technology innovation - hitachi

132

Development of Platform for Integrated Management of Value Chain for Regenerative Medicine Products

Because it involves multiple stakeholders along the

series of steps from extracting a sample from the patient

through to manufacturing, transportation, and admin-

istering the treatment back to the patient, regenerative

medicine demands stringent quality control and infor-

mation traceability. Accordingly, Hitachi has developed

a workfl ow system that utilizes IoT Compas to record

the progress of work as stakeholders hand on materials

and information from one to the other.

As samples taken from the patient are managed across

a number of branching processes, the resulting manage-

ment of workfl ow information has a complex structure

with multiple instances where steps branch or come back

together again. Nevertheless, regenerative medicine infor-

mation can be managed securely and with confi dence by

handling this data in IoT Compas with its ability to record

data interrelationships, and by providing fi ne-grained con-

trol of access to the information. Th e system itself it is able

to work with a variety of other systems by modifying the

data model that defi nes the workfl ow. Hitachi plans to

make it available to the pharmaceuticals industry as a data

management system for regenerative medicine.

1 Virtualization of Robot System Integration Using Collaborative Simulator

While there is rising demand for the automation of activ-

ities such as manufacturing and logistics, the diversity of

the work makes it more important than ever that system

integration (SI) coordinates and integrates the operation

of a wide variety of robots and other machinery to take

account of individual requirements. With this, there is

also a need not only to maximize the value delivered, but

also to make the design, approval, development, accep-

tance, and other steps faster, with allowance for people

being spread across diff erent remote locations without

access to the actual hardware.

It was with this in mind that Hitachi developed its

collaborative simulator, an integrated simulation environ-

ment. By providing a number of simulators intended for

diff erent purposes and applications, and by enabling tasks

such as the sharing and presentation of data, parameters,

and analysis results to be all tied together, analysis and

design is able to proceed in a comprehensive manner

that takes in various diff erent layers, including physi-

cal phenomena, the individual and group operation of

robots, and impacts on business. Th e simulator has also

been equipped with the ability to incorporate artifi cial

intelligence (AI) algorithms for things like the automatic

2

Technology Innovation:Industry

Order product Issue manufacturing and transportation instructions

Digital space (IoT Compas)

Patient information

Manufacturing and transportation

instructionsSample collection

recordsTransportation

recordsManufacturing

recordsAdministration

records

Take sample Transport sample Culturing/manufacturing

Transport product Administer

Medical institution

Pharmaceutical company

Medical institution

Transportation provider

Pharmaceutical company (overall coordination)

Manufacturer Transportation provider

Medical institution

1 Block diagram of workflow system using IoT Compas

IoT: Internet of Things

Hitachi Review Vol. 70, No. 3 350–351

Technology Innovation: IndustryResearch & Developm

ent

133

identifi cation of bottlenecks or for optimization achieved

through integration with IoT data.

Th e simulator has also been used under entirely remote

working conditions to design a system for demonstra-

tions at trade shows that combines three diff erent types

of robot, including development of the associated coor-

dination functions. Th e fi nal hardware integration work

conducted on-site was completed in a single day, with the

combined system successfully operating in accordance

with its design, without any need for rework. As a core

technology for merging the cyber and physical spaces, the

simulator will contribute to the deployment of advanced

automation systems.

Accuracy Improvement Service for Machining through Digitized Expert Know-how

Manufacturing that is independent of areas and people

is required in the trend of restructuring manufacturing

3

Three-robot systemSimulation in early stages of system integration planning Final simulation

Physical implementation

2 Example of planning, design, and implementation using collaborative simulator

The robot system at the center of the photograph was developed with support from a NEDO-funded project (Project for Innovative AI Chips and Next-Generation Computing Technology Development/Development of Innovative AI Edge Computing Technologies) (JPNP16007)NEDO: New Energy and Industrial Technology Development Organization

Manufacturing database

Operator

Machine tool

Original NC data

Modified NC data

MaterialsCutting tools

Machine tools

Manufacturing experience database

Request digital recipe

generation

Digital recipe generation for machiningCutting tool

Workpiece

Output digital recipe

Tool position (actual)

Example of modified NC dataTool position (design value)

