A PART DECOMPOSITION FRAMEWORK FOR IMPROVING ECONOMIC PRODUCTIVITY IN ADDITIVE MANUFACTURING

Additive manufacturing (AM) provides the ability to produce complex, fully functional, multi-material parts directly from its three-dimensional models, resulting in reduction of prototyping lead time and costs. In the literature it can be seen that various techniques have been developed to improve productivity of AM processes by focusing on reducing time and cost, and improving quality during part production. Despite these advantages of AM, it is characterized by high energy consumption that can decrease its sustainable benefits. Moreover, there is limited work in the literature that addresses productivity improvement in terms of energy consumption. This study addresses this shortcoming by introducing a framework for minimizing energy consumption during the production process. A novel optimization-based framework for part decomposition is presented that results in energy savings for AM processes thereby addressing an important research gap for decreasing energy consumption in manufacturing processes. The framework integrates a Genetic Algorithm (GA) based optimization approach to determine the optimal decomposition of a part into sub-parts and their corresponding orientation. This approach thus aims to achieve a reduction in energy consumption by reducing the energy required for both the build and assembly phases of production. In this study, the framework targets a reduction in energy consumption by at least 10% compared to the original, undecomposed part by decomposing a part into sub-parts and optimizing their orientation. The study thoroughly discusses the application of this framework to the Selective Laser Sintering (SLS) process, detailing the procedure from the initial calculation of energy consumption for a given part orientation to the iterative process of generating and evaluating decomposed parts. This decomposition process continues until the framework achieves the target energy consumption reduction, demonstrating the framework's adaptability to different AM processes and energy savings goals. The effectiveness of the framework is validated by applying it to four diverse test cases, including both simple geometric objects and complex shapes like the Stanford Bunny. For each test case, the target of 10% reduction in energy consumption due to decomposition is exceeded which demonstrates the potential of this framework to improve the energy efficiency of AM processes. These results demonstrate the utility of this framework as a practical tool for designers and manufacturers aiming to optimize energy use in AM thereby supporting the ongoing efforts on sustainable manufacturing practices. This research thus focuses on energy consumption reduction through intelligent part design and orientation thereby mitigating the environmental impacts of AM. Additionally, by filling the gap in existing literature regarding energy savings in the design phase of an AM process, this work lays the foundation for further innovations in sustainable manufacturing. In future the framework's application to other AM processes will be assessed, its impact on other production properties will be investigated, and energy savings from the assembly process will be quantified for a more comprehensive understanding of its utility.