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首页> 外文期刊>International Journal of Computer Integrated Manufacturing >Robust optimisation for simultaneous process mean, process tolerance and product specification determination
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Robust optimisation for simultaneous process mean, process tolerance and product specification determination

机译：强大的优化功能可同时实现过程平均值，过程公差和产品规格确定

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摘要

Conventionally, process mean determination is performed prior to process tolerance design, independent of product design, such as product specification. In addition, previous studies considered the production costs and quality losses as deterministic values in mean and tolerance decisions. This study foregoes the assumptions of deterministic value and independent determination. For further cost reduction and quality improvement, process mean, process tolerance and product specification are simultaneously determined as controllable factors under the assumption that production costs and quality losses are random variables with given probabilistic distributions. At first, the various levels of mean, tolerance and specification are combined in accordance with the Box-Behnken experimental matrix, and used as inputs for a Monte Carlo simulation to obtain the simulated outputs. Then, these outputs are transferred to the total cost, which includes quality loss, production cost and penalty cost. Mean, tolerance and specification are treated as controllable factors, while total cost is a response value of interest. The design problem is analysed statistically using response surface methodology (RSM) in order to find the response function, which in turn is used as an objective function and optimised through mathematical programming (MP). A bicurve lens design is employed to demonstrate the proposed approach.
机译：常规地，独立于诸如产品规格之类的产品设计，在过程公差设计之前执行过程均值确定。此外，先前的研究将生产成本和质量损失视为均值和公差决策中的确定性值。本研究放弃了确定性价值和独立确定性的假设。为了进一步降低成本和改善质量，在生产成本和质量损失是具有给定概率分布的随机变量的假设下，同时将过程平均值，过程公差和产品规格确定为可控制因素。首先，根据Box-Behnken实验矩阵组合各种级别的平均值，公差和规格，并将其用作蒙特卡洛模拟的输入以获得模拟输出。然后，将这些输出转移到总成本中，该总成本包括质量损失，生产成本和罚款成本。均值，公差和规格被视为可控制的因素，而总成本则是感兴趣的响应值。使用响应面方法（RSM）对设计问题进行统计分析，以找到响应函数，然后将其用作目标函数并通过数学编程（MP）进行优化。采用双曲线透镜设计来演示所提出的方法。

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