Bridging the gap between Industry 4.0 and manufacturing SMEs: A framework for an end-to-end Total Manufacturing Quality 4.0’s implementation and adoption

Abstract

Manufacturing is one of the industrial sectors taking benefit from the 4th industrial revolution and bringing existing production capacities closer to the ”factory of the future”. Quality, as a main concern in manufacturing, is also to benefit from this change of paradigm by introducing new key enabling technologies such as Internet of Things (IoT) and Artificial Intelligence (AI) into quality management, earning it the label of Quality 4.0 (Q4.0). The implementation of these paradigms is still gathering research efforts as it is arduous to design and realize effective end-to-end Decision Support Systems (DSSs) for Q4.0, with several dimensions to consider when integrating digitalization with quality. This is an even more challenging task when it comes to SMEs’ efforts to implement these concepts, given the particularities of these entities. This paper presents an approach to design a Total Manufacturing Quality 4.0 (TMQ 4.0) DSS by combining Sensor Network (SN) data and historical data in an end-to-end framework. Furthermore, the paper presents the validation of the approach through a case study application in a metal-cutting high-precision manufacturing SME. It shows promising Q4.0 estimations with regular Machine Learning (ML) algorithms (kNN, Random Forest, Logistic Regression, XGboost, feed-forward Deep Neural Network) when the steps of tending to data quality, data augmentation, and end-to-end design and implementation are applied. By providing building blocks for an end-to-end Q4.0 DSS design and implementation in an integrated quality control application, this approach aims at supporting end-users in the in-process quality control of their manufacturing operations.