Feature Extraction and Manufacturability Assessment of Sheet Metal Parts

Kumar, Shailendra; Singh, Rajender; Panghal, Deepak; Salunkhe, Sachin; Hussein, H.M.A.

doi:10.1007/978-981-10-2251-7_3

Cited by 4 publications

(1 citation statement)

References 27 publications

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“…In manufacturing, however, most of the recent work focuses on quantitative problems such as the prediction of springback mentioned above and cost estimation (Verlinden et al 2008). Existing work on classifying part geometries tends to be rule-based (Gupta and Gurumoorthy 2013;Kumar et al 2017) and hence tedious and sensitive to past errors. Additional examples of ML in sheet metal forming applications are detailed in books by Kumar Nee (2017) and Dixit and Dixit (2008).…”

Section: Introductionmentioning

confidence: 99%

Classification and selection of sheet forming processes with machine learning

Hamouche

Loukaides

2018

International Journal of Computer Integrated Manufacturing

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Sheet metal forming is a critical component of modern manufacturing. The procedure for selecting a suitable manufacturing process to achieve the final geometry of a metal part is unstructured and heavily reliant on human expertise. Similarly, classification and design of new metal forming processes has yet to be automated. In this study, a Machine Learning approach was used for the first time to identify the manufacturing process that formed a part solely from the final geometry. Several Neural Network configurations were tested with different geometry representation methods. The best performing classifier employed a deep Convolutional Neural Network and achieved an accuracy of 89%, namely when the geometry was given through a mapping of the Mean and Gaussian curvatures. The high accuracy rate establishes that automated methods can perform this step between design and manufacture, thus eliminating the need for human experts in matching each product to a suitable forming method.

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Section: Introductionmentioning

confidence: 99%