Session: 02-10-01: Sustainable Design

Paper Number: 145926

145926 - A Part Decomposition Framework for Improving Economic Productivity in Additive Manufacturing 

This paper presents an innovative approach to energy savings in additive manufacturing (AM). The cornerstone to our technique is an optimization-based framework for part decomposition. Additive manufacturing has become a basis of modern manufacturing that offers flexibility to produce complex parts. Despite the benefits, AM is characterized by high energy intensity that can overshadow the sustainable benefits in materials. The study addresses the critical issue as the proposed framework minimizes energy consumption during the production process. The framework employs a Genetic Algorithm (GA) to determine the optimal decomposition of a part into sub-parts and their corresponding orientation, aiming to achieve a significant reduction in energy consumption.

The methodology involves an optimization-based framework that integrates GA for the decomposition of parts into optimally oriented sub-parts. This approach seeks to reduce the energy required for both the build and assembly phases of production. By decomposing a part into sub-parts and optimizing their orientation, the framework targets a reduction in energy consumption by at least 10% compared to the original, undecomposed part. 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 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 results of applying this framework to four diverse test cases, ranging from simple geometric objects to more complex shapes, like the Stanford Bunny, validate the effectiveness of the proposed approach. Each example achieved a reduction in energy consumption exceeding the target of 10%, thereby underscoring the framework's potential to enhance the energy efficiency of AM processes. These findings not only contribute to the ongoing discourse on sustainable manufacturing practices but also provide a practical tool for designers and manufacturers aiming to optimize energy use in AM.

This research marks a significant step towards addressing the environmental impacts of additive manufacturing by focusing on energy consumption reduction through intelligent part design and orientation optimization. By filling a gap in existing literature regarding energy savings in the design phase of AM, this work lays the groundwork for further innovations in sustainable manufacturing. Future directions include exploring the framework's application to other AM processes, assessing its impact on other production properties, and quantifying energy savings from the assembly process for a more comprehensive understanding of its benefits.

Presenting Author: John Hall University of North Carolina at Charlotte

Presenting Author Biography: Dr. John Hall has held senior level positions in design, manufacturing, maintenance, and reliability engineering and is a licensed professional engineer. He has provided engineering consultation for GEC-Alstom Electric, TECO-Westinghouse, Lockheed-Martin, Advanced Micro Devices, Hewlett Packard, Accretech USA, and IBM. Following a career in industry, Dr. Hall, completed his graduate work at The University of Texas at Austin. He subsquently joined academics and his research concentrates on design methodologies and novel control techniques that promote sustainable systems. Key aspects of the work are system adaptability, maintenance, reliability, and lifecycle analysis. Dr. Hall is interested in the applied area of renewable energy systems. He has developed innovative design methods that promote the productivity and longevity of wind turbines. He has acquired a patent for his innovation, which has resulted in a startup company that is developing adaptive blade software and technology.

Authors:

Angshuman Deka University at Buffalo
John Hall University of North Carolina at Charlotte