is seen at the Hall-effect sensor. The secondary winding is driven by amplifiers in the current sensing IC and is terminated with a load resistance. The current in the current-carrying conductor is proportional to the voltage at this output resistor.

Pressure Sensor – The term pressure sensor is generally used as an all-encompassing term that includes pressure sensor, pressure transducers and pressure transmitters. In general, pressure sensors produce a 10-mV output signal where this output signal can be used 10 to 20 feet from electricals without noticeable signal loss. Pressure transducers produce higher

Transmitter

Diaphragm

-OUT

+OUT

+IN

-IN

Amplifying CircuitR3

R4

R5

R1

R2

Strain gauge

Pres

sure

Cut-off Valve

Pipe

+

-

Figure 3. Piezoresistive pressure sensor basic diagram.

A Look at Sensor Technologies


voltage outputs (0.5 to 4.5 V) that can travel beyond 20 feet without signal degradation. Pressure transmitters offer a current output signal of 4 to 20 mA. Pressure sensors can come in a number of configurations including Wheatstone Bridge-type/piezoresistive, capacitive, electromagnetic, piezoelectric, and optical.

This article focuses on the most common type of pressure sensors: the bridge-type/piezoresistive configuration (Figure 3). The most common pressure sensors rely on the piezoresistive effect where the change in resistance that occurs when a material is deformed correlates to the pressure the material is under. Typically, these sensors have a measuring diaphragm where the side of the diaphragm that faces the gas/liquid (i.e., hydraulic fluid, water, oil, etc.) is exposed to a “reference” pressure while the other side of the diaphragm is exposed to high pressure. In this case, the diaphragm deflects/deforms accordingly and strain gauges measure the difference in pressure between each to transduce this information to an electrical quantity ready for transmission.

Strain gauges essentially act as resistive elements whose change in resis tance is proportional to the amount of strain put upon them. These strain gauges are either a bonded-foil

type manufactured through a sputter deposition process or a diffusion, silicon-type strain gauge that is also known as a semiconductor strain gauge, as it is produced by diffusing impurity into a silicon based diaphragm. The foil-based strain gauge has the benefit of withstanding higher pressures while the semiconductor-based strain gauge of fers a higher sensitivity so it is often leveraged at lower pressures. However, silicon strain gauges are highly influenced by temperature and therefore tend to have lower operating temperatures than foil strain gauges.

Liquid Level Sensor – Level sensors detect the amount of liquid, powders, or granular material (e.g., pellets) within a container. Not unlike the pressure sensor, this measurement can be ac-complished in a variety of ways. The table above lists some of the methods with a description and some major considerations for each type of level sensor. This section will focus on the hydrostatic, diaphragm-based sensor.

The hydrostatic level sensor, in particular, relies on the same fundamental piezoresistive principles as the bridge-type sensor found in the pressure sensor. In fact, this type of liquid level sensor is a pressure sensor where the rising/falling level of liquid within a tank correlates to a change in pressure within the

diaphragm and thus maintains a highly linear relationship with the depth of liquid in the tank. As shown in the equation below, the static pressure (P) of liquid is equivalent to the specific gravity of the liquid (γ) and the height of the liquid (h).

P = γ * h

ConclusionUnderstanding the underlying

sensor technologies used in industrial monitoring applications can offer insight for anyone involved in the design and development of industrial systems. Each sensor can leverage a variety of fundamental principles, each of which has its own respective benefits and considerations for the application. The collection and dissemination of the data acquired from these sensors can either involve a wired or wireless backbone where IIoT in particular has the potential for more complex data analytics for future industrial applications.

This article was written by Tinu Oza, Product Line Manager at L-com, North Andover, MA. For more information, visit http://info.hotims.com/76510-290.

Reference1 Lewis, Joe. Solids Level Measurement and

Detection Handbook. Momentum Press, 2014. https://www.electronicdesign.com/

Level Sensor Type

Description Non-intrusive Moving Parts

Considerations

Ultrasonic Sensor sends an ultrasonic pulse and detects the return echo. The time of flight indicates is mathematically correlated to the fluid level.