Machine stiffness

Tool deflection = Cutting force/Tool stiffness

Tool stiffness

Machining direction Tool deflection

Cutting forceCutting force

Workpiece

Assess & analyze

User

Machine individuality (stiffness) data

Manufacturing experience

Accuracy improvement service for machining (web-based cloud service)

Module for digital recipe generation for machining

Machining simulation that takes account of machine individuality (stiffness)

Use

Modify NC data based on tool deflection

Compensate tool deflection (generate modified NC data)

Correction

3 Overview of accuracy improvement service for machining

134

bases accelerated by COVID-19 and international rela-

tions between major powers. To realize such a situation,

product quality has to be stabilized regardless of the pro-

duction equipment and skilled workers there. Th erefore,

Hitachi has developed a technique for digital recipe-

generating technologies that digitize expert know-how

and optimize process conditions input into production

equipment. Hitachi provides an accuracy improvement

service for machining, which is one of the major process-

ing methods, as a web-based cloud service.

In this technology, cutting tool defl ection is accurately

calculated by machining simulation incorporating the

individuality of each machine tool, and numerical con-

trol (NC) data is modifi ed to compensate the calculated

machining errors. By digitizing these machine individu-

alities, which skilled workers hold as their own know-how,

the service realizes accurate machining regardless of the

machine tools and skilled workers. In the future, Hitachi

plans to add services for digital recipe-generating tech-

nologies for other processes such as plastic forming and

injection molding.

New Oil-fl ooded Screw Air Compressors with Industry-leading Energy Effi ciency

Hitachi has commenced mass-production of two new

oil-fl ooded screw air compressors (22 and 37 kW) that

adopt its proprietary impinging oil atomization mecha-

nism. Th e new compressors are the fi rst in the world*

to feature this mechanism whereby impingements

between lubricating oil causes it to atomize, thereby

promoting cooling of the compressed air by the lubri-

cant and reducing compression power. Th e result is

* Th is solution is supplied by Hitachi Solutions, Ltd.

4

world-leading energy effi ciency [6.4 kW/(m3/min), an

approximate 6% improvement on the previous model],

that satisfi es the GB1 class in China’s energy effi ciency

regulations.

Along with an approximate 1.5-fold increase in rated

operating speed, the compressor has also been downsized

by about 10% by reducing the displacement and switch-

ing to a high-speed/low-torque motor. In addition to the

product’s energy effi ciency, these measures also encourage

optimal operation of the entire compressed air supply

system by allowing compressors to be spread across the

site, contributing to a reduction in plant carbon dioxide

emissions.

Future plans include supplying the compressors to

markets outside Japan, taking advantage of the standard-

ization of core components with Sullair, LLC.

(Hitachi Industrial Equipment Systems Co., Ltd.)

Discussion Paper on Revitalizing Human-machine Interaction in Digital Society Published in Collaboration with acatech

Given the greater workforce diversity that comes with a

falling population and the greater diversity in machines

due to technologies like the IoT and AI, industry too

is seeing increasing variety in its humans and machines.

For this to bring sustainable economic growth calls

for a world where humans and machines can evolve

in tandem.

acatech, the German Academy of Science and

Engineering, has established a project that addresses this

challenge and is led by Professor Henning Kagermann,

one of the people behind Germany’s Industrie 4.0 vision,

with Yoichi Nonaka, a Senior Chief Researcher at

Hitachi’s Research & Development Group, as co-

leader. Th e results of debate by experts from Japanese

and German industry and academia were published

in September 2019 in a discussion paper entitled,

“Revitalizing Human-Machine Interaction for the

Advancement of Society.”

Th is paper described the need for a digital knowledge

platform shared by humans and machines so as to pro-

vide ways in which they can uplift one another. Giving

the name “Multiverse Mediation” to this platform, this

paper also presented examples from Japan and Germany

of fl exible industrial systems that are able to achieve the

best overall outcome by having a wide variety of humans

* As of October 2020, based on research by Hitachi Industrial

Equipment Systems Co., Ltd.

5

4 New oil-flooded screw air compressors (37 kW)



ent

135

and machines make up for each other’s strengths and

weaknesses.

