Optimizing end milling parameters for custom 450 stainless steel using ant lion optimization and TOPSIS analysis

The current research examines the effectiveness of cryogenically treated (CT) tungsten carbide cutting inserts on Custom450 stainless steel using multi-objective soft computing approaches. The Taguchi-based L27 orthogonal array was employed in the experiments. During milling operations, cutting force, surface roughness, and cutting temperature were measured at different spindle speeds (rpm), feed rates (mm/min), and constant depths of cut (mm). The surface roughness and chip morphology of the Custom 450 stainless steel machined by cryo-treated (CT) and untreated (UT) cutting tool inserts were compared across various responses to cutting temperature and force. This paper also carried out multi-objective optimization, employing algorithm techniques such as Grasshopper Optimization Algorithm (GHO), Grey Wolf Optimization(GWO), Harmony Search Algorithm(HAS), and Ant line Optimization (ALO). The Multi-objective Taguchi approach and TOPSIS were first used to optimize the machining process parameters (spindle speed, feed rate, and cryogenic treatment) with different performance characteristics. Second, to relate the machining process parameters with the performance characteristics (cutting force, cutting temperature, and surface roughness), a mathematical model was developed using response surface analysis. The created mathematical response model was validated using ANOVA. The results showed that in IGD values of GHO, GWO, HSA and ALO module had 2.5765, 2.4706, 2.3647 and 2.5882 respectively, ALO has the best performance indicator. A Friedman’s test was also conducted, revealing higher resolution with the ALO method than with the HSA, GWO, and GHO methods. The results of the scanning test show that the ALO approach is workable

MFO tunned SVR models for analyzing dimensional characteristics of cracks developed on steam generator tubes

Accurate prediction of material defects from the given images will avoid the major cause in industrial applications. In this work, a Support Vector Regression (SVR) model has been developed from the given Gray Level Co-occurrence Matrix (GLCM) features extracted from Magnetic Flux Leakage (MFL) images wherein the length, depth, and width of the images are considered response values from the given features data set, and a percentage of data has been considered for testing the SVR model. Four parameters like Kernel function, solver type, and validation scheme, and its value and % of testing data that affect the SVR model's performance are considered to select the best SVR model. Six different kernel functions, and three different kinds of solvers are considered as two validation schemes, and 10% to 30% of the testing data set of different levels of the above parameters. The prediction accuracy of the SVR model is considered by simultaneously minimizing prediction measures of both Root Mean Square Error (RMSE), and Mean Absolute Error (MAE) and maximizing R-2 values. The Moth Flame Optimization (MFO) algorithm has been implemented to select the best SVR model and its four parameters based on the above conflict three prediction measures by converting multi-objectives into a single object using the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) method. The performance of the MFO algorithm is compared statistically with the Dragon Fly Optimization Algorithm (DFO) and Particle Swarm Optimization Algorithm (PSO).Web of Science1223art. no. 1237

Intelligent Modeling and Multi-Response Optimization of AWJC on Fiber Intermetallic Laminates through a Hybrid ANFIS-Salp Swarm Algorithm

The attainment of intricate part profiles for composite laminates for end-use applications is one of the tedious tasks carried out through conventional machining processes. Therefore, the present work emphasized hybrid intelligent modeling and multi-response optimization of abrasive waterjet cutting (AWJC) of a novel fiber intermetallic laminate (FIL) fabricated through carbon/aramid fiber, reinforced with varying wt% of reduced graphene oxide (r-GO) filled epoxy resin and Nitinol shape memory alloy as the skin material. The AWJC experiments were performed by varying the wt% of r-GO (0, 1, and 2%), traverse speed (400, 500, and 600 mm/min), waterjet pressure (200, 250, and 300 MPa), and stand-off distance (2, 3, and 4 mm) as the input parameters, whereas kerf taper (Kt) and surface roughness (Ra) were considered as the quality responses. A hybrid approach of a parametric optimized adaptive neuro-fuzzy inference system (ANFIS) was adopted through three different metaheuristic algorithms such as particle swarm optimization, moth flame optimization, and dragonfly optimization. The prediction efficiency of the ANFIS network has been found to be significantly improved through the moth flame optimization algorithms in terms of minimized prediction errors, such as mean absolute percentage error and root mean square error. Further, multi-response optimization has been performed for optimized ANFIS response models through the salp swarm optimization (SSO) algorithm to identify the optimal AWJC parameters. The optimal set of parameters, such as 1.004 wt% of r-GO, 600 mm/min of traverse speed, 214 MPa of waterjet pressure, and 4 mm of stand-off distance, were obtained for improved quality characteristics. Moreover, the confirmation experiment results show that an average prediction error of 3.38% for Kt and 3.77% for Ra, respectively, were obtained for SSO, which demonstrates the prediction capability of the proposed optimization algorithm

