Sustainable machining of superalloy in minimum quantity lubrication environment: leveraging GEP-PSO hybrid optimization algorithm

The present research focuses on establishing a sustainable manufacturing paradigm through the development of a model for optimizing machining parameters under a minimum quantity lubricating environment. The variables considered for investigation encompass cutting speed, feed, and depth of cut. To ga...

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Bibliographic Details
Published in:International journal of advanced manufacturing technology 2024-02, Vol.130 (9-10), p.4575-4601
Main Authors: Sen, Binayak, Debnath, Shantanu, Bhowmik, Abhijit
Format: Article
Language:English
Subjects:
Algorithms
CAE) and Design
Carbon
Computer-Aided Engineering (CAD
Cutting speed
Dry machining
Emission analysis
Emissions
Energy consumption
Engineering
Gene expression
Industrial and Production Engineering
Mathematical models
Mechanical Engineering
Media Management
Nickel base alloys
Optimization
Original Article
Particle swarm optimization
Process parameters
Sunflower oil
Superalloys
Surface roughness
Tool wear
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Summary:The present research focuses on establishing a sustainable manufacturing paradigm through the development of a model for optimizing machining parameters under a minimum quantity lubricating environment. The variables considered for investigation encompass cutting speed, feed, and depth of cut. To gauge the sustainability, five key indicators were assessed: total energy consumption, total carbon emission, total machining cost, surface roughness, and tool wear incurred during the machining of Inconel 690. Besides, in order to proficiently oversee machining parameters, the essential integration of modeling and optimization strategies assumes paramount significance. Consequently, a combined approach employing Gene Expression Programming (GEP) in conjunction with Particle Swarm Optimization (PSO) was adopted. GEP was employed to model the observed outcomes in relation to the design variables. Subsequently, the PSO technique was applied to ascertain the optimal configuration of machining parameters. Notably, a confirmation test demonstrated a minimal average discrepancy (less than 3%) between the experimental and GEP-PSO predicted results, showcasing the robustness of the hybrid optimization approach. Finally, machining with silica-doped sunflower oil in optimized machining conditions yields 20% energy reduction, 18.68% lower carbon emissions, an 11.76% cost decrement, a 20.21% reduction in surface roughness, and a 31.71% drop in tool wear, as compared to dry machining, making it an eco-friendly and cost-effective option for superalloy machining.
ISSN:0268-3768
1433-3015
DOI:10.1007/s00170-024-12962-9