Investigations of Machining Characteristics in the Upgraded MQL-Assisted Turning of Pure Titanium Alloys Using Evolutionary Algorithms

Environmental protection is the major concern of any form of manufacturing industry today. As focus has shifted towards sustainable cooling strategies, minimum quantity lubrication (MQL) has proven its usefulness. The current survey intends to make the MQL strategy more effective while improving its...

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Bibliographic Details
Published in:Materials 2019-03, Vol.12 (6), p.999
Main Authors: Singh, Gurraj, Pruncu, Catalin Iulian, Gupta, Munish Kumar, Mia, Mozammel, Khan, Aqib Mashood, Jamil, Muhammad, Pimenov, Danil Yurievich, Sen, Binayak, Sharma, Vishal S
Format: Article
Language:English
Subjects:
Aerodynamics
Compressed air
Cooling
Cooling effects
Environmental protection
Evolutionary algorithms
Genetic algorithms
Grain size
Inlet nozzles
Lubricants & lubrication
Lubrication
Mechanical engineering
Optimization algorithms
Particle swarm optimization
Process engineering
Process parameters
R&D
Research & development
Surface properties
Titanium alloys
Titanium base alloys
Tool wear
Turning (machining)
Variance analysis
Vortex chambers
Vortices
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Summary:Environmental protection is the major concern of any form of manufacturing industry today. As focus has shifted towards sustainable cooling strategies, minimum quantity lubrication (MQL) has proven its usefulness. The current survey intends to make the MQL strategy more effective while improving its performance. A Ranque⁻Hilsch vortex tube (RHVT) was implemented into the MQL process in order to enhance the performance of the manufacturing process. The RHVT is a device that allows for separating the hot and cold air within the compressed air flows that come tangentially into the vortex chamber through the inlet nozzles. Turning tests with a unique combination of cooling technique were performed on titanium (Grade 2), where the effectiveness of the RHVT was evaluated. The surface quality measurements, forces values, and tool wear were carefully investigated. A combination of analysis of variance (ANOVA) and evolutionary techniques (particle swarm optimization (PSO), bacteria foraging optimization (BFO), and teaching learning-based optimization (TLBO)) was brought into use in order to analyze the influence of the process parameters. In the end, an appropriate correlation between PSO, BFO, and TLBO was investigated. It was shown that RHVT improved the results by nearly 15% for all of the responses, while the TLBO technique was found to be the best optimization technique, with an average time of 1.09 s and a success rate of 90%.
ISSN:1996-1944
1996-1944
DOI:10.3390/ma12060999