Measurement of environmental aspect of 3-D printing process using soft computing methods

For improving the environmental performance of the manufacturing industry across the globe, 3-D printing technology should be increasingly adopted as a manufacturing procedure. It is because this technology uses the polymer PLA (Polyactic acid) as a material, which is biodegradable, and saves fuel and reduces waste when fabricating prototypes. In addition, the technology can be located near to industries and fabricates raw material itself, resulting in reduction of transport costs and carbon emission. However, due to its high production cost, 3-D printing technology is not yet being adopted globally. One way of reducing the production cost and improving environmental performance is to formulate models that can be used to operate 3-D printing technology in an efficient way. Therefore, this paper aims to deploy the soft computing methods such as genetic programming (GP), support vector regression and artificial neural network in formulating the laser power-based-open porosity models. These methods are applied on the selective laser sintering (a 3-D printing process) process data. It is found that GP evolves the best model that is able to predict open porosity satisfactorily based on given values of laser power. The laser power-based-open porosity model formulated can assist decision makers in operating the SLS process in an effective and efficient way, thus increasing its viability for being adopted as a manufacturing procedure and paving the way for a sustainable environment across the globe. (C) 2015 Published by Elsevier Ltd.