Virtual metrology as an approach for product quality estimation in Industry 4.0: a systematic review and integrative conceptual framework

Abstract

Virtual metrology (VM) involves estimating a product's quality directly from production process data without physically measuring it. This enables the product quality of each unit of production to be monitored in real time, while preserving the process efficiency. Initially developed for the semiconductor industry, VM has recently been examined for use in other industrial fields. VM is enabled by components such as quality estimators and drift detectors. It enhances various industrial applications such as machine control and sampling decision systems. The literature lacks a comprehensive summary and a systematic review of the state of the art in VM. To fill this gap, this study followed a systematic methodology to conduct a complete and structured literature review of VM. This paper presents a detailed analysis of the 199 papers that we identified based on our search and selection criteria. Following our methodological framework, we assigned these papers to categories and highlighted shortcomings. Finally, we discussed VM's use in various industrial fields, underlining its potential for every manufacturing industry. This study and the proposed framework are useful for both practitioners and academicians since they raise crucial theoretical and managerial questions as well as provide suggestions for further research on this critical topic.

End-to-end deep learning framework for printed circuit board manufacturing defect classification

“…Virtual Metrology exploits available information from sensors or visual inputs to assess parameters which are difficult or expensive to measure 8 , 9 . Based on the same paradigm, Autoencoders were trained on defect-free semiconductor chips and, then used for anomaly detection 22 .…”

“…With the growing importance of product customization, there is an increase in defect rates due to smaller production batch sizes 7 . In Virtual Metrology(VM), a sub-field of ZDM, data-driven methods help estimate and predict the quality of a product 8 . These methods leverage low-cost quality metrics to derive more complex metrics to achieve a significant improvement in cost efficiency 8 .…”

“…In Virtual Metrology(VM), a sub-field of ZDM, data-driven methods help estimate and predict the quality of a product 8 . These methods leverage low-cost quality metrics to derive more complex metrics to achieve a significant improvement in cost efficiency 8 . Emerging machine learning-based computer vision techniques have helped researchers apply Virtual Metrology to quality inspection 9 …”

Cost-Based Decision Support System: A Dynamic Cost Estimation of Key Performance Indicators in Manufacturing

“…The current methodology aims to bridge that conflict and provide a cost model tool that will allow manufacturers to properly select manufacturing parameters in order to maintain productivity at acceptable levels and, at the same time, be able to implement ZDM [47]. In contemporary manufacturing environments, product quality is a crucial aspect of the sustainability of the company [48]. The outcome of the proposed tool will be a set of manufacturing parameters that will allow good performance KPIs and implementation of ZDM.…”

Digital Innovation Enabled Nanomaterial Manufacturing; Machine Learning Strategies and Green Perspectives

“…Ontology can be the engine to organize and connect the process and materials metadata, which is a key enabler for AI to realize its prospect. Currently, MASON ontology has been used for establishing semantics, towards zero-defect manufacturing [ 13 , 14 ]. The use of ontology-assisted co-simulation and cognitive digital twin is a main driver for the future of manufacturing utilizing real-time data for status monitoring, fault diagnosis, and performance prediction [ 15 ].…”