Yes No Care when choosing mounting locations, dust can affect measurement, internal vessel obstructions can impact measurements

Optical Small prism mounted at the end of two optical fibers extended within a vessel. A light beam travels within the vessel and the return beam will contain level information based on the detected increase of the index of refraction, deducing the amount of light that has escaped into the liquid.

No No Debris or vibrations from filling can damage the sensitive sensor

Magnetic A magnetic float is placed atop the fluid, the liquid level can be extrapolated from interaction between float magnetics inside the vessel and magnetic “flags” outside the vessel.

No No Sloshing will confuse the sensor, installation requires attachment to vessel

Capacitive Conductive sense electrodes are applied to the sides of the tank or inside the tank, as the liquid level changes, the amount of dielectric material between the plates changes, causing a change in capacitance.

No No Immersed in liquid, installation can require attach-ment to vessel

Radar Suspended antenna within vessel sends a pulse of microwave energy where the signal reflects off the fluid surface and back to the antenna. Time of flight is correlated to the level of liquid within the vessel.

No No Pricey, measurements sensitive to material properties

Hydrostatic Submersible sensor where the pressure exerted on an internal diaphragm is registered measuring the static pressure of the liquid column above the transmitter.

No Yes Moving parts, immersed in liquid

Level sensor types1




Why Traceability is an Essential Foundation for IIoT-Enabled Manufacturing SystemsThe importance of a proactive and systematic method for collecting machine and process data within a smart manufacturing environment cannot be overstated.

Industrial Internet of Things (IIoT) technologies can lead to a dramatic increase in production quality and throughput but they’re often not

the plug-and-play solutions that many companies in the manufacturing sector may expect. To get the most value from an IIoT solution, manufacturers need to thoroughly understand the nature of their operations and invest in a robust, real-time traceability system to collect relevant data in a proactive and systematic way.

Traceability systems make use of identification methods like barcoding and radio-frequency identification (RFID) to gather and analyze data on the movement of works-in-process and finished goods throughout the plant and supply chain. Once a relatively simplistic meth od for tracking products and components, traceability has now evolved into a powerful strategy for optimizing productivity, quality, and brand reputation within the manufacturing operation by tying products to process parameters and raw material inputs.

From Simple Product Tracking to Comprehensive Process Visibility

The transformation of traceability over time — from basic barcode reading of individual parts and products to systems that enable the in-depth investigation of bottlenecks and quality issues — offers a variety of ways to envision this ubiquitous manufacturing practice. Om ron has broken these changes down into four general phases, culminating with the Traceability 4.0 phase that merges lower-level track-and-trace solutions with advanced Industry 4.0 and IIoT technologies.

Traceability 1.0 is about automatically identifying products to drive accuracy and efficiency. The ability to mark a part and then track it using barcode readers was groundbreaking and this strategy has improved manufacturing efficiency and accuracy during the processing of large numbers of discrete items or transactions.

Traceability 2.0 is about managing inventory and meeting the needs of society. Manufacturers recognized additional uses for barcodes — particularly the ability to track materials within the manufacturing facility and throughout the supply chain. This strategy has enabled targeted product recalls, reduced the cost of quality improvements, and in-creased consumer confidence.

Traceability 3.0 is about optimizing manufacturing and supply chain secu-rity by focusing on the raw material components and subcomponents need-ed to build a product as well as the finished product with an encoded se rial number. This helps ensure product authenticity and provides a strong foundation for anti-counterfeiting programs.

Traceability 4.0 is the union of all of the above, along with machine and process parameters to achieve the highest level of quality, productivity, and overall equipment effectiveness. Although some manufacturers have embraced Traceability 4.0, it represents the future for most. Those who adopt the strategy ascend to the forefront of manufacturing and brand protection.

TRACEABILITY

1.0TRACEABILITY

2.0TRACEABILITY

3.0TRACEABILITY

4.0

1970s 1980s 2000s 2020s

Product ID visibility

Supply chain visibility

Line item visibility

Process visibility

4 phases are distinct and overlap, to bring the full value of Traceability

Traceability has transformed over time from basic barcode reading of individual parts and products to systems that enable in-depth investigations of process issues.