Intelligent Modeling and Multi-Response Optimization of AWJC on Fiber Intermetallic Laminates through a Hybrid ANFIS-Salp Swarm Algorithm

The attainment of intricate part profiles for composite laminates for end-use applications is one of the tedious tasks carried out through conventional machining processes. Therefore, the present work emphasized hybrid intelligent modeling and multi-response optimization of abrasive waterjet cutting (AWJC) of a novel fiber intermetallic laminate (FIL) fabricated through carbon/aramid fiber, reinforced with varying wt% of reduced graphene oxide (r-GO) filled epoxy resin and Nitinol shape memory alloy as the skin material. The AWJC experiments were performed by varying the wt% of r-GO (0, 1, and 2%), traverse speed (400, 500, and 600 mm/min), waterjet pressure (200, 250, and 300 MPa), and stand-off distance (2, 3, and 4 mm) as the input parameters, whereas kerf taper (Kt) and surface roughness (Ra) were considered as the quality responses. A hybrid approach of a parametric optimized adaptive neuro-fuzzy inference system (ANFIS) was adopted through three different metaheuristic algorithms such as particle swarm optimization, moth flame optimization, and dragonfly optimization. The prediction efficiency of the ANFIS network has been found to be significantly improved through the moth flame optimization algorithms in terms of minimized prediction errors, such as mean absolute percentage error and root mean square error. Further, multi-response optimization has been performed for optimized ANFIS response models through the salp swarm optimization (SSO) algorithm to identify the optimal AWJC parameters. The optimal set of parameters, such as 1.004 wt% of r-GO, 600 mm/min of traverse speed, 214 MPa of waterjet pressure, and 4 mm of stand-off distance, were obtained for improved quality characteristics. Moreover, the confirmation experiment results show that an average prediction error of 3.38% for Kt and 3.77% for Ra, respectively, were obtained for SSO, which demonstrates the prediction capability of the proposed optimization algorithm

Multi-Response Optimization of Abrasive Waterjet Cutting on r-GO-Reinforced Fibre Intermetallic Laminates through Moth–Flame Optimization Algorithm

Laminated metal-composite structures, also known as fibre metal laminates (FMLs), have emerged as prominent engineering materials in various industries, particularly in the domains of aircraft and automobile manufacturing. These materials are sought after due to their enhanced impact and fatigue resistance capabilities. The machining of FMLs plays a crucial role in achieving near-net shapes for the purpose of joining and assembling components. Delamination is a prevalent issue encountered during the process of conventional machining, thus rendering FMLs are challenging materials to machine. This study aims to investigate the cutting process of novel fibre intermetallic laminates (FILs) using the abrasive water jet (AWJ) cutting technique. The FILs consists of carbon and aramid fibers that are adhesively bonded with a resin matrix filled with reduced graphene oxide (r-GO) nano fillers. Moreover, these laminates contain embedded Nitinol shape memory alloy sheets as the skin materials. Specifically, the study aims to investigate the impact of different factors, such as the addition of reduced graphene oxide (r-GO) in the laminates (ranging from 0 to 2 wt%), traverse speed (ranging from 400 to 600 mm/min), waterjet pressure (ranging from 200 to 300 MPa), and nozzle height (ranging from 2 to 4 mm), on the material removal rate (MRR), delamination factor (FD), and kerf deviation (KD). ANOVA was used in the statistical analysis to determine the most influential parameters and their effects on the selected responses. The optimal AWJC parameters are determined using a metaheuristic-based moth–flame optimization (MFO) algorithm in order to enhance cut quality. The efficacy of MFO is subsequently compared with similar well-established metaheuristics such as the genetic algorithm, particle swarm algorithm, dragonfly algorithm, and grey-wolf algorithm. MFO was found to outperform in terms of several performance indices, including rapid divergence, diversity, spacing, and hypervolume values, among the algorithms compared

An Evolutionary Algorithmic Approach for Improving the Success Rate of Selective Assembly through a Novel EAUB Method

This work addresses an evolutionary algorithmic approach to reduce the surplus pieces in selective assembly to increase success rates. A novel equal area amidst unequal bin numbers (EAUB) method is proposed for classifying the parts of the ball bearing assembly by considering the various tolerance ranges of parts. The L16 orthogonal array is used for identifying the effectiveness of the proposed EAUB method through varying the number of bins of the parts of an assembly. Because of qualities such as minimal setting parameters, ease of understanding and implementation, and rapid convergence, the moth–flame optimization (MFO) algorithm is put forward in this work for identifying the optimal combination of bins of the parts of an assembly toward maximizing the percentage of the success rate of making assemblies. Computational results showed a 5.78% improvement in the success rate through the proposed approach compared with the past literature. The usage of the MFO algorithm is justified by comparing the computational results with the harmony search algorithm