Th e project is currently expanding the scope of debate

and engaging in activities to help create a society in which

humans and machines coexist.

5G-powered Real-time Edge AI Framework for Realizing Effective Remote Robot Control

Today’s trend of multi-tiered systems requires IoT system

to integrate with multiple technologies and co-exist with

6

Former Today Future

Multiverse MediationTradition

Support

Machine Machine Machine

Operation

Human

Human Human Human

lti

nen MaMutual Aid

ana

Mutual Aid

Mul MedM l

Mutual Aidersetion Mutual Aid

Human

5 Evolution of human-machine interaction

5G-powered interactive remote collaboration system between skilled workers and robots

Manufacturing Railway

Real-time edge analytics (ML/AI) Robotics platform

ML/AI models/field devices management

5G infrastructure

Energy

Data fusion/data synchronization/data curation

TSN

Smart city/space

Flexible automation/optimization

Mobility maintenance/management Smart grid COVID-19

Verticals

AnalysisIoT platform

5G

Control edge

Use cases

Data

Connectivity

Physical field

Object detection/recognition

Unstructured Structured

Anomaly detection

Acoustic Vibration

Robots Human Machines Environment

Video LiDAR Motion PLC MES ERP

Machine failure detection

5G

Real-time edge AI in MEC

Large-capacity sensing data

4K/TOF camera

Robot arm5G setups

Control signal

4K video

Instruction

Remote worker

Management agent

Interactive UI

Trajectory optimization

Grasp planning/ranking

Management agent

Human/human action recognition

Signal denoising

6 Framework for 5G-powered real-time edge AI solutions, and its application to remote robot control

MEC: multi-access edge computing ML: machine learning PLC: programmable logic controller TSN: time sensitive network MES: manufacturing execution system ERP: enterprise resource planning TOF: time of fright UI: user interface LiDAR: light detection and ranging * Function blocks surrounded by the red rectangles indicate utilized in the application.

136

with control cycle times in the order of hundreds of mil-

liseconds. Th is makes it impractical for use with control

cycles of just a few milliseconds, which is what is needed

to operate robots safely given the external factors found

in the real world, such as fl oor vibration or arm collisions.

In response, Hitachi has developed a high-speed

controller system made up of a small edge controller for

executing control acquired from deep learning; a small

input/output (I/O) controller to handle sensor, motor, and

other I/O; and a real-time network linking these together.

High-speed processing is achieved by implementing it

in hardware circuits on fi eld-programmable gate arrays

(FPGAs) in the controllers. Testing has demonstrated

that this can achieve a 5-ms response time for reacting to

external events. Th e intention is to utilize the system in

actual applications equipped with these controllers.

System for Formulating and Managing Optimal Product Space Allocations and Process Plans in Factories with Wide Product Range

Because short-run manufacturing requires that the same

equipment be used for diff erent products, with those

products having diff erent sizes and taking up diff ering

amounts of space in the production process, there is

a need to formulate process plans that allow for effi -

cient space allocation and equipment use sequencing.

Unfortunately, the complexity and myriad permutations

of space and equipment use has in the past meant that

coming up with these plans has required a lot of work

by expert staff .

8

other solutions as well as partner/vendor ecosystem. To

enable this scalable solution architecture for multiple

verticals, Hitachi America, Ltd. has established a soft-

coupling approach in the fi fth-generation (5G)-powered

framework for real-time edge AI and robotics.

In order to perform realistic validation of this frame-

work, the company established a real-time and interactive

remote collaboration system between skilled workers and

robots in close collaboration with Georgia Institute of

Technology. Th e core function of this system is real-time

edge AI extracting insights not only from large-volume

sensing data relayed over dedicated 5G network, but also

human interaction data collected via intuitive human

machine interface (HMI) which, with support of 5G’s

enhanced mobile broadband (eMBB) feature, drastically

improves response time and usability for remote workers.

Th is validates the capability of the framework to explore

new age of fl exible automation in manufacturing industry,

as well as possibility of this framework to scale multiple

verticals.

(Hitachi America, Ltd.)

