The kerf taper angle is one of the most significant parameters for AWJ cutting. Choosing the best cutting parameters is critical to reducing the kerf taper. Therefore, the aim of this work is to demonstrate on to implement RSM in conjunction with the Rat swarm optimizer algorithm to optimize the kerf taper angle of a Titanium alloy. Thirty-two number of experiments were conducted according to CCD considering five process parameters namely jet traverse rate, water jet pressure, abrasive flow rate, stand-off distance and diameter of focusing nozzle to achieve kerf taper angle. A second-order full quadratic model is used for mathematical modeling. The result demonstrates that the model is sufficiently adequate and acceptable. A recent Rat Swarm Optimization (RSO) algorithm is considered for finding out the global optimal solution. It is observed that after optimizing the process that the jet traverse rate of 1.952 mm/sec, water jet pressure of 250.548 MPa, abrasive flow rate of 0.554 kg/min, stand-off distance of 3.75 mm and diameter of focusing nozzle of 2.0 mm gives minimized with 0.754 degree of kerf taper angle. A series of verification tests are carried out to ensure that the optimization technique is accurate in determining the best levels of process.

This article proposes a Kapur-based hybridized Water Cycle and Moth-Flame Optimisation (WCMFO) algorithm that combines a water cycle algorithm (WCA) and moth flame optimisation (MFO) in multilevel thresholding of brain MR image segmentation. The WCMFO algorithm, proposed by Khalilpourazari and Khalilpourazary, gives both WCA and MFO advantages, while avoiding some of the drawbacks of either approach on its own, as demonstrated by faster convergence with broader exploration and exploitation capabilities. Experiments on 10 axial, T₂-weighted test images were performed using Kapur entropy as the objective function at a threshold level of m = 2–5. The spiral movement of the behaviour of the moths is used for better exploitation in the WCA to find the global optimum values. WCMFO results, such as objective function value, peak signal to noise ratio, Central processing unit time and standard deviation, are collated and compared with other existing adaptive wind-driven optimization algorithm, adaptive bacterial foraging and particle swarm optimization algorithms. Experimental findings and comparison demonstrated that hybridized WCMFO algorithm was superior to the other algorithms. Moreover, the best segmentation is achieved on grey matter, white matter and cerebrospinal fluid that allows for better clinical decision-making and diagnosis in the medical field. Therefore, the proposed multilevel thresholding-based hybridized WCMFO algorithm is believed to be the most prominent preference for segmenting such complex brain images.

In this study, a novel hybrid Water Cycle Moth-Flame Optimization (WCMFO) algorithm is proposed for multilevel thresholding brain image segmentation in Magnetic Resonance (MR) image slices. WCMFO constitutes a hybrid between the two techniques, comprising the water cycle and moth-flame optimization algorithms. The optimal thresholds are obtained by maximizing the between class variance (Otsu’s function) of the image. To test the performance of threshold searching process, the proposed algorithm has been evaluated on standard benchmark of ten axial T2-weighted brain MR images for image segmentation. The experimental outcomes infer that it produces better optimal threshold values at a greater and quicker convergence rate. In contrast to other state-of-the-art methods, namely Adaptive Wind Driven Optimization (AWDO), Adaptive Bacterial Foraging (ABF) and Particle Swarm Optimization (PSO), the proposed algorithm has been found to be better at producing the best objective function, Peak Signal-to-Noise Ratio (PSNR), Standard Deviation (STD) and lower computational time values. Further, it was observed thatthe segmented image gives greater detail when the threshold level increases. Moreover, the statistical test result confirms that the best and mean values are almost zero and the average difference between best and mean value 1.86 is obtained through the 30 executions of the proposed algorithm.Thus, these images will lead to better segments of gray, white and cerebrospinal fluid that enable better clinical choices and diagnoses using a proposed algorithm.

Purpose: The goal of this study is to determine the values of the process parameters that should be used during the machining of ceramic tile using the abrasive water jet (AWJ) process in order to achieve the lowest possible values for surface roughness and kerf taper angle.Design/methodology/approach: In the present work, ceramic tile is processed by the AWJ process and experimental data were recorded using the RSM approach based Box–Behnken design matrix. The input process factors were water jet pressure, jet traverse speed, abrasive flow rate and standoff distance, to determine the surface roughness and kerf taper angle. ANOVA was used to check the adequacy of model and significance of process parameters. Further, the elite opposition-based learning grasshopper optimization (EOBL-GOA) algorithm was implemented to identify the simultaneous optimization of multiple responses of surface roughness and kerf taper angle in AWJ.Findings: The suggested EOBL-GOA algorithm is suitable for AWJ of ceramic tile, as evidenced by the error rate of ±2 percent between experimental and predicted solutions. The surfaces were evaluated with an SEM to assess the quality of the surface generated with the optimal settings. As compared with initial setting of the SEM image, it was noticed that the bottom cut surface was nearly smooth, with less cracks, striations and pits in the improved optimal results of the SEM image. The results of the analysis can be used to control machining parameters and increase the accuracy of AWJed components.Originality/value: The findings of this study present an innovative method for assessing the characteristics of the nontraditional machining processes that are most suited for use in industrial and commercial applications.

In this work, an experimental attempt has been made to model theresponse variables, i.e kerf taper angle and MRR in AWJ process whilemachining of NFRP composite. The experimental plan is based on BBDdesign. The feed rate of sand, stand-off distance, traverse speed andwater jet pressure have been varied to investigate their effect onresponses. The ANOVA was used to determine the significance ofdeveloped models. The model is further coupled with the Improved SOAalgorithm to optimise AWJ machining parameters. The result provedthat the standard SOA optimisation algorithm is inferior to theimproved optimisation algorithm, which can achieve greater processingquality. Subsequently, the outcomes of the optimisation results werereviewed and experimentally confirmed. The error between experimentaland predicted values was proven to be less than 2% at the inputvariable combinations with the best performance, demonstrating theviability and efficacy of the chosen strategy.