TY - JOUR
T1 - Multi-response optimization in high-speed machining of Ti-6Al-4V using TOPSIS-fuzzy integrated approach
AU - Abbas, Adel T.
AU - Sharma, Neeraj
AU - Anwar, Saqib
AU - Luqman, Monis
AU - Tomaz, Italo
AU - Hegab, Hussien
N1 - Funding Information:
This research received no external funding. The authors extend their appreciation to the Deanship of Scientific Research at King Saud University for funding this work through research group No. RGP-1439-020.
Funding Information:
Acknowledgments: The authors extend their appreciation to the Deanship of Scientific Research at King Saud University for funding this work through research group No. RGP-1439-020.
Publisher Copyright:
© 2020 by the authors. Licensee MDPI, Basel, Switzerland.
PY - 2020/3/1
Y1 - 2020/3/1
N2 - Titanium alloys are widely used in various applications including biomedicine, aerospace, marine, energy, and chemical industries because of their superior characteristics such as high hot strength and hardness, low density, and superior fracture toughness and corrosion resistance. However, there are different challenges when machining titanium alloys because of the high heat generated during cutting processes which adversely affects the product quality and process performance in general. Thus, optimization of the machining conditions while machining such alloys is necessary. In this work, an experimental investigation into the influence of different cutting parameters (i.e., depth of cut, cutting length, feed rate, and cutting speed) on surface roughness (Rz), flank wear (VB), power consumption as well as the material removal rate (MRR) during high-speed turning of Ti-6Al-4V alloy is presented and discussed. In addition, a backpropagation neural network (BPNN) along with the technique for order of preference by similarity to ideal solution (TOPSIS)-fuzzy integrated approach was employed to model and optimize the overall cutting performance. It should be stated that the predicted values for all machining outputs demonstrated excellent agreement with the experimental values at the selected optimal solution. In addition, the selected optimal solution did not provide the best performance for each measured output, but it achieved a balance among all studied responses.
AB - Titanium alloys are widely used in various applications including biomedicine, aerospace, marine, energy, and chemical industries because of their superior characteristics such as high hot strength and hardness, low density, and superior fracture toughness and corrosion resistance. However, there are different challenges when machining titanium alloys because of the high heat generated during cutting processes which adversely affects the product quality and process performance in general. Thus, optimization of the machining conditions while machining such alloys is necessary. In this work, an experimental investigation into the influence of different cutting parameters (i.e., depth of cut, cutting length, feed rate, and cutting speed) on surface roughness (Rz), flank wear (VB), power consumption as well as the material removal rate (MRR) during high-speed turning of Ti-6Al-4V alloy is presented and discussed. In addition, a backpropagation neural network (BPNN) along with the technique for order of preference by similarity to ideal solution (TOPSIS)-fuzzy integrated approach was employed to model and optimize the overall cutting performance. It should be stated that the predicted values for all machining outputs demonstrated excellent agreement with the experimental values at the selected optimal solution. In addition, the selected optimal solution did not provide the best performance for each measured output, but it achieved a balance among all studied responses.
KW - Machining
KW - Optimization
KW - Ti-6Al-4V
UR - http://www.scopus.com/inward/record.url?scp=85081692031&partnerID=8YFLogxK
U2 - 10.3390/ma13051104
DO - 10.3390/ma13051104
M3 - Article
AN - SCOPUS:85081692031
VL - 13
JO - Materials
JF - Materials
SN - 1996-1944
IS - 5
M1 - 1104
ER -
