There is growing interest in adopting metal additive manufacturing (AM) as a processing technology across sectors, including the fabrication of aerospace and medical device components. While a number of studies have assessed the environmental impacts of metal AM, most provide only isolated data points with no consideration of environmental optimisation. The objective of this review is to present a systematic critical review of quantitative environmental optimisation studies on metal AM. In this context, a multi-objective design space is necessary, as the optimisation process involves trade-offs among performance, cost, and sustainability. Recent advances in intelligent metal AM offer promising tools for optimisation and autonomous control. However, studies that directly target environmental performance optimisation in this context remain limited. As a result, this paper presents a systematic literature review that addresses the gap by investigating how current quantitative environmental assessment methods can support optimisation. After screening, removing duplicates, refining, and filtering relevant works, 62 studies were included in the final review, published until the end of 2025. Studies had to focus on metal AM, while works on post-processing or comparisons between AM and conventional manufacturing (CM) were excluded. To this end, five research questions are posed to guide the review, which explore the environmental objectives considered, the types of input data used, the application of computational intelligence techniques, relevant engineering use cases, and key gaps for future research. Overall, the paper aims to connect environmental assessment with optimisation to support sustainable and intelligent metal AM processes.
Ghadimi et al. (Wed,) studied this question.