Toward automated life cycle assessment for additive manufacturing: A systematic review of influential parameters and framework design

Additive manufacturing (AM), a disruptive technology of building parts layer-by-layer directly from 3D models, has been considered a tempting cleaner production process compared to other conventional production routes. This is because AM has demonstrated impressive green characteristics which contributed to significantly reduced material and energy consumptions, a shorter supply chain, and diversion of the waste stream by reparation, etc. The majority of the current quantitative studies on the environmental assessment of AM are based on utilizing the knowledge-intensive Life Cycle Assessment (LCA) methodology. Further, current studies on assessing the environmental performance of AM are based on a limited selection of design- or process-related parameters. These knowledge barriers may cause delays and challenges in the selection of the optimal design and process parameters for additively manufactured parts. Such challenges are particularly prevalent during the product design and planning stages due to the iterative design-evaluation process. Therefore, there is a need for an automated LCA tool to support AM toward elevated sustainability. As such, this paper provides three main contributions to the research community. Firstly, this is the first study to identify a comprehensive set of influential AM design and process parameters that pose an impact on the environmental performance of AM. Secondly, this review also summarizes the impacts of each of these parameters on the environmental sustainability of AM. Lastly, to the best of the authors' knowledge, no work in the literature has been reported on automating LCA for AM. Thus, this paper promotes research toward a more environmentally benign and innovative AM technology by proposing a new framework to automate the environmental assessment of the process. The proposed framework is anticipated to take advantage of the fruitful integration between Machine Learning (ML) and the product process co-design concept to mitigate the challenges and limitations associated with the current LCA-based assessment tools.Natural Science and Engineering Research Council of Canada (NSERC) Discovery Grant (RGPIN-2022-03448)

A mathematical model for the design of distributed layout by considering production planning and system reconfiguration over multiple time periods

In this paper, we develop a new mathematical model that integrates layout configuration and production planning in the design of dynamic distributed layouts. The model incorporates a number of important manufacturing attributes such as demand fluctuation, system reconfiguration, lot splitting, work load balancing, alternative routings, machine capability and tooling requirements. In addition, the model allows several cost elements to be optimized in an integrated manner. These costs are associated with material handling, machine relocation, setup, inventory carrying, in-house production and subcontracting needs. Numerical examples of different sizes are presented to illustrate the nature of the developed model and shed light on several managerial insights