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

Author:

MURAT Naci1ORCID,KURNAZ Gülşah1ORCID

Affiliation:

1. ONDOKUZ MAYIS UNIVERSITY

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.

Publisher

Black Sea Journal of Engineering and Science

Subject

Pulmonary and Respiratory Medicine,Pediatrics, Perinatology, and Child Health

Reference19 articles.

1. Alpaydın E. 2010. Introduction to machine learning (Second edition). MIT Press, London, UK, pp: 537.

2. Bayır F. 2006. An application on artificial neural networks and predictive modeling. Master Thesis, Istanbul University Institute of Social Sciences, Department of Business Administration, İstanbul, Türkiye, pp: 122.

3. Beale MH, HaganMT, Demuth HB. 2010. Neural network toolbox 7 user’s guide. The MathWorks Inc., Natick, Massachusetts, US, pp: 424.

4. Burduk A. 2013. Artificial neural networks as tools for controlling production systems and ensuring their stability. 12th International Conference on Information Systems and Industrial Management (CISIM), Computer Information Systems and Industrial Management, 25-27 October, 2013, Krakow, Poland, pp: 487-498.

5. Chiang YM, Chang LC, Chang FJ. 2004. Comparison of static-feedforward and dynamic-feedback neural networks for rainfall–runoff modeling. J Hydrol, 209: 297-311.

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