Metaheuristic approaches to tool selection optimisation

Abstract

In this paper we discuss our approach to solving the tool selection problem, specifically applied to rough machining. A simulation is used to evaluate tool sequences, which provides accurate values for tool paths and a 3D model of the final machined part. This allows for a largely unrestricted search using different tool types, making this approach more useful for real world applications than previous attempts at solving the problem. An exhaustive search of every valid tool sequence is executed and shows that assumptions present in related research can prevent the optimal solution from being discovered. Metaheuristic algorithms are used to traverse the search space because of its complex combinatorial properties. Four algorithms are tested -Genetic Algorithm, Stochastic Hill Climbing, Hybrid Genetic Algorithm and Random Restart Stochastic Hill Climbing. Evaluating their performance at coping with two competing demands, finding optimal solutions and keeping the number of potentially expensive evaluations low, it is shown that RRSHC performs best in terms of solution accuracy but at the greatest computational cost. SHC finds the optimum sequence less frequently but needs far fewer evaluations and the HGA lies somewhere in between, making it a good choice if the problem domain is not well-specified.

A geometry search approach in case-based tool reuse for mould manufacturing

“…First, the HD approach could be improved by searching for an hierarchical optimal decomposition. Second, current adaptation of FFS retrieval is designed for layer-based roughing only; how to adapt it to 'remove all possible material roughing techniques' classified in [12] needs to be studied. Currently, the case-based approach can be used as reference for tool reuse; the final decision is still up to the users, and it cannot replace the optimization-based approach.…”

“…If the tool is too large, then a smaller tool will be used [12]. DSouza et al used a graph-based method to optimize this kind of roughing technique [10].…”

“…In the work of Ahmad et al [19], good solutions were found by GA by defining that the last tool of the sequence is fixed before the search. Recent studies introduce metaheuristic algorithms to find sets of good solutions, and it is up to the user to select the best suited algorithm [12].…”

“…Conventionally, the selection of appropriate cutting tools relies on experience of domain expert. Heuristic, analytical and metaheuristic approaches were explored to automate the tool selection process in previous research [8][9][10][11][12]. With simplification and abstraction, these algorithms solve for single or multiple optimization goals.…”

Multi-objective tool sequence and parameter optimization for rough milling applications

“…This optimization is a complicated problem, as machine tools are available in a variety of shapes and sizes, and operate optimally with different problem specific parameters. In previous work, the authors introduced a new, evolutionary approach combining single and multi-objective algorithms with a realistic simulator to find near-optimal tool sequences while using a strict budget of expensive evaluations [2] [3]. This work was successful in producing industrially relevant tool sequences while using fewer assumptions than previous models, such as those found in [4][5] [6].…”