This study introduce the application of machine learning algorithms for supporting the manufacturing quality control of a complex process as an alternative for the destructive testing methodologies. The choice of this application eld was motivated by the lack of a robust engineering technique to assess the production quality in real time, this arise the need of using advanced smart manufacturing solution as AI in order to save the extremely high cost of destructive tests. In concrete, this paper investigates the performance of machine learning techniques including Ridge regression, Linear Regression, Light Gradient Boosting Machine, Lasso Regression and more, for predicting the at glass tempering quality within the building glass industry. In the rst part, we applied the selected machine learning models to a dataset collected manually and made up by 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, R Squared, declared that Ridge Regression was the most accurate model. The second part consist of developing a digitalized device connected with the manufacturing process in order to provide predictions in real time. This device operates as an errorproo ng system that send 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 does not meet the desired quality, and can advantageously replace the trial and error approach that is generally adopted for de ning 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-grad predictive solution within a new eld which is the glass manufacturing.
This study introduce the application of machine learning algorithms for supporting the manufacturing quality control of a complex process as an alternative for the 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 arise the need of using advanced smart manufacturing solution as AI in order to save the extremely high cost of destructive tests. In concrete, this paper investigates the performance of machine learning techniques including Ridge regression, Linear Regression, Light Gradient Boosting Machine, Lasso Regression and more, for predicting the flat glass tempering quality within the building glass industry. In the first part, we applied the selected machine learning models to a dataset collected manually and made up by 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, R Squared, declared that Ridge Regression was the most accurate model. The second part consist of developing a digitalized device connected with the manufacturing process in order to provide predictions in real time. This device operates as an error-proofing system that send 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 does 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-grad predictive solution within a new field which is the glass manufacturing.
The detection and localization of small and tiny defects on high-resolution images is considered one of the main challenges in the field of computer vision. In the manufacturing industry, the production speed and cycle time are considered the major target of a production process. For such reason, automated quality detection is getting even more complexified by the need of performing defect detection on moving products. In this work, we investigate the performance of a small defect detection process on high-scale images by utilizing state-of-the-art object detection architectures and a set of the hardware setup. Taking as a target the detection of defects on moving products, and using a small training set and a procedure of data augmentation, we demonstrated that such a challenge can be solved using machine learning and artificial intelligence coupled with domain knowledge in machine vision hardware selection and design. The sections of this paper are organized into two parts, the first part describes the problem, the existing and related works, and a summary of the existing methodologies. The second part of the paper is centered on our case study for which we started by describing the adopted methodology, the vision system design, the data acquisition and processing, the model training, and the obtained results, then it is concluded with a discussion of the model outputs and the listing of challenges that still to be studied in future works.
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