Abstract
Manufacturers have great power to change the sustainability of products over the whole life cycle, but they need holistic life cycle models to guide those decisions. Challenges exist in connecting the product’s life cycle data to model-based sustainability metrics and in quantifying uncertainty in the product data. This study develops a life-cycle energy framework around two application cases to showcase informed and transparent decision-making. The case studies investigate additively manufactured parts in two friction scenarios, one where low friction is desired and one where high friction is preferred. The layer height is chosen as process parameter of additive manufacturing that changes the surface roughness of the sample parts, but also the manufacturing time and energy. The use phase energy in the first friction scenario is influenced by the user behavior, and by a random input function in the second scenario. The life-cycle energy framework is used to discuss total life cycle energy for each scenario. In general, this framework should be used to better connect product use phase and manufacturing phase, in particular by examining the interconnections of part design, manufacturing phase impacts, and use performance. Product quality is the central aspect of optimization. The framework can be used for engineering education and be expanded to study data uncertainty, user behavior, system complexity, process chains, machine learning, sustainability metrics, and more.
