Nonlinear optimisation in tolerance charts ? A study of objective functions

Ngoi, B. K. A.; Agarwal, Mukul; Chua, Chin-Seng

doi:10.1007/bf01304621

Cited by 3 publications

(1 citation statement)

References 14 publications

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“…Graphical methods were developed as an alternative to the existing models. Tolerance charts (Ngoi et al ., 1998; Shen et al ., 2008) were developed. For each product, a special chart with a product drawing was developed.…”

Section: Introductionmentioning

confidence: 99%

An explicit methodology for manufacturing cost–tolerance modeling and optimization using the neural network integrated with the genetic algorithm

Saravanan

Jerald

Rani

2020

AIEDAM

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The objective of the paper is to develop a new method to model the manufacturing cost–tolerance and to optimize the tolerance values along with its manufacturing cost. A cost–tolerance relation has a complex nonlinear correlation among them. The property of a neural network makes it possible to model the complex correlation, and the genetic algorithm (GA) is integrated with the best neural network model to optimize the tolerance values. The proposed method used three types of neural network models (multilayer perceptron, backpropagation network, and radial basis function). These network models were developed separately for prismatic and rotational parts. For the construction of network models, part size and tolerance values were used as input neurons. The reference manufacturing cost was assigned as the output neuron. The qualitative production data set was gathered in a workshop and partitioned into three files for training, testing, and validation, respectively. The architecture of the network model was identified based on the best regression coefficient and the root-mean-square-error value. The best network model was integrated into the GA, and the role of genetic operators was also studied. Finally, two case studies from the literature were demonstrated in order to validate the proposed method. A new methodology based on the neural network model enables the design and process planning engineers to propose an intelligent decision irrespective of their experience.

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Section: Introductionmentioning

confidence: 99%