High-speed Controller System with Hardware-based Network Control and Deep Learning

Amid growing demand for robots as a solution to labor

shortages, progress is being made on ways of controlling

them without the need for programming by using deep

learning to provide robot operation control. Th e prob-

lem with simply combining deep learning control with

existing controllers, however, is that this is only realistic

7

60 mm

30 mm

FPGA

FPGA

FPGA

FPGA

FPGA

ExampleDetection of bad connections in wiring harnesses

Information from sensorssors

High-speed feedback

Externalfactor

Externalfactor

Realtime networkSmall edge controller

100 µs

Input data

Input data

Small I/O controller

Install close to equipment

Sensor

Sensor

Sensor

Sensor

Reflexive response(avoidance, repositioning, etc.)

Networkcontrol

Deep learningexecution

Model(pre-taught)

7 Block diagram of new control system



ent

137

new control technology takes advantage of the fact that

the wire rope length is at its shortest when there is no

such misalignment. It works by winding the wire rope

before lifting and measuring its length at the point where

all of the slack is taken in and the wire rope becomes

tensioned, moving the trolley to reduce the misalign-

ment. To detect when the wire rope becomes tensioned,

Hitachi also developed a load estimation method using

deep learning to estimate the weight of the suspended

load on the trolley. Th is is able to estimate the weight to

within 50 kg and enables tensioning of the wire rope to be

detected with high accuracy. By using these technologies,

load sway at liftoff that would have been around 200 mm

with manual operation was reduced to less than 100 mm.

In the future, Hitachi intends to further improve safety

by developing even more precise control technology.

In response, Hitachi has devised an algorithm that

eliminates delays due to lack of space by planning where

to put products while also taking account of the schedule

of equipment use. A scheduling technique that auto-

matically generates optimal process plans has also been

developed. It can be used to investigate ways of keeping

plans on track, being able to show the progress of work

in the factory by cross-checking image data from cameras

installed in the plant against the plan.

An evaluation of the new system made using historic

operational data from a short-run manufacturing plant

indicated that it could reduce work time by 20%.

Control Technology for Adjusting Initial Position of Hoist Crane

Th e labor shortage caused by the aging of skilled workers

and rising demand for cranes is increasing the number

of unskilled workers. Because such workers are unfamil-

iar with how to prevent load sway, this brings a height-

ened risk of collision, catch, and other such accidents,

and causes an increase in work time. Accordingly, with

the aim of improving safety and shortening work times,

Hitachi has developed a control technology for adjusting

the initial position of the hoist cranes that suppresses the

load sway that occurs when a suspended load is lifted off

the ground.

Th is problem of load sway at liftoff occurs if there

is any horizontal misalignment between the top of the

suspended load and the trolley used to transport it. Th e

9

Work schedule (Gantt chart)

Lot: DK30759Customer: XXXXXXProduct: XXXXXXInward goods: 4/19Dispatch: 6/26Start work: 4/19Completion: 5/17Duration: 28 days

Lot: DK30741-4Customer: XXXXXXProduct: XXXXXX

Lot: DK30741-4Customer: XXXXXXProduct: XXXXXXPart: LIM-2Process: Assembly

STEnlargement

Enlargement

B1

A13

Lot: DK30741-4Part: LIM-2Process: Preliminary drying

A6

A15A11

B2

B1

B1B2

B1

A7

A15

A13

A11A7

A6

Span

use

d

4/24 4/24Clean-

ing

MeasurementCoating

Cleaning

Drying

CoatingTidy upTidy up

Drying

Salt removalRT

BGBKLBGBKR

CLRETC1ETC2IM2

Space dataProduct data

Output

Presentation

Worker Simultaneous optimization

Image dataIn-plant camera Image comparison and analysis

Feedback

Data entry

Designer

Layout schedule

Layout Camera images

(1) Simultaneous optimization of work schedule and product layout

(2) Output of process planning

(3) Work progress presentation

8 Overview of system for formulation and management of process plans

Move trolley

No

Yes

Yes

No

Winding wire rope

Is wire rope tensioned?

(determine by deep learning)

Measure wire rope length

Load

Wire rope

Trolley

Initial position adjusting complete

Is wire rope length at minimum?

9 Control technology for adjusting initial position of hoist crane

technology innovation - hitachi

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