The operation of pick and place large parts is a very common operation on the manufacturing floor. The efficiency of the pick and place operation affects the efficiency of the entire manufacturing floor. Large parts in terms of this research are parts which must be transported by two robots.
This paper concludes the development of an expert system and a numerical simulation to assign two available robots to perform the pick and place operations of large parts.
The developed expert system consists of a knowledge-base structured in interrelated frames to define large manufacturing systems and an inference-engine to perform the robots matching and their performances monitoring. Matching robots considerations can not be performed by numerical algorithms in real time systems, because they are major computation time consumers. Therefore, unmatched robots can be assigned by the numerical algorithms to perform inefficient pick and place operations. The expert system and the numerical simulation were programmed, and many computer runs were performed to explore the efficiency of the two systems in a large variety of manufacturing systems.
The achieved results show that the expert system has no advantage in assigning matched robots to perform the pick and place operations in systems characterized by very short machine process times (0.5 moment to 1 moment). If such short machine process times are relevant, the knowledge-base is updated very frequently, and the inference-engine reasoning processes can not be initiated. For longer machine process times, the frequency of knowledge-base changes is reduced, and the inference-engine reasoning processes can be initiated without delays. Therefore, the advantages of the expert system become significant if machine process times are extended.