PREDICTION OF HIGH CYCLE TIMES IN WHEEL RIM MOLDING WITH ARTIFICIAL NEURAL NETWORKS

Year 2022, DİJİTAL DÖNÜŞÜM VE VERİMLİLİK, 79 - 90, 12.01.2022

İnanç Kabasakal , Fatma Demircan Keskin

https://doi.org/10.51551/verimlilik.988472

Abstract

Purpose: Monitoring processes through real-time data collection is useful for businesses to understand their processes better, and deal with production problems. Predicting cycle-time allows identifying production delays, downtime, and productivity loss. Thereby, taking necessary actions is facilitated to eliminate detected losses and to prevent problems towards meeting customer due dates. This study proposes a two-stage approach to determine a cycle-time threshold and predict high cycle times by examining sample molding process data obtained from a wheel-rim manufacturer.

Methodology: Our study firstly determines thresholds for high cycle times with two alternate approaches. Subsequently, data were labeled regarding the cycle-time threshold. Alternate models based on Artificial Neural Networks (ANNs) were developed in R to predict high cycle times.

Findings: Our findings include a comparison of cycle-time threshold approaches through a distance-based metric. After labeling of high cycle times, our study presents the performance of alternate predictive models. The performance of models was compared in terms of accuracy, recall and precision.

Originality: Process mining in wheel rim molding has been found meager in prior research, despite the abundance of process mining applications and cycle-time prediction models. Another distinctive aspect of the study is cycle-time threshold determination with multiple methods to eliminate manual labeling of processes.

Keywords

Cycle Time Prediction, Process Mining, Machine Learning, Artificial Neural Networks

JANT DÖKÜMÜNDE YAPAY SİNİR AĞLARI İLE YÜKSEK ÇEVRİM SÜRELERİNİN TAHMİN EDİLMESİ

Abstract

Amaç: Gerçek zamanlı veri toplama yoluyla süreçlerin izlenmesi, işletmelerin üretim süreçlerini anlamalarında ve üretimdeki sorunlarla başa çıkabilmelerinde yarar sağlamaktadır. Çevrim süresinin tahmin edilmesi, üretim gecikmelerinin, duruşların ve verimlilik düşüşlerinin belirlenmesine olanak tanımaktadır. Bu sayede, tespit edilen kayıpların giderilmesi için gerekli adımların atılması ve müşteri teslim tarihlerinin karşılanmasında yaşanan problemlerin önüne geçilmesi kolaylaşmaktadır. Bu çalışmada, bir jant üreticisinden alınan numune kalıplama proses verileri incelenerek, çevrim süresi eşik değerleri belirleyen ve bu değere dayalı yüksek çevrim sürelerini tahmin eden iki aşamalı bir yaklaşım önerilmektedir.

Yöntem: Çalışmada öncelikle iki alternatif yaklaşımla çevrim süresi için eşik değer belirlenmektedir. Ardından, eşik değer uyarınca proses verileri etiketlenmektedir. Yüksek çevrim sürelerini tahmin etmek için R'da Yapay Sinir Ağları (YSA) uygulanarak alternatif sınıflandırma modelleri geliştirilmiştir.

Bulgular: Çalışmada uzaklık bazlı bir ölçüt aracılığıyla çevrim süresi eşiği belirleme yaklaşımları karşılaştırılmaktadır. Yüksek çevrim sürelerinin etiketlenmesini takiben alternatif tahminleme modellerinin performansları sunulmaktadır. Tahminleyici modellerin performansı doğruluk, duyarlılık ve kesinlik ölçütleri ile karşılaştırılmaktadır.

Özgünlük: Literatürde proses madenciliği uygulamaları ve çevrim süresi tahmin modelleri sıklıkla çalışılmış olmasına karşın, jant dökümünde proses madenciliği ile ilgili çalışmalara sık rastlanmamaktadır. Çalışmada bir diğer özgün yön ise, gecikmelerin manuel biçimde etiketlenmesi yerine, çevrim süresi için eşik değer belirleyen çoklu yaklaşım izlenmesidir.

Keywords

Çevrim Süresi Tahmini, Proses Madenciliği, Makine Öğrenmesi, Yapay Sinir Ağları

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