It is this final — and cumulative —stage of traceability where IIoT be comes fully supported and functional. With the types of data that Traceability 4.0 brings in, manufacturers can easily answer a variety of production-related questions such as which machine worked on which product at what time and who was operating the machine at that time. The potential diagnostic and process analytic scenarios are virtually limitless and substantial improvements arise in many areas when the relevant machine and process data is collected systematically.

Driving Manufacturing Decisions IIoT solutions, in effect, build a bridge

between the lower-level processes

happening on the plant floor and the overarching business goals. The key ingredient in this holistic view of a company’s manufacturing operations is data, which is acquired, organized, and utilized by means of a traceability system. When implementing a traceability system, the following questions should be considered in order to help define requirements.• How will upstream components or raw

materials be confirmed to be compliant based on information en coded in a barcode, RFID tag, or other identifier?

• Through what process does a particular part move during production?

• What production tooling, process parameters, and testing scripts should

be used when performing a specific process step for a given item in a flexible manufacturing environment?

• Which components are used on a specific subassembly?

• What data should be collected at each process step and how should that data be made available to higher-level MES or Historian applications?

• What real-time decisions can be made based on collected data?

When manufacturers implement a traceability system that considers the above factors, they’ll be able to support increasingly complex and delicate pro-cesses. Ultimately, even the most basic components — like door switches or proximity sensors — will be network-

Some traceability systems use RFID, with each part or bin being given a reusable and unique tag that is read and written to by the reader/writer.


capable. Assembly verification, quality assurance, and bill of material (BOM) control can all be effectively optimized with a Traceability 4.0 strategy that em-ploys smart manufacturing technologies like IO-Link-enabled sensors.

IO-Link is a recent innovation that underpins many “smart” devices to pro-vide a connection between the sensor/actuator and an interface module that helps garner more information from the sensors themselves beyond a basic ON/OFF reading. Process values, parameters, and diagnostic messages can now be exchanged, broadening the pool of available information and allowing for a wide range of process options.

In addition to providing more data to work with, smart components also help

cut the cost of machine construction and overall maintenance by shifting from a traditional direct wire solution to a network solution for their equipment’s individual components. With smart components on a network, re placement of failed devices is literally plug-and-play and some OEMs have reported up to a 38% reduction in wiring costs.

What’s Next for Traceability and IIoT?

Artificial intelligence (AI) is in-creasingly being used to support new aspects of manufacturing. Employing these algorithms within the cloud to monitor and support processes isn’t a new thing but manufacturers are starting to pull AI out of the cloud and

push it onto the machine to impact manufacturing on a specific machine in real time. As part of a traceability system, it can identify trends when there are too many variables to allow for explicit programming.

That said, it’s important to keep in mind what AI does and what it doesn’t do. It’s basically an ad vanced way to crunch data and for that reason, it requires human expertise to determine which data to use and how to use it. Letting algorithms function as a “black box” without a solid grasp of the intricacies of the production line may not be a recipe for disaster but it’s also not a recipe for success. Manufacturers need to understand what type of information they’re collecting for each process and why that information matters.

Essentially, this is why a clear traceability strategy should be taken into account for any manufacturer’s adoption of IIoT technologies. Trace-ability, by definition, is a means of collecting and organizing factory floor data in real time. If this data is being collected haphazardly with minimal understanding of its importance, that’s not effective traceability and it’s not a workable foundation for implementing smart manufacturing solutions. IIoT-enabled smart manufacturing demands a well-organized traceability solution.

The more insight manufacturers have into their processes, the closer they’ll be to the ultimate goal of plug-and-play IIoT solutions based on specific, targeted needs. These are the type of gaps that AI fills most effectively. Although building a robust, real-time Traceability 4.0 system that truly reflects the architecture of the production line can be a daunting task, it’s not a thankless one. The immense value of such an undertaking will be seen in the ease with which data can be manipulated to offer insights.

This article was written by Felix Klebe, Marketing Manager – Sensor and Advanced Sensing, Omron Automation Americas, Hoffman Estates, IL. For more information, visit http://info.hotims.com/79411-121.

Barcoding is a highly popular and cost-effective method for traceability, with compact, industry-ready barcode readers featuring advanced decoding algorithms serving as an essential capability of these systems.

