With the advent of Industry 4.0, Artificial Intelligence (AI) has created a favorable environment for the digitalization of manufacturing and processing, helping industries to automate and optimize operations. In this work, we focus on a practical case study of a brake caliper quality control operation, which is usually accomplished by human inspection and requires a dedicated handling system, with a slow production rate and thus inefficient energy usage. We report on a developed Machine Learning (ML) methodology, based on Deep Convolutional Neural Networks (D-CNNs), to automatically extract information from images, to automate the process. A complete workflow has been developed on the target industrial test case. In order to find the best compromise between accuracy and computational demand of the model, several D-CNNs architectures have been tested. The results show that, a judicious choice of the ML model with a proper training, allows a fast and accurate quality control; thus, the proposed workflow could be implemented for an ML-powered version of the considered problem. This would eventually enable a better management of the available resources, in terms of time consumption and energy usage.

Machine Learning and image analysis towards improved energy management in Industry 4.0: a practical case study on quality control / Casini, Mattia; De Angelis, Paolo; Porrati, Marco; Vigo, Paolo; Fasano, Matteo; Chiavazzo, Eliodoro; Bergamasco, Luca. - In: ENERGY EFFICIENCY. - ISSN 1570-646X. - ELETTRONICO. - 17:5(2024), pp. 1-16. [10.1007/s12053-024-10228-7]

Machine Learning and image analysis towards improved energy management in Industry 4.0: a practical case study on quality control

Casini, Mattia;De Angelis, Paolo;Vigo, Paolo;Fasano, Matteo;Chiavazzo, Eliodoro;Bergamasco, Luca
2024

Abstract

With the advent of Industry 4.0, Artificial Intelligence (AI) has created a favorable environment for the digitalization of manufacturing and processing, helping industries to automate and optimize operations. In this work, we focus on a practical case study of a brake caliper quality control operation, which is usually accomplished by human inspection and requires a dedicated handling system, with a slow production rate and thus inefficient energy usage. We report on a developed Machine Learning (ML) methodology, based on Deep Convolutional Neural Networks (D-CNNs), to automatically extract information from images, to automate the process. A complete workflow has been developed on the target industrial test case. In order to find the best compromise between accuracy and computational demand of the model, several D-CNNs architectures have been tested. The results show that, a judicious choice of the ML model with a proper training, allows a fast and accurate quality control; thus, the proposed workflow could be implemented for an ML-powered version of the considered problem. This would eventually enable a better management of the available resources, in terms of time consumption and energy usage.
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11583/2988868