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JPO acknowledges funding by national funds from FCT - Fundação para a Ciência e a Tecnologia, I.P., in the scope of the projects LA/P/0037/2020. This activity has received funding from the European Institute of Innovation and Technology (EIT) – Project Smart WAAM: Microstructural Engineering and Integrated Non-Destructive Testing . This body of the European Union receives support from the European Union's Horizon 2020 research and innovation programme. Publisher Copyright: © 2023 The Authors Growing consciousness regarding the environmental impacts of additive manufacturing (AM) processes has led to research focusing on quantifying their environmental impacts using Life Cycle Assessment (LCA) methodology. The main objective of this paper is to review the state of the art of the existing LCA studies of AM processes. In this paper, a systematic literature review is carried out where a total of 77 papers focusing on LCA, including social-Life Cycle Assessment (S-LCA), are analyzed. Accordingly, the application of LCA methodology to different AM technologies was studied and different research themes such as the goal and scope of LCA studies, life cycle inventory data for different AM technologies, AM part quality and mechanical properties, the environmental, economic, and social performances of various AM technologies, and factors affecting AM´s sustainability potential were analyzed. Based on the critical analysis of the existing research, five major shortcomings of the existing research are realized: (i) some AM technologies are under studied; (ii) more focus only on the environmental sustainability dimension of AM, neglecting its economic and social dimensions; (iii) exclusion of AM pat quality and its mechanical performance from the sustainability assessment; (iv) not enough focus on the life cycle stages after product manufacture by AM; (v) effect of different product variables on AM´s sustainability not studied extensively. Lastly, based on these shortcomings realized, the following research directions for future works are suggested: (i) inclusion of new AM materials and technologies; (ii) transition to a triple-bottom-line sustainability assessment considering environmental, economic, and social dimensions of AM; (iii) extending the scope of LCA studies to post-manufacture stages of AM products; (iv) development of predictive environmental impact and cost models; (v) integration of quality and mechanical characterization with sustainability assessment of AM technologies. publishersversion published

Funding Information: João Pedro Oliveira acknowledges funding by national funds from FCT - Fundação para a Ciência e a Tecnologia , I.P., in the scope of the projects LA/P/0037/2020 of the Associate Laboratory Institute of Nanostructures, Nanomodelling and Nanofabrication – i3N. This activity has received funding from the European Institute of Innovation and Technology (EIT) – Project Smart WAAM: Microstructural Engineering and Integrated Non-Destructive Testing. This body of the European Union receives support from the European Union’s Horizon 2020 research and innovation programme . Publisher Copyright: © 2023 The Authors The industrial progress made throughout these years has led to great results in terms of producing fast and with good quality. However, the impacts related to that production, whether these are environmental, economic, or social have been, at times, neglected. The manufacturing sector, as one of the most polluting sector, felt the urge to adapt to this industrial progress and find ways to produce with improved sustainability goals without compromising the quality of the final product and the production time. Industry easily understood the benefits of this greener approach, and, with this, new sustainable technologies started to emerge. Additive Manufacturing (AM) is one of those technologies that provide alternative sustainable paths to traditional manufacturing. In order to generalize the benefits of AM production in terms of sustainability, when compared to traditional processes, further investigations must be conducted. In this sense, the proposed work has the intention of finding the environmental impacts associated with a particular AM technique for the fabrication of metal parts, Wire Arc Additive Manufacturing (WAAM). A practical work based on the production of three different complexity metal parts considering an additive (WAAM) and a subtractive (Computer Numerical Control (CNC) Milling) manufacturing process is developed. To quantify the environmental impacts of both processes, the author resorts to the Life Cycle Assessment (LCA) methodology. The assessment is conducted in the SimaPro 9.2 software, accordingly to ISO 14044:2006 standard. The results allow a comparison between both types of manufacturing and enable the suggestion of measures to decrease the environmental footprint of WAAM. It was found that WAAM approach leads to a material saving ranging between 40% and 70% and an environmental impact reduction in the range of 12%–47%, compared to the subtractive approach for fabricating the 3 geometries considered in this study. The conclusions obtained are specific to this particular application and, once more, it is acknowledged that in order to reach a global understanding relative to this technology's environmental implications, extra research still needs to be made. publishersversion published

Funding Information: Inês Ferreira acknowledges financial support from Fundação para a Ciência e Tecnologia for funding PhD Grant - REF: SFRH/BD/145448/2019. Helena Carvalho and Radu Godina acknowledge Fundação para a Ciência e a Tecnologia (FCT - MCTES) for its financial support via the project UIDB/00667/2020 (UNIDEMI). Publisher Copyright: © 2022 The Authors The role of new technologies such as additive manufacturing and blockchain technology in designing and implementing circular economy ecosystems is not a trivial issue. This study aimed to understand if blockchain technology can be an enabler tool for developing additive symbiotic networks. A real case study was developed regarding a circular economy ecosystem in which a fused granular fabrication 3D printer is used to valorize polycarbonate waste. The industrial symbiosis network comprised four stakeholders: a manufacturing company that produces polycarbonate waste, a municipality service responsible for the city waste management, a start-up holding the 3D printer, and a non-profit store. It was identified a set of six requirements to adopt the blockchain technology in an additive symbiotic network, bearing in mind the need to have a database to keep track of the properties of the input material for the 3D printer during the exchanges, in addition to the inexistence of mechanisms of trust or cooperation between well-established industries and the additive manufacturing industry. The findings suggested a permissioned blockchain to support the implementation of the additive symbiotic network, namely, to enable the physical transactions (quantity and quality of waste material PC sheets) and monitoring and reporting (additive manufacturing technology knowledge and final product's quantity and price). Future research venues include developing blockchain-based systems that enhance the development of additive symbiotic networks. publishersversion published

The authors acknowledge Fundacao para a Ciencia e a Tecnologia (FCT-MCTES) for its financial support via the project UIDB/00667/2020 (UNIDEMI). For decades, industrial companies have been collecting and storing high amounts of data with the aim of better controlling and managing their processes. However, this vast amount of information and hidden knowledge implicit in all of this data could be utilized more efficiently. With the help of data mining techniques unknown relationships can be systematically discovered. The production of semiconductors is a highly complex process, which entails several subprocesses that employ a diverse array of equipment. The size of the semiconductors signifies a high number of units can be produced, which require huge amounts of data in order to be able to control and improve the semiconductor manufacturing process. Therefore, in this paper a structured review is made through a sample of 137 papers of the published articles in the scientific community regarding data mining applications in semiconductor manufacturing. A detailed bibliometric analysis is also made. All data mining applications are classified in function of the application area. The results are then analyzed and conclusions are drawn. publishersversion published