Grey integrated Multiobjective-Particle Swarm Optimization (MOPSO) for Machining assessment and predictive modeling of Cutting Forces generated during Polymer nanocomposite Drilling

Abstract

Carbon nanomaterials reinforced composite materials have been broadly utilized in manufacturing engineering due to improved thermal resistivity, reduced weight, and other improved mechanical properties. This article highlights the drilling experimentation of zero-dimensional (0-D) Carbon nano onion (CNO) reinforced polymer composite. For this, three drilling constraints was considered viz., spindle speed, feed rate, and weight % of nanomaterial reinforced. The objective is to achieve the desired value of generated drilling forces such as Torque (Nm) and Thrust Force (N) during the machining procedure of developed composite samples. The Multiobjective-Particle Swarm Optimization (MOPSO) is utilized to achieve optimal results from the multi-decision criterion for the Machining performance. Exploiting this optimization process, non-dominated solutions were obtained, and the Pareto front was identified. Practical applications for the discovered relationships include using Grey relation analysis (GRA) to extract the most relevant finding from the Pareto Front space of optimal solutions. Using the GRA, the optimum solution was found: Spindle Speed of 1000 RPM, Feed Rate of 100 mm/min, and CNO weight percentage of 0.5. After this, a confirmation test was performed, the expected effects have been confirmed. The findings reveal that the proposed optimization module can be recommended for online quality and productivity control.

A static load position identification method for optical fiber-composite structures based on particle swarm optimization- back Propagation neural network algorithm

“…This article maps all data sets to the interval [0, 1]. Data sets are normalization using the following equation (13).…”

“…11 To overcome these shortcomings, some optimization algorithms are usually used to optimize the parameters of neural network, such as Genetic Algorithm (GA), Particle Swarm Optimization (PSO), Ant Colony Optimization (ACO), Grey Wolf Optimizer (GWO), and so on. [12][13][14][15][16] At present, the use of hybrid algorithms has become the choice of more scholars, such as Grey Wolf Optimizer-Long Short-Term Memory (GWO-LSTM), Multi-Objective-Particle Swarm Optimization-Grey Relation Analysis (MOPSO-GRA), Multi-Objective-Particle Swarm Optimization-Radial Basis Function based Support Vector Regression (MOPSO-RBFSVR), GA-ANN, Continuous Genetic Algorithm-Particle Swarm Optimization (CGA-PSO), etc.…”

“…11 To overcome these shortcomings, some optimization algorithms are usually used to optimize the parameters of neural network, such as Genetic Algorithm (GA), Particle Swarm Optimization (PSO), Ant Colony Optimization (ACO), Grey Wolf Optimizer (GWO), and so on. 12–16…”

“…The obtained experimental results indicated that the proposed combined model can capture non-linear characteristics of wind speed time series (WSTS), achieving better forecasting performance than single forecasting models, in terms of accuracy. Kesarwani et al 13 highlighted the drilling experimentation of zero-dimensional (0-D) Carbon nano onion (CNO) reinforced polymer composite. The MOPSO is utilized to achieve optimal results from the multi-decision criterion for the Machining performance.…”