Machining Behavior and Material Properties in Additive Manufacturing of Ti-6Al-4V Parts
Open Access
- Author:
- Gong, Xi
- Graduate Program:
- Mechanical Engineering
- Degree:
- Doctor of Philosophy
- Document Type:
- Dissertation
- Date of Defense:
- April 17, 2020
- Committee Members:
- Guhaprasanna Manogharan, Dissertation Advisor/Co-Advisor
Guhaprasanna Manogharan, Committee Chair/Co-Chair
Edward Demeter, Committee Member
Eric Russell Marsh, Committee Member
Saurabh Basu, Outside Member
Daniel Connell Haworth, Program Head/Chair - Keywords:
- Additive Manufacturing
Ti-6Al-4V
PSP Linkages
Machine Learning
Microstructure - Abstract:
- Additive Manufacturing (AM) has unique advantages in processing complex and customized part designs using superalloys. Metal AM parts currently do not meet the part tolerance and surface finish requirements for most mechanical applications. Hence, there is a growing need for integrated metal hybrid manufacturing through both “in-envelope” and “sequential” additive-subtractive manufacturing. Metal hybrid manufacturing integrates the unique advantages of the ability to produce complex geometries in AM with the desired surface finish and required tolerance in subtractive manufacturing. During the metal AM process, thermal cycling and different cooling rate lead to anisotropic mechanical properties and residual stress. Since AM metal parts are inherently different from traditional alloys, a critical gap to correlate as-built AM material properties, material characterization, machining parameters on resulting cutting force, specific cutting energy, and surface morphology have been revealed in this study. To the best of this author’s knowledge, this is the first reported effort to establish a comprehensive data science based framework to correlate highly unique AM-specific grain morphology and resulting post-AM machining behavior. This study reports on the influence of different AM processes (EBM, L-PBF, DED) that would lead to varying microstructure and subtractive machine parameters (feed, speed, and depth of cut) with varying microstructure under different machining conditions (feed, speed, and depth of cut) for AM Ti-6Al-4V alloy. It was found that specific cutting energy is statistically different across all AM materials and can vary by over 21.6% based on the AM process through a Taguchi-based orthogonal array design of experiments. A novel machine learning (ML) based PSP linkage is established to understand the relationship between AM grain morphology and resulting material response, i.e., machining behavior with a high prediction accuracy (> 99.5%).