Precision and Energy Usage for Additive Manufacturing
Market pressures on manufacturing enterprises incentivize minimum resource consumption while maintaining part quality. Facilities with advanced manufacturing tools often utilize rapid prototyping for production of complicated or specialty parts. Additive manufacturing offers an alternative to traditional production methods which are often time and resource expensive. This study aims to explore part quality and energy usage for additive manufacturing through a focused study of Fused Deposition Modeling and Photopolymer Jetting technologies. A control part is developed for maintaining test consistency across different machines. The control part design consists of various positive and negative features including width varied slots and walls, ramps, and curved features so that the manufacturing of different surfaces may be investigated. Several different machine models are tested to evaluate precision for a variety of applications. Part quality is quantified by measuring the surface roughness in two directions for the control test part printed on each machine. Qualitatively, part quality is assessed by positive and negative feature resolution. High quality machines resolve features closely to design specifications. Lower quality machines do not resolve some features. In addition to exploring the effects of advertised print precision, layup density is varied on two machines. Advertised print resolution does not well represent the achievable feature sizes found in this study. Energy usage is quantified by measuring electricity demands while printing the control part on each of the five different machines. Power consumption in additive manufacturing is found to follow a distinct pattern comprised of standby, warm up, printing and idle phases. Measurement and analysis suggest a relationship between the precision of these machines and their respective energy demand. Part quality is found to generally improve with increased initial and process resource investment. The energy and quality assessment methods developed in this study are applicable to a greater variety of additive manufacturing technologies and will assist designers as additive manufacturing becomes more production friendly. The presented data also provides designers and production planners insight for improvements in the process decision making.