MOSS-Multi-Modal Best Subset Modeling in Smart Manufacturing.

Lening Wang,Pang Du,Ran Jin

doi:10.3390/s21010243

Abstract

Smart manufacturing, which integrates a multi-sensing system with physical manufacturing processes, has been widely adopted in the industry to support online and real-time decision making to improve manufacturing quality. A multi-sensing system for each specific manufacturing process can efficiently collect the in situ process variables from different sensor modalities to reflect the process variations in real-time. However, in practice, we usually do not have enough budget to equip too many sensors in each manufacturing process due to the cost consideration. Moreover, it is also important to better interpret the relationship between the sensing modalities and the quality variables based on the model. Therefore, it is necessary to model the quality-process relationship by selecting the most relevant sensor modalities with the specific quality measurement from the multi-modal sensing system in smart manufacturing. In this research, we adopted the concept of best subset variable selection and proposed a new model called Multi-mOdal beSt Subset modeling (MOSS). The proposed MOSS can effectively select the important sensor modalities and improve the modeling accuracy in quality-process modeling via functional norms that characterize the overall effects of individual modalities. The significance of sensor modalities can be used to determine the sensor placement strategy in smart manufacturing. Moreover, the selected modalities can better interpret the quality-process model by identifying the most correlated root cause of quality variations. The merits of the proposed model are illustrated by both simulations and a real case study in an additive manufacturing (i.e., fused deposition modeling) process.

Highlights

Published: 1 January 2021Smart manufacturing integrates multi-modal sensing systems and computing resources (e.g., Fog computing and Cloud computing) to support efficient real-time quality modeling, monitoring, diagnosis, and control in manufacturing [1,2,3,4]
One modality in this paper is defined as a group of features extracted from the sensing signal that measures the same kind of physical quantity from the same place in the manufacturing process [5]
Yields the best prediction accuracy in most of the cases with both linear and nonlinear model structures. It is because the proposed MOSS can deal with the nonlinear model components, and can effectively implement the variable selection for both among the modalities and within each modality compared with the benchmarks via the function norm and l-1 norm simultaneously

Summary

Introduction

Smart manufacturing integrates multi-modal sensing systems and computing resources (e.g., Fog computing and Cloud computing) to support efficient real-time quality modeling, monitoring, diagnosis, and control in manufacturing [1,2,3,4]. Based on the multi-modal sensing systems, variables that can reflect the status of manufacturing processes are collected from different modalities to effectively model the quality-process relationship in smart manufacturing [6,7]. One can equip sensors and collect the corresponding process variables as many as possible to accurately model the quality-process relationship in the manufacturing process. This approach is not cost-effective, because some modalities might be redundant or comparable with each other. Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations

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