Process Optimization and Product Design for Metal Additive Manufacturing via Knowledge-Assisted Machine Learning

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Additive Manufacturing (AM) is a disruptive technology that can fabricate complex shapes, customized parts, and directly make objects without expensive tooling. Among various AM technologies, metal AM has attracted the most attention due to the potentially wide application in industry. Key technical challenges to metal AM include process modeling, process parameter optimization, and product design for AM.  Process parameters (e.g., laser power, laser velocity, and layer thickness) influence the final mechanical properties such as porosity, tensile strength, and hardness significantly. To understand the relationship between the process parameters and the product mechanical properties, one builds physics-based models or data-driven models. Physics-based models use differential equations that govern the underlying thermomechanical process. Due to high process complexity, there are still lack of credible and reliable simulation tools for metal AM technologies. Data-driven models are derived from experiments on different process parameter settings. This approach, however, needs an exponentially growing number of experiments as the number of parameters becomes relatively large, which is the case for metal AM. The experiment costs soon become too high. Moreover, this approach needs to be applied for every different technology and even every machine, which prevents its practice use. Another challenge in design for AM is that the topology optimization results are not parametric and need tedious manual processing before AM. This proposal aims to research and develop methods for process parameter optimization and product design for metal AM to address the above challenges. I propose to use knowledge-assisted artificial neural networks to build a data-driven model with fewer experiments, which in return help to fine-tune a physics-based model and define key parameters and their effective value ranges. An integrated and simplified hybrid model will be defined that is easily transferable between different machines and technologies. Dedicated optimization algorithms are to be developed to optimize process parameters for both Laser Beam Melting and Laser Beam Deposition metal AM machines. I also propose to use primitive geometries such as holes, cylinders and triangles to be the basic elements for topology optimization so that the output of topology optimization is parametric geometry that can be directly used for optimization and AM. The outcome of the proposal will be methods and a software prototype that can generate parametric topology and geometry, model metal AM processes, and optimize process parameters for maximum efficiency, lowest cost, or highest product quality from metal AM. This research will fill a gap in the literature and meet the needs of an emerging US$6 billion industry. In total, 4 PhD, 2 MSc and 5 undergraduate students will be directly trained via this research program.