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A generalized approach to determine the optimal manufacturing target for arbitrary loss functions and process models

A generalized approach to determine the optimal manufacturing target for arbitrary loss functions... The deviation of a quality characteristic from its design target is typically modeled using mathematically well-tractable models, such as Taguchi’s quadratic loss function. By incorporating a process distribution model, the optimal manufacturing target can be determined analytically—for a subset of loss functions and process models. In this article, the manufacturing target is instead interpreted as the result of a combination between two signals, the loss function, and the process model. This allows to approximate the optimal manufacturing target for arbitrary loss functions and process models by using signal processing techniques. Numerical results of the proposed method are compared to analytical solutions obtained from literature, demonstrating accurate approximations. The generalizability of the approach to determine the optimal manufacturing target under the presence of multiple quality characteristics is discussed and demonstrated. As an application that can benefit from the model’s flexibility, an application example from the metrology field is discussed. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png The International Journal of Advanced Manufacturing Technology Springer Journals

A generalized approach to determine the optimal manufacturing target for arbitrary loss functions and process models

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References (34)

Publisher
Springer Journals
Copyright
Copyright © 2018 by Springer-Verlag London Ltd., part of Springer Nature
Subject
Engineering; Industrial and Production Engineering; Media Management; Mechanical Engineering; Computer-Aided Engineering (CAD, CAE) and Design
ISSN
0268-3768
eISSN
1433-3015
DOI
10.1007/s00170-018-2223-3
Publisher site
See Article on Publisher Site

Abstract

The deviation of a quality characteristic from its design target is typically modeled using mathematically well-tractable models, such as Taguchi’s quadratic loss function. By incorporating a process distribution model, the optimal manufacturing target can be determined analytically—for a subset of loss functions and process models. In this article, the manufacturing target is instead interpreted as the result of a combination between two signals, the loss function, and the process model. This allows to approximate the optimal manufacturing target for arbitrary loss functions and process models by using signal processing techniques. Numerical results of the proposed method are compared to analytical solutions obtained from literature, demonstrating accurate approximations. The generalizability of the approach to determine the optimal manufacturing target under the presence of multiple quality characteristics is discussed and demonstrated. As an application that can benefit from the model’s flexibility, an application example from the metrology field is discussed.

Journal

The International Journal of Advanced Manufacturing TechnologySpringer Journals

Published: May 28, 2018

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