A high-performance barcode reader scans sample-specific information on barcodes applied to laboratory test tubes.


IIoT-Enabled Manufacturing Systems



APPLICATION BRIEFSRenishaw and Hartford Combine to Deliver Smart Factory Solutions

In the face of global skills shortages and rapidly emerging Industry

4.0 trends, Taiwanese CNC machine manufacturer Hartford undertook to develop an innovative, easier-to-use human machine interface (HMI) for its CNC machines. At the same time, the company strived to ensure process measurement and inspection at its CNC machine manufacturing operations could keep pace with ever-improving product quality goals.

BackgroundEstablished in 1965, Hartford is

Taiwan’s biggest exporter of CNC machining centers as well as being the country’s largest machining center manufacturer. It is recognized as one of the CNC machining industry’s leading global brands with a reputation for technological advancement.

Hartford’s comprehensive range of machine tools is used extensively by major manufacturers including Airbus, Boeing, CRRC Corporation, Ferrari, Ford, Foxconn, LG, Pratt & Whitney, Samsung, Siemens, and Volkswagen.

Developments in Industry 4.0 technology and worldwide labor shortages have meant that forward-thinking CNC machine manufacturers like Hartford are placing greater emphasis on automation, connectivity,

data transparency, and ease of use. However, achieving Industry 4.0’s goals of “intelligent manufacturing” and the “smart factory” still relies on accurate and effective process control systems. They need to be easy to use and provide sufficient immediate data to enable self-correction and adaptation to any sources of process variation.

Achieving the Goal of “Intelligent Manufacturing”

For Hartford — a global company that has exported 46,000 machines to 65 countries across Europe, North America, and the Asia Pacific regions — maintaining the very high quality of its product in the face of rapid technological and economic change and fierce international competition is a key consideration.

At its manufacturing facility in Taiwan, the company produces a complete range of medium- to large-sized three-axis and five-axis CNC machines for use in major industry sectors including aerospace, automotive, electronics, and energy. Its product range comprises vertical machining centers, precision boring machines, and gantry-type machines.

With more than 95% of Hartford’s cast components being manufactured and machined in-house, a continuous and progressive approach towards quality inspection is essential for

achieving the precision required for a wide range of machine components, including machine heads, spindles, and automatic tool changers.

Helping customers cope with the widespread shortages of skilled labor presents Hartford with a further vital challenge to address, as Bruce Lin, Manager of Hartford’s R&D Intelligent Technology Department, explained: “Our customers are needing to process work pieces of increasing complexity, however a lack of skilled labor means they are having to insist on machining centers that are even simpler to use.”

Intelligent HMI with Renishaw App

Hartford has invested significant resources in the research and develop-ment of intelligent CNC controllers in recent years and developed Hartrol Plus. The Hartrol Plus intelligent controller is as simple to use as a smartphone.

The HMI provided by the Hartrol Plus CNC controller follows key design principles promoted by Industry 4.0 ideals and helps address skills shortages. It provides machine operators with all the information they need to make the right decisions. The way in which it visualizes data helps operators to make more informed decisions and solve problems more quickly.

Hartford CNC machines.


APPLICATION BRIEFS

Renishaw’s Set and Inspect on-machine app has been integrated with Hartford’s new controller, enabling users to exploit advances in automated measurement and data collection, making machine tool operation and human-machine interactions simpler and more intuitive. Set and Inspect is a highly visual graphical user interface (GUI) that leads the operator through every step of on-machine probing processes including workpiece setup, tool setting, and other measurement tasks.

Operators no longer need to commit machine code instructions to memory, reducing data entry errors and programming times, while increasing processing efficiency by as much as 20%.

Precision Measurement for High-Quality CNC Manufacturing

Hartford began using Renishaw products more than 20 years ago. In order to meet its stringent high-quality objectives, the company has introduced a variety of Renishaw high-precision measurement systems. The precision of all CNC machined components it manufactures is verified using Renishaw PH20 5-axis probes on coordinate measuring machines (CMMs). This happens before components enter the assembly line to ensure that they are ready to be assembled.

