Machine Learning-based Predictive Life Cycle Assessment Approach during Product Design - Choudhary, Deepika; Patil, Parth; Weise, Sebastian; Sangwan, Kuldip Singh; Mennenga, Mark; Herrmann, Christoph (Procedia CIRP, 2025)
Most of the past efforts for the environmental impact assessment has been carried out based on the manufactured product. Environmental improvement strategies intertwined with the product design will be more effective as 70-80% of the environmental impacts are fixed during the design phase. However, the major challenge to carry out the life cycle assessment (LCA) during design phase – predictive LCA – is the data scarcity during the design phase. Therefore, this paper proposes data augmentation using deep learning techniques to overcome this challenge for predictive LCA. This paper proposes a four-phase predictive LCA methodology consisting of (i) identification of design requirements and environmental aspects, (ii) database building based upon product descriptors & environmental performance data, (iii) deep learning assisted data preprocessing, and (iv) machine learning based predictive LCA models – random forest, support vector machine, and neural network. It was found that random forest gives the better prediction based on model evaluation metrics of mean absolute error (MAE), mean square error (MSE), root mean squared error (RMSE), and R-square (R2). This research equips environmentalists, companies, researchers, and businesses with a predictive environmental conscious approach to their decision-making processes early in the design phase, thereby, fostering a sustainable approach right from the inception of a product’s design.