Comparison of Laser Milling Performance against Difficult-To-Cut Alloys: Parametric Significance, Modeling and Optimization for Targeted Material Removal

• Published in: Materials 2019-05, Vol.12 (10), p.1674
• Main Authors: Ahmed, Naveed, Rafaqat, Madiha, Ishfaq, Kashif, Ur Rehman, Ateekh, Hassan, Adeel, Umer, Usama, Ragab, Adham Ezzat, Al-Zabidi, Ayoub
• Format: Article
• Language: English
• Subjects: Accuracy; Aluminum alloys; Aluminum base alloys; Aluminum composites; Design of experiments; Geometry; Laser machining; Lasers; Material removal rate (machining); Mathematical models; Nickel alloys; Optimization; Parameters; Performance evaluation; Response surface methodology; Statistical analysis; Titanium alloys; Titanium base alloys; Variables
• ISSN: 1996-1944; 1996-1944
• DOI: 10.3390/ma12101674

During laser milling, the objective is not always to maximize the material removal rate (MRR). Milling of new material with targeted MRR is challenging without prior knowledge and established sets of laser parameters. The laser milling performance has been evaluated for three important aerospace alloys, i.e., titanium alloy, nickel alloy and aluminum alloy using the response surface method experimental plan (54 experiments for each alloy). Parametric effects of five important laser parameters, statistical analysis (main effects, interaction effects, strength and direction of effects), mathematical modeling and optimality search is conducted for the said alloys. Under the non-optimized laser parameters, the actual MRR significantly varies from the targeted MRR. Variation in the aluminum alloy is at the top as compared to the other two alloys. Among other significant terms, three terms have the largest effect on MRR in the case of TiA, two terms in the case of NiA, and five terms in the case of AlA. Under the optimized sets of laser parameters, the actual material removal highly close to the desired level (100%) can be achieved with minimum variation in all the three alloys. Mathematical models proposed here have the capability to well predict material removal prior to the actual machining of Ti6Al4V, Inconel 718 and AA 2024.