Precise assembly and positioning of machine tools is also critically important, with five-axis machine tools needing to be positioned with a deviation of less than ±6 µm. A Renishaw XL-80 laser interferometer is used to measure machine position and both linear and angular errors. The XL-80 generates an extremely stable laser beam with a wavelength that conforms to international standards. Linear measurement accuracy of ±0.5 ppm can be guaranteed, thanks to a precision stabilized laser source and accurate environmental compensation. Hartford uses the Renishaw QC20-W ballbar measurement system to perform cross-validation at different operating speeds to ensure that X- and Y-axes of the machine tool are correctly matched and errors are kept to less than 5 µm.

Every Hartford CNC machine undergoes 100% laser verification and ballbar testing before dispatch, and can use the customer’s own workpiece for processing verification, with Renishaw OMP40, OMP60, and RMP60 machine tool measurement probes used to measure the precision of the processed workpiece.

AxiSet™ Check-Up for Rotation Center Compensation

Hartford also uses Renishaw AxiSet Check-Up to analyze the performance

of machine rotary axes. Compatible with common 5-axis and multi-axis machines, it provides CNC machine users with a fast and accurate way to check the location of rotary axis pivot points and automatically compensate if necessary.

Importantly, AxiSet Check-Up does not need to rely on operator experience, as the operator can simply call up the relevant program and press Cycle Start to complete the test process in just a few minutes. Data is automatically recorded into parameters for use in analysis, further guaranteeing the standardization of every machine tool produced.

Said Lin, “We also recommend that users apply AxiSet Check-Up to test the machines’ rotary axes after they are installed, as factory conditions may differ significantly from Hartford’s manufacturing conditions in terms of foundations and how level surfaces are. Shipping and installation can also cause precision errors, so AxiSet Check-Up’s automatic compensation allows machine tools to maintain high levels of precision and quality.”

He continued, “All machine tools can suffer from wear and drift after a certain period of usage, with the level of precision of their positioning declining over time and causing a correspondingly poor level of machining precision. We therefore recommend that users perform scheduled checks on machine tools using AxiSet Check-Up every 6 to 12 months to ensure that the level of machining precision remains consistent and productivity remains high.”

Hartford’s ongoing commitment to stringent quality inspection has seen the company continuously embrace the latest thinking in process measurement techniques. Its imaginative use of leading-edge Renishaw measurement solutions over time has helped sustain its global competitive edge and reflects Hartford’s bold corporate philosophy: “We are here to make the best machines to the highest standards.”

For more information visit www.renishaw.com/hartford.

Set and Inspect on-machine app integrated with the Hartrol Plus CNC controller.



Immersive Mixed Reality: Moving Automation Technologies to the Cloud

Cloud platforms provide performance and scalability.

Grid RasterMountain View, CA

There are many industries where manufacturing continues to be

the backbone of the economy, where companies that produce products and goods used by both businesses and consumers have found a way to push forward during the COVID-19 pandemic. None have been more prominent than the medical technology community, where automation technologies are increasingly used to design and create critical devices used by physicians and health organizations.

Even though manufacturing continued throughout 2020, it is showing signs of a slight slowdown in a year highlighted by great economic challenges. Accord-ing to a recent report in Reuters, The Institute for Supply Management (ISM) said that its index of national factory activity fell to a reading of 55.4 in September, down slightly from

56 in August.1 Despite the slowdown, September marked the fourth straight month of growth, and it compares to an August figure that was the highest level dating back to November 2018. A reading above 50 indicates expansion in manufacturing, which accounts for 11.3 percent of the U.S. economy.

Technology Helped Manufac-turers Push Forward in 2020

Technology has helped medtech manufacturers maintain factory output despite the challenges posed by COVID-19. One technology in particular is the suite of immersive mixed reality (MR) technologies, which are best described as a fully immersive experience that brings virtual objects into the real world or one that blends the physical world with the digital one.

