Purpose

The purpose of this paper is to establish a tolerance optimization method based on manufacturing difficulty computation using the genetic algorithm (GA) method. This proposal is among the authors’ perspectives of accomplished previous research work to cooperative optimal tolerance allocation approach for concurrent engineering area.

Design/methodology/approach

This study introduces the proposed GA modeling. The objective function of the proposed GA is to minimize total cost constrained by the equation of functional requirements tolerances considering difficulty coefficients. The manufacturing difficulty computation is based on tools for the study and analysis of reliability of the design or the process, as the failure mode, effects and criticality analysis (FMECA) and Ishikawa diagram.

Findings

The proposed approach, based on difficulty coefficient computation and GA optimization method [genetic algorithm optimization using difficulty coefficient computation (GADCC)], has been applied to mechanical assembly taken from the literature and compared to previous methods regarding tolerance values and computed total cost. The total cost is the summation of manufacturing cost and quality loss. The proposed approach is economic and efficient that leads to facilitate the manufacturing of difficult dimensions by increasing their tolerances and reducing the rate of defect parts of the assembly.

Originality/value

The originality of this new optimal tolerance allocation method is to make a marriage between GA and manufacturing difficulty. The computation of part dimensions difficulty is based on incorporating FMECA tool and Ishikawa diagram This comparative study highlights the benefits of the proposed GADCC optimization method. The results lead to obtain optimal tolerances that minimize the total cost and respect the functional, quality and manufacturing requirements.

中文翻译：

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基于制造难度系数和遗传算法的关联公差优化方法

目的

本文的目的是利用遗传算法（GA）方法建立一种基于制造难度计算的公差优化方法。该建议是作者对先前已完成的研究工作的观点之一，用于并行工程领域的协作最优容差分配方法。

设计/方法/途径

本研究介绍了所提出的 GA 建模。所提出的 GA 的目标函数是最小化受考虑难度系数的功能需求公差方程约束的总成本。制造难度计算基于设计或过程可靠性的研究和分析工具，如失效模式、影响和临界分析（FMECA）和石川图。

发现

所提出的方法基于难度系数计算和 GA 优化方法 [使用难度系数计算的遗传算法优化 (GADCC)]，已应用于从文献中获取的机械装配，并与以前关于公差值和计算总成本的方法进行了比较。总成本是制造成本和质量损失的总和。所提出的方法经济高效，通过增加公差和降低组件缺陷率来促进困难尺寸的制造。

原创性/价值

这种新的最优公差分配方法的独创性是将遗传算法与制造难度结合起来。零件尺寸难度的计算基于结合 FMECA 工具和 Ishikawa 图这项比较研究突出了所提出的 GADCC 优化方法的好处。结果导致获得最佳公差，从而最大限度地降低总成本并满足功能、质量和制造要求。