A New Architectural Approach to Monitoring and Controlling AM Processes

Abstract

The abilities to both monitor and control additive manufacturing (AM) processes in real-time are necessary before the routine production of quality AM parts will be possible. Currently, neither ability exist! The major reason is that AM processes are different from traditional manufacturing processes in many ways and so are the sensors and the monitoring data collected from them. In traditional manufacturing, that data is mostly numeric in nature. To that numeric data, AM monitoring data add large volumes of a variety of in situ, high-speed, image data. Collecting, fusing, and analyzing all that AM data and making the necessary control decisions is not possible using traditional, rigid, hierarchical-control architectures. Therefore, researchers are proposing to use real-time, machine-learning algorithms to analyze the data and to execute the other control functions. This paper identifies those control functions and proposes a new architecture to integrate them. This paper also shows an example of using that architecture to analyze the melt-pool, shape analysis using a clustering method.

Research and Application of Information Model of a Lithium Ion Battery Intelligent Manufacturing Workshop Based on OPC UA

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“…To realize the closed-loop systems, diverse kinds of control strategies such as adaptive control, fuzzy control, neural control, predictive control, and AI control algorithm can be utilized [14]. And, a time-based multi-loop architecture was proposed to realize the AM process with internal real-time and near real-time control loops [15].…”

Emerging Paradigms and Architectures for Industry 4.0 Applications

“…Other relevant Industry 4.0 technologies were studied in [8][9][10]. In [8], the authors analyze the problem of how to monitor and control additive manufacturing processes in real-time.…”

“…Other relevant Industry 4.0 technologies were studied in [8][9][10]. In [8], the authors analyze the problem of how to monitor and control additive manufacturing processes in real-time. With such a purpose, the paper identifies real-time machine learning algorithms to analyze the received data and then execute control functions, and then proposes a new architecture, which is illustrated through a practical industrial example.…”