Tao Peng

TL;DR: In this article, the authors provide an overview of the sustainability of additive manufacturing (SAM), with a focus on energy and environmental impacts, and discuss the opportunities to reduce energy and material consumption through design, material preparation, manufacturing, usage, and end-of-life treatment.

TL;DR: A framework of big data-driven sustainable and smart additive manufacturing (BD-SSAM) is proposed which helped AM industry leaders to make better decisions for the beginning of life (BOL) stage of product life cycle and results indicate that energy consumption and quality of the product are adequately controlled which is helpful for smart sustainable manufacturing, emission reduction, and cleaner production.

TL;DR: In this article, the main elements of an individual energy-efficient machining system are summarized and discussed for energy optimisation methodologies and strategies for energy efficient process planning and production scheduling.

TL;DR: In this article, an integration model based on nonlinear process planning (NLPP) is proposed to implement such energy-saving method, and the Therblig-based model is used to predict the energy consumption of machine tools in product manufacturing process.

TL;DR: In this article, the impact of energy density on the porosity was analyzed with the data from experiments and existing works, and an effective energy-optimal (E2O) zone was proposed, where a relationship between energy density and porosity were developed.