Session: 17-01-01: Research Posters

Paper Number: 150364

150364 - Data Driven Sustainability in Powder Bed Fusion With Big Data Analysis of Environmental Impacts Indicators 

The European Union's (EU) sustainable development initiatives underscore the urgent need for innovative research in sustainable manufacturing, particularly addressing lifecycle challenges with advanced technologies. Conventional manufacturing processes are often associated with significant environmental impacts, including material waste and greenhouse gas emissions. Additive manufacturing, especially Powder Bed Fusion (PBF), presents a promising alternative due to its potential to reduce material waste and improve energy efficiency.

However, there is a scarcity of literature that verifies the methodologies for sustainable manufacturing through reliable data analysis from a lifecycle assessment (LCA) perspective. This study originally implemented Application Programming Interface (API) strategies to extract data from Environmental Product Declarations (EPDs) and analyze the extensive data to validate the implementation of PBF as a more sustainable process compared to conventional manufacturing methods. The methodology employed programming with Python to extract LCA results data from over 14,000 EPDs sourced from more than 10 different platforms using APIs, which were then analyzed and visualized to identify the primary contributors to environmental impacts, such as Global Warming Potential (GWP). Specifically, the energy consumption of PBF for producing a metal bracket with topology design was assessed, focusing on validating PBF's sustainability advantages.

Results from this study indicated that over 14,000 EPDs were extracted, and after filtering based on criteria such as materials (metal), standards (ISO 14025), and validation periods (until 2025), the LCA results of 3,000 EPDs were analyzed and visualized. The data revealed that raw materials are the biggest contributors to environmental impacts, accounting for approximately 70%, followed by manufacturing (9%) and transport (7%) processes. Most of the available EPDs with LCA results are in the industry of building and construction, while metals take up less than a quarter of the accessible data. According to a related study by the same author, the PBF process has a raw material to final part waste ratio of 3.28, with a 55% reduction in weight and a 5% reduction in raw material per unit.

The findings highlighted the significant potential of applying additive manufacturing to metal manufacturing, as most metal parts with EPDs are currently produced using conventional methods. This underscores a vast opportunity for adopting AM technologies like PBF. Additionally, the study identified the need for a more concise and comprehensive LCA data management platform for AM. This study also provides data support and a theoretical foundation for future innovations in sustainable manufacturing. The weight reduction for each part also contributes to lower transport consumption, further decreasing GWP contributions. This research conclusively demonstrated that PBF is a more sustainable option than conventional manufacturing, with lower overall environmental impact and higher energy efficiency. Future studies should focus on the digitalization of LCA for PBF, leveraging more reliable and accurate data, and exploring how AI with Machine Learning can assist in achieving more sustainable design and manufacturing practices.

Presenting Author: Shunyang Ning Aalto University

Presenting Author Biography: Doctoral Researcher at Aalto University, working on sustainability in additive manufacturing with data driven methods. Slecialized in 3D printing, Powder Bed Fusion, sustainability, Lifecycle Assessment, Supply Chain

Authors:

Shunyang Ning Aalto University