Determination of Harness Production Time and Defective Product Formation Risk Factors with Artificial Neural Network

Year 2023, , 325 - 329, 15.10.2023

Naci Murat , Gülşah Kurnaz

https://doi.org/10.34248/bsengineering.1296187

Abstract

The aim of this research is to estimate the projected production times of the cable harnesses produced for the tender in a company operating in the aviation and defense industry in our country by artificial neural network. For this, artificial neural network model has been formed for the number of work order, the number of cable harness module, the number of cable harness pin, the number of cable harness label, the number of cable harness back shell, the number of cable harness heat shrink tube, and the number of cable harness terminal variables which may have an effect on the projected production times of cable harnesses for the tender. Multiple linear regression analysis method is used to compare the predictive power of this model and the most appropriate method for estimating the projected production time of cable harnesses for the tender is provided. The aim of the research is to determine the effect of cable harness connector type, cable harness label type and personnel competence level risk factors on the formation of faulty cable harnesses determined during the quality control and electrical testing steps in the production process using logistic regression analysis.

Keywords

Machine learning, Artificial neural networks, Logistic regression analysis, Determination of risk factors

Thanks

It was produced from the thesis titled “Determination of harness production time and defective product formation risk factors with machine learning algorithms” at Ondokuz Mayis University Thesis no: 571508. https://tez.yok.gov.tr/UlusalTezMerkezi/tezSorguSonucYeni.jsp

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