Machine learning in optimization of multi-hole drilling using a hybrid combinatorial IGSA algorithm

Abstract

The multi-hole operation is a frequently used process in an industry. Owing to the escalating demand for reducing the production cost and time, it is inevitable for any manufacturing industry to develop an optimistic process plan. This research work mainly focuses on developing a novel combinatorial meta-heuristic hybrid technique for solving the proposed multi-hole drill sequencing problem. The integrated genetic and simulated annealing algorithm is hereby proposed and tested against assorted complex case study problems. From the results, it is evident that the proposed technique is superior in all aspects exceeding the reported optimum values. Also, this new technique consistently outperformed well with higher levels of precision and this stored data will aid the computer-aided process planning mechanism to perform well through machine learning.