BIRLA INSTITUTE OF TECHNOLOGY

Department of Production Engineering Sujal Amrit Topno

BE/10199/2013

MANUFACTURING AUTOMATION AND

ROBOTICSModelling and Identification of Industrial Robots for Machining

Applications

Introduction:

■ Industrial robots have been in use for about 50 years. The first industrial robot was used for material handling in a General Motors facilities

■ Industrial robots offer considerable advantages if they are used at the right time, in the right place and for the right task, spanning technical as well as economic and social factors.

■ Commonly used robot configurations are articulated robots, SCARA robots, delta robots and Cartesian Coordinate Robots (gantry robots or x-y-z robots).

Applications of Industrial Robots:■ Nowadays, many different applications can be done by robots. Five of the most

popular applications of industrial robots are:– Robotic handling operations (38%)

■ This includes robotic machine tending, palatalizing and various operations for metal machining and plastic molding.

– Robotic Welding (29%)■ Mostly includes spot welding and arc welding which is mainly used by the

automotive industry. – Robotic Assembly (10%)

■ Assembly operations include: fixing, press-fitting, inserting, disassembling, etc. 

– Robotic Dispensing (4%)■ These include painting, gluing, applying adhesive sealing, spraying, etc.

– Robotic Processing (2%)■ The main application areas are mechanical, laser and water jet cutting.

Factors affecting Machining Applications:

■ The major fields of cutting applications for industrial robots are prototyping, cleaning and pre-machining of cast parts as well as end-machining of middle tolerance parts.

■ Tasks performed by a robot manipulator require the robot to interact with its environment, such as pushing, scraping, deburring, grinding, pounding, polishing, twisting, cutting, excavating, etc.

■ Besides realizing it’s current position, system should provide the necessary force to either overcome the resistance from the environment, or comply with environment.

■ Integration of task goals like modelling the environment, position, velocity and force feedback, and adjustment of the applied torque to the robot joints.

■ These methods can be further categorized as fundamental robot force control algorithms and advanced robot force control strategies.

Factors affecting Machining Applications (contd.):

■ 1.1 Fundamental force control (Table I)– A classification of robot force control algorithms based on

application of the relationship between position and applied force or between velocity and applied force, or the application of direct force feedback, or their combinations.

■ 1.2 Advanced force control (Table II)– The advanced force control algorithms are based on adaptive

control, robust control, and learning methods integrated or combined with the fundamental methods.

■ 1.3 Stability– Stability is an important factor to application and implementation

of robot force control. Many research results of the stability problems associated with force control.

Modelling of the Robot Structure:■ The considered manipulator consists of a series of

links connected by revolute joints. With the direct kinematics one can calculate the position and orientation of the end effector as a function of the joint variables q.

■ The calculation of the joint variable dependent homogeneous transformation matrices is usually done by the well-known Denavit-Hartenberg convention.

Modelling of the Robot Structure (contd.):■ The mapping between static forces applied to the

end effector and resulting torques at the joints is described by a matrix, termed Jacobian. The Jacobian has as many rows as there are degrees of freedom (normally 6) and the number of columns is equal to the number of joints n with the column vectors.

Conclusion:

■ The machining experiments showed that the static path displacement is one of the major problems in robotic machining.

■ Based on the existing research results in robot force control, it may be envisaged that more work is needed in the following areas.

– More efficient filter and estimate to allow more sophisticated algorithms;

– Investigation on better stabilization and theory to decide what feedback strategy should be employed for each robot task;

– Faster learning capabilities to cope with unpredictable changes in robot and environment’s parameters;

– Stronger robustness to comply with unknown restriction and disturbance imposed by the environment.