PARÇA ISKARTALARININ MAKİNE ÖĞRENMESİ KULLANILARAK AZALTILMASI: OTOMOTİV SEKTÖRÜNDE BİR UYGULAMA

Abstract

Bu çalışmada, enjektör imal eden bir firmanın taşlama makinesindeki insan faktörüne bağlı ıskartaların azaltılması amaçlanmıştır. İnsan faktörüne bağlı ıskartalar, makinenin taş değişimi, arıza gibi çeşitli nedenlerle durmasından sonra makine operatörünün, makine ve ürün parametrelerinde yaptığı ayarlamalardan kaynaklanmaktadır. Iskartaların azaltılması amacıyla iki aşamalı bir çözüm yaklaşımı önerilmiştir. İlk aşamada, makine öğrenmesi kullanılarak kalite tahminlenmiştir. Kalite tahminleme, bir sınıflandırma problemi olarak ele alınmıştır. Farklı sınıflandırma algoritmaları modellenerek en yüksek performansa sahip Destek Vektör Makineleri (DVM) algoritması seçilmiştir. İkinci aşamada ise, DVM kullanılarak kaliteli ürün ile sonuçlanması beklenen uygun parametre değerleri belirlenmiştir. Güncel veri dikkate alınarak parametre değerlerinin otomatik olarak revize edilmesi ve operatöre sunulması amacıyla bir öneri sistemi geliştirilmiştir. Bu öneri sistemi ile, taşlama işleminin insan etmenine olan bağlılığının ortadan kaldırılarak dijitalleşmesi amaçlanmıştır.

Keywords

• Iskarta azaltma
• Kalite sınıflandırma
• Makine öğrenmesi
• Parametre ayarı
• Dijitalleşme

Reducing Part Rejects Using Machine Learning: A Case Study In Automotive Industry

Abstract

This study aims to reduce the human-based rejects in the grinding machine of a company that manufactures injectors. Human-based rejects are caused by the operator's adjustments to the machine and product parameters after the engine stops due to some reasons such as stone change or breakdown. A two-stage solution approach is proposed to reduce rejects. In the first stage, quality is predicted using machine learning. Quality prediction is addressed as a classification problem. Various classification algorithms are modeled, and the outperforming Support Vector Machine (SVM) is selected. In the second stage, the proper parameter values expected to result in a quality product are determined using SVM. A system is developed to automatically revise the parameter values by considering the updated data and recommend them to the operator. This system aims to digitalize the grinding process by removing its dependence on the human factor.

Keywords

• Reject reduction
• Quality classification
• Machine learning
• Parameter adjustment
• Digitalization

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