E-intelligence for integrated product design, manufacturing and service is becoming the core of manufacturing technology. This paper presents the current efforts at the McMaster Manufacturing Research Institute (MMRI) to implement an Internet-based computer aided engineering (CAE) facility for the development of new manufacturing processes. The facility, termed The Centre for Online Manufacturing Optimization (COMO), integrates advanced process simulation software, a remote machine monitoring system, and state of the art multimedia and Internet technologies, to realize a virtual manufacturing process optimization environment. This environment leads the manufacturing engineer through a set of procedures in which physics based process simulations allow process parameters and operational sequences to be evaluated and optimized with respect to desired product features. Once the process parameters are established, the part can be prototyped at the MMRI laboratory or the industrial facility. The Internet-based computer engineering facility will provide companies with predictive intelligence tools for process development, and production monitoring. A case study is presented.

The design and implementation of force control for robotic rigid disk grinding are described. Experiments were conducted using a PUMA 762/VAL II industrial robot equipped with a 4-hp pneumatic grinder and a JR/sup 3/ force sensor. An external, 386-based host microcomputer, communicating with VAL II online, performs the force control algorithm calculations. The robotic grinding force model used was an experimentally verified analytic model. It was found that the grinding forces are very sensitive to the robot arm stiffness. Also, the end-effector path tracking errors, caused by the limited accuracy of the PUMA robot, significantly affect the grinding forces. The experimental results show, however, that a finely tuned PID force-feedback controller is able to maintain the grinding forces at a specified value. It can effectively compensate for force errors caused by both step force disturbances and robot path-tracking errors. The benefits of such force control are demonstrated by improved profiles of finished workpieces.>

A knowledge-based system approach for designing an adaptive controller is introduced. The scheme has been used successfully in designing a self-tuning controller for force regulation in a computer numerically controlled (CNC) milling machine. In this scheme, frames are used for knowledge representation and rules of logic for reasoning. This synergistic combination of frames and rules provides the environment for intelligent control. As a consequence of representing knowledge in frames, the large amount of logic that goes along with most conventionally designed adaptive controllers to ensure safe operation is considerably reduced. Procedural attachments to the slots in the frame replace the extra logic elements in the knowledge-based controller. The self-tuning controller for the CNC milling machine is implemented on a 32-b microprocessor-based computer running at 20 MHz. The knowledge representation and the reasoning process are implemented in Prolog, whereas the numerical algorithms are written in C. Simulations and experimental results are provided that demonstrate the usefulness of this approach.>