Online prediction of automotive tempered glass quality using machine learning

Abstract

This study explores the application of machine learning algorithms for supporting complex product manufacturing quality through a focus on quality assurance and control. We aim to take advantage of ML technics to solve one of the complex manufacturing problems of the tempered glass manufacturing industry as a first attempt to automate product quality prediction and optimization in this industrial field as an alternative to destructive testing methodologies. The choice of this application field was motivated by the lack of a robust engineering technique to assess the production quality in real time; this arises the need of using advanced smart manufacturing solution as AI to save the extremely high cost of destructive tests. As methodology, this paper investigates the performance of machine learning techniques including Ridge Regression, Linear Regression, Light Gradient Boosting Machine, and Lasso Regression, for predicting the product thermal treatment quality within the selected type of industry. In the first part, we applied the selected machine learning models to a dataset collected manually and made up of the more relevant process parameters of the heating and the quenching process. Evaluating the results of the applied models, based on several performance indicators such as mean absolute error, mean squared error, and r-squared, declared that Ridge Regression was the most accurate model with a mean error of 14.33 which is significantly acceptable in a business point of view and not reachable by any human level experience of prediction. The second part consists of developing a digitalized device connected to the manufacturing process to provide predictions in real time. This device operates as an error-proofing system that sends a reverse signal to the machine in case the prediction shows a non-compliant quality of the current processed product. This study can be expanded to predict the optimal process parameters to use when the predicted values do not meet the desired quality and can advantageously replace the trial-and-error approach that is generally adopted for defining those parameters. The contribution of our work relies on the introduction of a clear methodology (from idea to industrialization) for the design and deployment of an industrial-grade predictive solution within a new field which is the glass transformation.