Optimization of automated high-speed modular placement machines using knowledge-based systems

This paper introduces an optimizer for a new family of modular, multistation, walking beam, high-speed chip mounters. The objective is to optimize the machines in a manner that would streamline the use of nozzles and part feeder mechanisms and at the same time increase throughput. The optimization of these machines is a large NP-complete problem, and therefore, a heuristic search method is needed to solve the problem in reasonable time. Four knowledge-based systems are introduced to solve this problem. These systems were designed to emulate human experts, who have optimized these types of machines manually. Benchmarks were performed for 18 industrial test cases. The results show that overall, the knowledge-based systems outperformed software supplied by the vendor of the machine in both feeder slot savings and throughput. This performance represents a key improvement, and a prototype system has been implemented in our industrial partner's factory.