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BIRLA INSTITUTE OF
TECHNOLOGY
Department of Production
Engineering Sujal AmritTopno
BE/10199/2013
MANUFACTURING
AUTOMATION AND ROBOTICS
Modelling 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.
– RoboticWelding (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.

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Modelling and Identification of Industrial Robots for Machining Applications

  • 1. BIRLA INSTITUTE OF TECHNOLOGY Department of Production Engineering Sujal AmritTopno BE/10199/2013
  • 2. MANUFACTURING AUTOMATION AND ROBOTICS Modelling and Identification of Industrial Robots for Machining Applications
  • 3. 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).
  • 4. 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. – RoboticWelding (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.
  • 5. 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.
  • 6. 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.
  • 7.
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  • 9. 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.
  • 10. 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.
  • 11. 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.