Structural integrity is a crucial aspect of engineering components, particularly in the field of additive manufacturing (AM). Surface roughness is a vital parameter that significantly influences the structural integrity of additively manufactured parts. This research work focuses on the prediction of the surface roughness of additive-manufactured polylactic acid (PLA) specimens using eight different supervised machine learning regression-based algorithms. For the first time, explainable AI techniques are employed to enhance the interpretability of the machine learning models. The nine algorithms used in this study are Support Vector Regression, Random Forest, XGBoost, AdaBoost, CatBoost, Decision Tree, the Extra Tree Regressor, the Explainable Boosting Model (EBM), and the Gradient Boosting Regressor. This study analyzes the performance of these algorithms to predict the surface roughness of PLA specimens, while also investigating the impacts of individual input parameters through explainable AI methods. The experimental results indicate that the XGBoost algorithm outperforms the other algorithms with the highest coefficient of determination value of 0.9634. This value demonstrates that the XGBoost algorithm provides the most accurate predictions for surface roughness compared with other algorithms. This study also provides a comparative analysis of the performance of all the algorithms used in this study, along with insights derived from explainable AI techniques.

Electrochemical action and subsequent discharges are utilized in electrochemical discharge machining (ECDM) for the fabrication of components by subtracting the undesired material. However, as the process progresses, localized electrolyte vaporization (machining zone) and its leading effects limit the process performance. Controlled delivery of fresh electrolyte into the machining zone to replenish the vaporized electrolyte improves the ECDM process performance, utilized in the electrolyte injection-ECDM (EI-ECDM) process. Apart from the control strategies, the literature lacks a detailed investigation of the phenomena involved in deteriorating the ECDM’s machining performance. Few researchers enlighten that the deposition of the machined by-products on the tooltip might be a significant reason. Therefore, the present work is carried out to investigate the influence of deposition of the machined by-products on outcomes of the ECDM process at different parametric conditions. Various scientific tools and techniques are used to explore the underlying phenomena of machined by-products deposition. It shows that deposition significantly alters the geometry, surface texture, and properties of tool-electrode, which intern affects the ECDM’s performance. Further, experimental results and subsequent characterization reveal that EI-ECDM can significantly control the deposition and enhance the process performance. Thus, a multi-response optimization was performed to increase the applicability of the EI-ECDM process.

Developing an educational game about sustainable manufacturing can be an effective way to teach people about the importance of sustainable practices in the manufacturing industry. Through educational sustainable manufacturing games, users can learn about the impact of sustainable manufacturing on the environment, along with strategies and techniques that can be used to reduce waste and methods of sustainable production. A minimal number of articles have focused on board game development for sustainable manufacturing, and of the few, most focused on educational simulation of machines/systems, catering to specific profession. This paper chronicles the creation of “Spaceship Earth,” a sustainable manufacturing board game from game design to testing. It explores the entire process of developing an educational game, from game theory to final testing. The game incorporates various elements such as resource management, decision-making, and problem-solving to simulate the real-world challenges faced by manufacturers when implementing sustainable practices. By examining the design of educational games, this paper aims to gain insight into the constraints of engineering education gamification through development of a board game. The paper demonstrates the potential of educational games as a means of promoting sustainability and provides useful framework for designing and developing similar games in the future.

Structural integrity is a crucial aspect of engineering components, particularly in the field of additive manufacturing (AM). Surface roughness is a vital parameter that significantly influences the structural integrity of additively manufactured parts. In this study, we present a comprehensive investigation into the relationship between surface roughness and structural integrity of Polyactic Acid (PLA) specimens produced through additive manufacturing. This research work focuses on the prediction of surface roughness of Additive Manufactured Polyactic Acid (PLA) specimens using eight different supervised machine learning regression-based algorithms. For the first time, Explainable AI techniques are employed to enhance the interpretability of the machine learning models. The eight algorithms used in this study are Support Vector Regression, Random Forest, XG Boost, Ada Boost, Catboost, Decision Tree, Extra Tree regressor, and Gradient Boosting regressor. The study analyzes the performance of these algorithms to predict the surface roughness of PLA specimens, while also investigating the impact of individual input parameters through Explainable AI methods. The experimental results indicate that the XG Boost algorithm outperforms the other algorithms with the highest coefficient of determination value of 0.9634. This value demonstrates that the XG Boost algorithm provides the most accurate predictions for surface roughness compared to other algorithms. The study also provides a comparative analysis of the performance of all the algorithms used in this study, along with insights derived from Explainable AI techniques.