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

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

Application of Machine Learning Methods with Dimension Reduction Techniques for Fault Prediction in Molding Process

Significant advances in digital technology and advanced analytical tools have had a substantial impact on the production environment and laid the foundation for Industry 4.0 and intelligent production concepts. Predictive engineering is one of the key pillars of smart manufacturing that necessitates the collection and analysis of real-time data with an anticipatory point of view through advanced analytical techniques. In the literature, machine learning-based methods have received a great deal of attention to extract valuable information from process data for fault detection. In this study, fault prediction problem was addressed in a molding process that includes successive steps by applying machine learning methods with dimension reduction techniques. The techniques of Principal Component Analysis (PCA), and Isometric Feature Mapping (Isomap) were first utilized for dimension reduction. Then, the data was analyzed for fault prediction with several machine learning techniques, namely, Support Vector Machine (SVM), Neural Network (NN), and Logistic Regression (LR). The dataset for our analysis includes sensor data captured during the molding process of a wheel rim manufacturer. Several criteria, including accuracy, area under curve (AUC), Type I, and Type II error, were employed to assess the predictive performance of the methods applied, including and the model variants reinforced with PCA and Isomap. Our study demonstrates that all predictive model variants have performed with high accuracy, ranging between 92.16% (LR) and 98.04% (PCA-NN). PCA and Isomap improved the accuracy and Type-I error measures of all models; however, no such improvement was obtained on the Type-II error rates