Multi-Sensor Data Fusion for Additive Manufacturing: An Overview

Although additive manufacturing (AM) technologies continue to improve, some primary challenges still hinder its broad adoption. The challenges include the following: parts qualification; quality assurance; process reliability needs for in-situ, real-time monitoring systems; and predictive modeling approaches that support fault detection, diagnosis, and prediction. In-situ AM monitoring systems can be designed and implemented using a wide range of sensors; each sensor can independently measure a specific parameter. The amount, type, and speed of the collected data from these sensors are unprecedented. It needs to be combined and processed into meaningful information for monitoring systems and closed-loop control processes. In such cases, all sensors' independent measurements are combined into a complete measurement value, and unique signal-processing algorithms are used to make a comprehensive overview of the measurement. This process is named "multi -sensor data fusion" (MSDF). This paper will summarize the MSDF architectures and algorithms; it also demonstrates the current applications of MSDF in AM technologies.

Keywords:

Additive Manufacturing , Multi-Sensor Data Fusion , In-situ Monitoring System