Theoretical modelling and prediction of surface roughness for hybrid additive–subtractive manufacturing processes

ABSTRACT

Hybrid additive–subtractive manufacturing processes are becoming increasingly popular as a promising solution to overcome the current limitations of Additive Manufacturing (AM) technology and improve the dimensional accuracy and surface quality of parts. Surface roughness, as one of the most important surface quality measures, plays a key role in the fit of assemblies and thus needs to be thoroughly evaluated at the design and manufacturing stages. However, most of the studies on surface roughness modelling and analysis employ empirical approaches, and only consider the effect of a single manufacturing process. In particular, the existing surface roughness models are not applicable to hybrid additive–subtractive manufacturing processes in which a secondary process is involved. In this article, analytical models are established to predict the surface roughness of parts fabricated by AM as well as hybrid additive–subtractive manufacturing processes. A novel surface profile representation scheme is also proposed to increase the prediction accuracy. Case studies are performed to validate the effectiveness of the proposed models. An average of 4.25% error is observed for the AM case, which is significantly smaller than the prediction error of the existing models in the literature. Furthermore, in the hybrid case, an average of 91.83% accuracy is obtained.

KEYWORDS:

• Additive manufacturing
• hybrid additive–subtractive manufacturing process
• milling process
• surface roughness
• surface profile

Additional information

Funding

This work is supported by the U.S. National Science Foundation under Grant Number 1604825.

Notes on contributors

Lin Li

Lin Li is an associate professor in the Department of Mechanical and Industrial Engineering, at the University of Illinois at Chicago. He also serves as the director of U.S. Department of Energy Industrial Assessment Center and the director of the Sustainable Manufacturing Systems Research Laboratory, at the University of Illinois at Chicago. He received a B.E. degree in mechanical engineering from Shanghai Jiao Tong University, Shanghai, China, in 2001, and an M.S.E. degree in mechanical engineering, an M.S.E. degree in industrial and operations engineering, and a Ph.D. degree in mechanical engineering from the University of Michigan, Ann Arbor, in 2003, 2005, and 2007, respectively. His research interests include energy control and electricity demand response of manufacturing systems, environmental sustainability of additive manufacturing processes, cost-effective cellulosic biofuel manufacturing system, lithium-ion electric vehicle battery remanufacturing and reliability assessment, multi-machine system modelling and throughput estimation, and intelligent maintenance of manufacturing systems.

Azadeh Haghighi

Azadeh Haghighi is a Ph.D. student in the Department of Mechanical and Industrial Engineering, at the University of Illinois at Chicago.  She received a B.Sc. degree in industrial engineering from Sharif University of Technology, Tehran, Iran in 2011, and an M.Sc. degree in mechanical engineering from KTH Royal Institute of Technology, Stockholm, Sweden, in 2013.  Her research interests include dimensional and geometrical error characterization, modelling and control, tolerance analysis and design of assemblies, additive manufacturing, hybrid manufacturing, and process planning.

Yiran Yang

Yiran Yang is a Ph.D. candidate in the Department of Mechanical and Industrial Engineering at the University of Illinois at Chicago. She received a B.S. degree in vehicle engineering from Beijing Institute of Technology, China in 2013, and M.S. degree in mechanical engineering from Purdue University Northwest, IN, USA in 2015. Her research interests include additive manufacturing, environmental sustainability, cost evaluation, and life cycle analysis.