A control system design for selective laser melting process: toward sustainable additive manufacturing
Year: 2025
Type: article
Abstract: Additive manufacturing (AM) has been widely used in industry and academia, exhibiting advantages such as fabricating complex objects and reducing waste compared to conventional manufacturing. Considering the rapid growth of additively manufactured products in the consumer market, studying the sustainability aspects of AM is increasingly gaining attention. Therefore, this paper seeks to contribute to the sustainable manufacturing research domain in the AM processes. The focus of this study is the development of a theoretical model-based control system, which is evaluated against an existing model. To this purpose, a new control system is proposed to achieve a lower process control signal in the selective laser melting (SLM) additive manufacturing process, aiming to reduce energy consumption. The proposed controller seeks to modify the melt pool by manipulating the laser power and applying less laser power to the SLM AM system. This achievement leads to a more sustainable AM process. Also described is the performance of the proposed controller and when compared with other control systems reported in the literature, indicating its superiority in terms of laser power consumption. Furthermore, the robustness of the proposed controller is investigated under conditions of measurement noise and parameter uncertainty using worst-case scenarios and Mont Carlo simulations, indicating its efficacy in real-world scenarios. Finally, some remarks have been made regarding further developing such control systems to provide decision support in finding trade-offs, considering various AM process environmental and sustainability indicators, production time, and part quality.
Source: Procedia CIRP
Authors: Danial Pazoki, Denis Dowling, Pezhman Ghadimi
Language: English
Subfield: Mechanical Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: diamond
Award: I-Form_Phase 2
OpenAlex ID: W4413097320
