Print Surface Thermal Modeling and Layer Time Control for Large-Scale Additive Manufacturing

Abstract

Large-scale additive manufacturing (LSAM) has a similar mechanism to the fused filament fabrication (FFF) and is capable of fabricating a part in large size. This capability provides LSAM with potentials in a variety of industries, including aerospace and automotive manufacturing. Product quality and production efficiency are two main concerns, as LSAM is implemented. It has been proven that print surface temperature is a major factor that impacts the quality of final products. Therefore, it needs to be controlled throughout the process. As an infrared camera is implemented, the real-time data of surface temperature of parts are available. A dynamic approach is studied in this article to perform real-time layer time control based on the real-time data from the infrared camera to improve both product quality and production efficiency. A regression model is formulated and proved to fit the cooling dynamics. To deal with the layerwise change of cooling dynamics, due to humidity and airflow, the Gaussian process is used to keep the regression model updated. The regression model together with the Gaussian process can predict the surface temperature of a part accurately, even in a dynamic environment. This method to predict surface temperature is then combined into an optimization model for real-time layer time control. Specifically, more than one position on the surface is monitored and considered in the optimization model, and the resulting layer time for each layer by solving the optimization model has the quality requirement satisfied and improves production efficiency. The improved system performance is presented in a case study. This article provides practitioners of LSAM with a useful tool to improve the process through manufacturing automation. Note to Practitioners-Carbon fiber reinforced thermoplastic material is used for large-scale additive manufacturing (LSAM) to fabricate parts in large size. This technology is new compared with other additive manufacturing technologies, and several key problems are to be addressed before it is widely applied in industry. One issue is product quality, which depends largely on print surface temperature. Quality problems caused by improper print surface temperature include cracking, warping, and deformation. Another problem is the operation inefficiency, which results in a high cost. Currently, it takes hours to print a single part. This article provides a framework to improve both quality and efficiency of LSAM by employing the real-time data captured from the infrared thermal camera. Specifically, a regression model is formulated to describe the surface temperature with high accuracy. Then, a layer time control method is proposed to schedule printing operations in real time to guarantee high printing efficiency and quality.