In past few decades, the non-traditional machining techniques of electrical discharge machining (EDM) has been developed to machine materials that are difficult to cut. Additionally, the EDM method has also been employed to change the material's surface properties by applying a layer on the workpiece that is up to a few microns thick in order to improve the surface attributes. In past three decades, several numerous investigations on surface modification using EDM have been conducted globally. Present study provides a comprehensive review along with bibliometric investigation of the many approaches used to enhance the surface topology of the materials during the EDM process from 1993 to 2023. Firstly, a brief introduction to EDM-based different surface modification techniques such as electric discharge coating, surface texturing, surface alloying, and powder-mixed EDM has been given. All related documents were acquired from Scopus Core Collection and evaluated using a bibliometric analysis technique. The bibliometric analysis was examined based on the time distribution of articles, geography, top-cited documents, analysis of authors’ keywords, country-wise publications, etc. The result showed that India dominates this research area, followed by China, and there is a growing trend in the number of publications. Further, the influence of EDM parameters, dielectric fluid, tool, and work material etc. has also been discussed. The research work published related to topic has been summarized and resented in tabular format. This review offers interested scholars with a thorough overview of the significant achievements in the subject over the last few decades, including a description of the most relevant studies and their results. Moreover, potential areas for further investigation are emphasized with the goal of revitalize the interest of the worldwide scientific community in advancing this method.

The trend of growth and global population expansion generate new issues in health care, demanding rapid solutions tailored to specific clinical needs. Additive manufacturing is a rapidly evolving technology for biomedical applications and metal implant fabrication. Additive manufacturing allows the development of complex structures with biomimicry attributes in reduced lead time. This article comprises a general overview of the implementation of various additive manufacturing technologies in biomedical applications. In addition, the research area under examination has also been described using bibliometric analysis, which includes the number of publications each year and in terms of regional distribution, top-cited documents, citation mapping of journals and researchers, co-occurrence mapping, keyword analysis, country-wise publications, cluster assessment, content analysis, etc. Moreover, future study directions are highlighted with an objective to recuperate the curiosity of the global research community to mature this technique further.

One of the primary causes of greenhouse gas emanation is the use of fossil fuels in vehicles and aircraft. Amidst the present fuel and environmental crisis, the transportation industry is looking for new ways to enhance fuel economy and cut greenhouse gas emissions. Weight loss, improved conventional engine efficiency, the development of new, more energy-efficient powertrains, and the use of low-emission fuels, such as biofuels, are among these initiatives. Reducing weight is recognized as one of the most economical approaches. Reducing the mass of automobiles and aircraft is a significant challenge for the automobile and aerospace industries. Using lightweight structural materials such as aluminum (Al), magnesium (Mg), and titanium (Ti) is an excellent technique to reduce weight, especially in aviation. In this direction, a multi-material model in the aerospace and automotive industry seems to be another promising solution. <!----> <!----> <!----> <!---->

Artificial intelligence (AI) and machine learning (ML) are revolutionizing composite manufacturing by enabling data-driven forecasts, process optimization, defect identification, and intelligent process control. The objective of this review is to discuss the recent innovations and emerging trends in the application of AI/ML in the major composite manufacturing technologies, such as automated fiber placement (AFP), automated tape laying (ATL), resin transfer molding (RTM), resin infusion, compression molding, pultrusion, additive manufacturing, and non-destructive evaluation (NDE). This paper discusses the capabilities and limitations of advanced technologies, including deep learning, physics-informed machine learning, transfer learning, multimodal learning, explainable AI, digital twin, surrogate modeling, and closed-loop control, and their future potential. A comprehensive literature survey explored the latest developments in composite manufacturing using AI/ML. The review categorized the research by manufacturing process, AI/ML approach, sensor and inspection modality, prediction and/or optimization objective, and reported performance. The review thoroughly addressed applications in process monitoring, defect prediction, quality evaluation, surrogate modeling, intelligent inspection, and adaptive process control. The review also surveyed data scarcity, domain shift, model interpretability, multimodal sensing, and the incorporation of physical process knowledge. The literature study reveals a substantial potential for AI/ML in prediction, inspection, process optimization, and manufacturing decision-making. Reported applications include RTM filling-pattern prediction, automated thermographic and ultrasonic flaw identification, process-properties prediction, and composite additive-manufacturing inspection. For example, a PixelRNN surrogate model predicted RTM filling patterns with up to 97.35% accuracy, at roughly half the computational cost of exhaustive simulation. In ultrasonic composite inspection, ML-based thermographic impact-damage detection reached 98.8% classification accuracy, while DenseNet121 and VGG19 reached 98.6%. Integrating deep learning and infrared thermography in fiber-reinforced composite additive manufacturing achieved a mean average precision of 99.3%. Under the reported experimental conditions, the defect-area error was within 6%. These results are promising, but differences in datasets, sensors, fault types, validation procedures, and performance measures make direct comparisons difficult. Major challenges for industrial adoption include small, proprietary datasets; non-standard data formats; domain shift between materials and machines; sensor variability; limited model interpretability; the simulation-to-reality gap; cybersecurity and intellectual property constraints; and a lack of closed-loop validation. The assessment noted that purely data-driven methods may not transfer easily when processing conditions and/or the material system change. Thus, a promising approach to build more reliable and transferable Intelligent manufacturing systems is to use hybrid methods that combine artificial intelligence/machine learning with physical knowledge, multimodal sensing, digital twins, uncertainty quantification, and human expertise. In composite manufacturing, AI/ML is becoming an important enabling technology for next-generation approaches such as automated inspection, fault categorization, surrogate modeling, production-process prediction, and parameter optimization. Future work should move beyond laboratory-level prediction accuracy toward validated manufacturing applications with standard datasets, third-party validation, machine validation, physics-informed and multimodal learning, digital-twin integration, and measurable manufacturing-level performance indicators. Coupling AI/ML with physical process models, intelligent sensing, and human-in-the-loop decision-making can enable even more autonomous, high-quality, low-waste, adaptable, and intelligent composite manufacturing systems.