Non-traditional machining (NTM) has gained significant attention in the last decade due to its ability to machine conventionally hard-to-machine materials. However, NTMs suffer from several disadvantages such as higher initial cost, lower material removal rate, more power consumption, etc. NTMs involve several process parameters, the appropriate tweaking of which is necessary to obtain economical and suitable results. However, the costly and time-consuming nature of the NTMs makes it a tedious and expensive task to manually investigate the appropriate process parameters. The NTM process parameters and responses are often not linearly related and thus, conventional statistical tools might not be enough to derive functional knowledge. Thus, in this paper, three popular machine learning (ML) methods (viz. linear regression, random forest regression and AdaBoost regression) are employed to develop predictive models for NTM processes. By considering two high-fidelity datasets from the literature on electro-discharge machining and wire electro-discharge machining, case studies are shown in the paper for the effectiveness of the ML methods. Linear regression is observed to be insufficient in accurately mapping the complex relationship between the process parameters and responses. Both random forest regression and AdaBoost regression are found to be suitable for predictive modelling of NTMs. However, AdaBoost regression is recommended as it is found to be insensitive to the number of regressors and thus is more readily deployable.

For generating a texture or pattern on a work surface, one of the emerging processes is laser surface texturing (LST). It is an effective method for producing texture on a work surface. Literature shows that various lasers have been applied to generate textures on the surface of work materials. Recently, LST has shown tremendous potential in the field of biomedical applications. Applying the LST process, the efficacy of the biomaterial has been drastically improved. This paper presents an in-depth review of laser surface texturing for biomedical applications. The effect of LST on important biomaterial has been thoroughly studied; it was found that LST has extreme potential for surface modification of biomaterial and can be utilized for biomedical applications.

As the volume of data generated by information systems continues to increase, machine learning (ML) techniques have become essential for the extraction of meaningful insights. However, the sheer volume of data often causes these techniques to become sluggish. To overcome this, feature selection is a vital step in the pre-processing of data. In this paper, we introduce a novel K-nearest neighborhood (KNN)-based wrapper system for feature selection that leverages the iterative improvement ability of the weighted superposition attraction (WSA). We evaluate the performance of WSA against seven well-known metaheuristic algorithms, i.e., differential evolution (DE), genetic algorithm (GA), particle swarm optimization (PSO), flower pollination algorithm (FPA), symbiotic organisms search (SOS), marine predators’ algorithm (MPA) and manta ray foraging optimization (MRFO). Our extensive numerical experiments demonstrate that WSA is highly effective for feature selection, achieving a decrease of up to 99% in the number of features for large datasets without sacrificing classification accuracy. In fact, WSA-KNN outperforms traditional ML methods by about 18% and ensemble ML algorithms by 9%. Moreover, WSA-KNN achieves comparable or slightly better solutions when compared with neural networks hybridized with metaheuristics. These findings highlight the importance and potential of WSA for feature selection in modern-day data processing systems.

Robots are being increasingly utilized for various operations in industrial and household applications. One such application is for spray painting, wherein atomized paint particles are sprayed on a surface to coat the surface with paint. As there are different models of robots available for the job, it becomes crucial to select the best among them. Multi-criteria decision-making (MCDM) techniques are widely used in various fields to tackle selection problems where there are many conflicting criteria and several alternatives. This work focuses on selecting the best robot among twelve alternatives based on seven criteria, among which payload, speed, and reach are beneficial criteria while mechanical weight, repeatability, cost, and power consumption are cost criteria. Five MCDM techniques, namely combination distance-based assessment (CODAS), complex proportional assessment (COPRAS), combined compromise solution (CoCoSo), multi-attributive border approximation area comparison (MABAC), and višekriterijumsko kompromisno rangiranje (VIKOR) were used for the selection while a weight calculation was performed using an objective weight calculation technique called MEREC. HY1010A-143 was found to be the most suitable robot for spray-painting applications by four of the five techniques used. Correlation studies showed a significant level of correlation among all the MCDM techniques.

This study delves into the exploration of a novel Multi-objective Snow Ablation Optimizer (MOSAO) algorithm, tailored for addressing expansive Optimal Power Flow (OPF) challenges inherent in intricate power systems. These systems are often complemented with the integration of renewable energy modalities and the state-of-the-art Flexible AC Transmission Systems (FACTS). Building upon the foundational framework of a previously documented single-objective Snow Ablation Optimizer, we have evolved it into the MOSAO paradigm. This transformation is achieved by harnessing the potency of non-dominated sorting coupled with the crowding distance strategy. The task of OPF magnifies in complexity when integrating renewable energy resources due to their inherent unpredictability and intermittent nature. As the modern power landscape evolves, FACTS devices are witnessing an increasing deployment to mitigate network demand and alleviate congestion issues. Within the ambit of this research, we've incorporated a stochastic wind energy source, working synergistically with an array of FACTS instruments. These encompass the static VAR compensator, thyristor-controlled series compensator and thyristor-driven phase shifter, all operating within the confines of an IEEE-30 bus framework. Strategic placement and calibration of these FACTS devices aim to optimize the system by minimizing the cumulative fuel expenditure. The capricious essence of wind as an energy source is elegantly depicted through the lens of Weibull probability density graphs. To distil the optimal middle-ground solutions, we've employed a fuzzy decision-making matrix. When benchmarking our findings against those derived from other esteemed optimization algorithms, we observe a notable distinction. The results from the modified IEEE-30 bus system accentuate the superior convergence, diversity and distribution attributes of MOSAO, especially when scrutinizing power flows. The MOSAO source code is available at: https://github.com/kanak02/MOSAO.