A Bayesian framework to estimate part quality and associated uncertainties in multistage manufacturing

Manufacturing is usually performed as a sequence of operations such as forming, machining, inspection, and assembly. A new challenge in manufacturing is to move towards Industry 4.0 (the fourth Industrial revolution) concerning the full integration of machines and production systems with machine learning methods to enable for intelligent multistage manufacturing. This paper discusses Multistage Manufacturing Processes (MMPs) and develops a probabilistic model based on Bayesian linear regression to estimate the results of final inspection associated with comparative coordinate measurement given in-process measured coordinates. The results of two case studies for flatness tolerance evaluation demonstrate the effectiveness of the probabilistic model which aims at being part of a larger metrology informatics system to be developed for predictive analytics and agent-based advanced control in multistage manufacturing. This solution relying on accurate models can minimise post-process inspection in mass production with independent measurements

Development of a new machine learning-based informatics system for product health monitoring

Manufacturing informatics aims to optimize productivity by extracting information from numerous data sources and making decisions based on that information about the process and the parts being produced. Manufacturing processes usually include a series of costly operations such as heat treatment, machining, and inspection to produce high-quality parts. However, performing costly operations when the product conformance to specifications cannot be achievable is not desirable. This paper develops a new machine learning-based informatics system capable of predicting the end product quality so that non-value-adding operations such as inspection can be minimized and the process can be stopped before completion when the part being manufactured fails to meet the design specifications

An Intelligent Metrology Informatics System based on Neural Networks for Multistage Manufacturing Processes

The ability to gather manufacturing data from various workstations has been explored for several decades and the advances in sensory and data acquisition techniques have led to the increasing availability of high-dimensional data. This paper presents an intelligent metrology informatics system to extract useful information from Multistage Manufacturing Process (MMP) data and predict part quality characteristics such as true position and circularity using neural networks. The input data include the tempering temperature, material conditions, force and vibration while the output data include comparative coordinate measurements. The effectiveness of the proposed method is demonstrated using experimental data from a MMP