Path optimization of CNC PCB drilling using hybrid Taguchi genetic algorithm

Abstract

Purpose – In this study, the hybrid Taguchi genetic algorithm (HTGA) was used to optimize the computer numerical control-printed circuit boards drilling path. The optimization was performed by searching for the shortest route for the drilling path. The number of feasible solutions is exponentially related to the number of hole positions. The paper aims to discuss these issues. Design/methodology/approach – Therefore, a traveling cutting tool problem (TCP), which is similar to the traveling salesman problem, was used to evaluate the drilling path; this evaluation is considered an NP-hard problem. In this paper, an improved genetic algorithm embedded in the Taguchi method and a neighbor search method are proposed for improving the solution quality. The classical TCP problems proposed by Lim et al. (2014) were used for validating the performance of the proposed algorithm. Findings – Results showed that the proposed algorithm outperforms a previous study in robustness and convergence speed. Originality/value – The HTGA has not been used for optimizing the drilling path. This study shows that the HTGA can be applied to complex problems.

[Retracted] Optimization Design and Economic Development of Cruise Travel Routes Based on Wireless Communication Networks and Genetic Algorithms

“…Directly transform the objective function into a fitness function [14]; if the objective function is a maximization problem, then…”

The Combination of a Fuzzy Analytical Hierarchy Process and the Taguchi Method to Evaluate the Malaysian Users’ Willingness to Pay for Public Transportation

“…In addition, this method is able to cope with component variations during the developmental phase of the products/processes, as well as minimize the variation of the product/process' targeted value. The significant advantages of the Taguchi method, over other optimizing techniques, are that multiple factors can be considered at once [57], reducing the number of experiments [58], and reducing the experimental cost [59].…”

Application of Basketball Training System Based on Dynamic Intelligent Fog Computing Network

“…is paper improves the basis of the basic genetic algorithm, uses a linear weighting method to merge multiple targets into one target, and assigns a Mobile Information Systems different weight to each target, which is set according to the degree of preference requested by the user [19]. For the three optimization goals proposed in this paper, the three goals are first normalized, and then the fitness function is established according to the preference weight, so that multiple optimization goals are combined into one objective function [20]. Among them, α, β, c are the optimization weights of the three objectives.…”

“…e average node degree of the network topology graph is set to 3, and the connection probability between nodes is 0.5. It can be seen from Table 5 that the parameters of this experiment are set as follows: the range of task number is [20,200], the range of task calculation is [5,100] MB, the range of task communication is [5,100] MB, the task requires nonforward node, and the data range is [5,50] MB.…”