Improving production process performance thanks to neuronal analysis

TL;DR

This paper presents a neural network-based approach for optimizing production machine settings to improve product quality in real-time, demonstrated through a case study in a high-quality lacquerer company.

Contribution

It introduces an online quality approach using neural networks to determine optimal machine settings, enhancing production performance and quality control.

Findings

Neural network model effectively predicts optimal machine settings.

Implementation improves product quality and process efficiency.

Case study demonstrates practical applicability in industry.

Abstract

Product quality level is become a key factor for companies' competitiveness. A lot of time and money are required to ensure and guaranty it. Besides, motivated by the need of traceability, collecting production data is now commonplace in most companies. Our paper aims to show that we can ensure the required quality thanks to an "on-line quality approch" and proposes a neural network based process to determine the optimal setting for production machines. We will illustrate this with the Acta-Mobilier case, which is a high quality lacquerer company.