Production efficiency and product quality need to be addressed simultaneously to ensure the reliability of large scale additive manufacturing. Specifically, print surface temperature plays a critical role in determining the quality characteristics of the product. Moreover, heat transfer via conduction as a result of spatial correlation between locations on the surface of large and complex geometries necessitates the employment of more robust methodologies to extract and monitor the data. In this paper, we propose a framework for real-time data extraction from thermal images as well as a novel method for controlling layer time during the printing process. A FLIR™ thermal camera captures and stores the stream of images from the print surface temperature while the Thermwood Large Scale Additive Manufacturing (LSAM™) machine is printing components. A set of digital image processing tasks were performed to extract the thermal data. Separate regression models based on real-time thermal imaging data are built on each location on the surface to predict the associated temperatures. Subsequently, a control method is proposed to find the best time for printing the next layer given the predictions. Finally, several scenarios based on the cooling dynamics of surface structure were defined and analyzed, and the results were compared to the current fixed layer time policy. It was concluded that the proposed method can significantly increase the efficiency by reducing the overall printing time while preserving the quality.

Due to rapid cyclic heating and cooling in metal additive manufacturing processes, such as selective laser melting (SLM) and direct metal deposition (DMD), large thermal stresses will form and this may lead to the loss of dimensional accuracy or even cracks. The integration of numerical analysis and experimental validation provides a powerful tool that allows the prediction of defects, and optimization of the component design and the additive manufacturing process parameters. In this work, a numerical simulation on the thermal process of DMD of 0Cr18Ni9 stainless steel is conducted. The simulation is based on the finite volume method (FVM). An in-house code is developed, and it is able to calculate the temperature distribution dynamically. The model size is 30mm × 30mm × 10.5mm, containing 432,000 cells. A DMD experiment on the material with the same configuration and process parameters is also carried out, during which an infrared camera is adopted to obtain the surface temperature distribution continuously, and thermocouples are embedded in the baseplate to record the temperature histories. It is found that the numerical results agree with the experimental results well.

As an emerging and effective nano-manufacturing technology, the directional freezing based 3D printing can form 3-Dimensional (3D) nano-structures with complex shapes and superior functionalities, and thus has received ever increasing publicity in the past years. One of the key challenges in this process is the proper heat management, since the heat induced melting and solidification process significantly affects the functional integrity and structural integrity of the 3D printed nano-structures. To address this challenge, this paper proposes a novel path planning modeling and optimization framework to intelligently control the internal and external heat transfer process and ultimately optimize both the macro- and micro-structure of the printed part. Specifically, a heuristic tool path planning model was formulated and optimized based on thermal analysis process. The simulation results demonstrate that the tool path planning highly affects the spatial and temporal temperature distribution of the being printed part and the optimized tool path planning can effectively improve the uniformity of the temperature distribution which will consequently enhance the performance of the fabricated nano-structures.

This paper presented a novel 3D printing technique to fabricate graphene aerogel based on directional freezing. Thermal property of the graphene ink is one of key factors in this process which affects the material integrity and morphology as well as process efficiency and reliability. The major objective of this paper is to develop a heat transfer model to efficiently and reliably predict the temperature evolution of the printed materials and the waiting time between the layers for any input geometry. The simulation results show that the input geometry significantly affects the temperature evolution and waiting time. The proposed technique can not only improve the process efficiency and reliability, it can also serve as a flexible tool to predict and control the microstructure of the printed graphene aerogels.

Grinding is a special machining process with large number of parameters influencing each other. Any grinding process involves six basic microscopic wheel-workpiece interaction modes in terms of grain cutting, plowing, and sliding, as well as bond-workpiece friction, chip-workpiece friction, and chipbond friction. And quantification of all the 6 modes immensely enhances understanding and managing of the grinding processes. In this paper, the kinematics simulation is presented to imitate the grinding wheel surface moving against the workpiece under specified grinding conditions. The grinding wheel surface is imported from the fabrication analysis based grinding wheel model of previous work. During each simulation iteration step, it provides the number of contacting grains, contact cross-section area for each grain, and resultant workpiece surface condition. Through retrieving the specific force value from the single grain cutting simulation, the cutting force and plowing force can be calculated. This model can also be potentially used in the time dependent behavior and thermal analysis of grinding processes.