MR technologies, including augmented reality (AR) and virtual reality (VR), are poised to grow considerably over the next few years. In fact, more manufacturers are leveraging this technology as the AR market is expected to reach $70 to $75 billion in revenue by 2023.2

Medtech manufacturers should be cautious in how they design and deploy these technologies, because there is great difference in the platform they are built on and maximized for use. Even though technologies like AR/VR have been in use for several years, many medtech manufacturers have deployed virtual solutions that are built upon an on-premises environment, where all the technology data is stored locally. This buildout was more common a few years ago and was considered the de facto platform for this type of technology. On-premises AR/VR infrastructures limit the speed and scalability needed for today’s virtual designs for medical devices and it limits the ability to conduct knowledge sharing between organizations that can be critical when designing new products and understanding the best way for virtual buildouts.

Cloud-Based Automation Technologies Proving Pivotal

Medtech manufacturers are overcoming these limitations by leveraging cloud-based (or remote-server based) AR/VR

AR/VR platforms powered by distributed cloud architecture and 3D vision-based artificial intelligence (AI) provide the desired performance and scalability to drive innovation in the medical industry at speed and scale. (Credit: Grid Raster)

Please visit www.techbriefs.com/webinar144

Implementing Smart Factory Solutions

Speakers:

Sachin Andhare Head of Product Market-ing, dotData

Dan Skulan General Manager, Industrial Metrology, Renishaw Inc.

Jim Quinn President and CEO, Plethora

The smart factory is much more than just an automated facility. It is a fully connected digital production system that has a constant stream of data coming from sources including production machinery, pick-and-place equipment, machine vision systems for in-spection, robots of different types, and various sensors. This Webinar from the editors of Tech Briefs highlights the components and technologies that comprise the smart factory and helps you determine which ones you need and how to control them. You’ll also learn how implementing a smart factory can increase value and other benefits for your manufacturing facility.

WebinarAvailable On Demand!


APPLICATION BRIEFS

platforms powered by distributed cloud architecture and 3D vision-based artificial intelligence (AI). These cloud platforms provide the desired performance and scalability to drive innovation in the industry at speed and scale.

Enterprise-grade high-quality AR/VR platforms require both performance and scale. However, existing systems such as MS HoloLens and others are severely limited in both aspects. Most enterprises have a rich repository of existing complex 3D CAD/CAM models created over the years.

These 3D models may vary in their complexity (such as poly count, hierarchy, details, etc.), making it difficult to run and excel within on-premises virtual platform environments, restricted by device limitations. This forces developers to decimate the contents (3D models/scenes) to fit to different mobile de vices, spending months in the process and sacrificing on the overall quality of the experience.

As these virtual environments become richer and larger, the problem continues to compound. This cycle is

repeated for each of the different AR/VR hardware platforms, making it difficult for any enterprise to move from experiments and pilots to full-scale deployable solutions, thus stunting the speed of innovation and effectiveness.

The device limitations also severely restrict the capability of existing AR/VR systems to generate and work with very fine mesh with large polygon count models and point clouds, which is essential to collocate and precisely fuse the virtual objects on top of physical objects in the real world with complex surfaces and varied lighting and environment.

Medtech manufacturers are overcoming this great challenge by partnering with providers of cloud-based (or remote server based) AR/VR platforms powered by distributed cloud architecture and 3D vision-based AI. These AR/VR cloud plat-forms provide the desired performance and scalability to drive innovation in the industry at speed and scale.

Manufacturers today are experiencing the next wave of technology innovation

that will fundamentally alter the way they operate. This transformation is primarily driven by merging of the digital and physical world to create a better, smarter, and more efficient way of operating. Immersive technologies such as AR/VR technologies are playing a pivotal role in this transformation.

The organizations that take a leadership role will be the ones that not only leverage these technologies but they will partner with the right technology provider to help scale appropriately without having to stunt technological growth.

This article was written by Dijam Panigrahi, Co-founder and COO of Grid Raster Inc., a provider of cloud-based AR/VR platforms based in Mountain View, CA. For more information, visit http://info.hotims.com/79413-348.

References1. “U.S. manufacturing sector slows in

September: ISM,” Reuters, Oct. 1, 2020, https://www.reuters.com/article/us-usa-economy-manufacturing-idUSKBN26M6FT.

2. Seal, A., “Top 7 Augmented Reality Trends for 2021,” vXchnge, Feb. 9, 2021, https://www.vxchnge.com/blog/augmented-reality-statistics.





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