Online monitoring of machining processes is revealed as a critical tool for detecting tool wear, influencing the determination of the remaining useful lifetime of cutting tools. Embracing the ethos of Industry 4.0, the study emphasizes the automatic monitoring of cutting forces, surface roughness, power consumption, tool wear, and tool life, citing their indispensable role in mitigating unfavourable machining conditions such as chatter vibrations, tool breakage, and compromised dimensional accuracy. The paper underscores the pivotal role played by advanced sensor systems in achieving enhanced machining characteristics, characterized by reduced human effort, minimized errors, and streamlined production times. The exploration extends to a comprehensive overview of online detection systems, encompassing sensors and signal processing software tailored for mechanical machining operations. Commencing with an up-to-date literature introduction, the paper systematically navigates through the types of sensors employed in machining, online detection methods, and addresses pertinent challenges while offering insightful suggestions. In a nutshell, the paper summarized its findings and provided future insights, particularly centered on the industry 4.0 theme. It is important to note that this review offers significant assistance to researchers and academics in the industrial sectors.

Lightweight materials are the great demand in the aerospace sector to enhance system performance. The automotive, aerospace sector has utilized the composite materials to strengthen the physical and mechanical qualities of less weight materials and to improve their functionality. In this study, three different base matrix alloy combinations comprise the specimens being examined. Selective laser melting was used to combine boron carbide, graphite, and iron oxide powder (2.5, 5 and 7.5 wt.%) with an aluminium alloy matrix. Use the ASTM B-557M standards, specimens are prepared for testing the hybrid composite including such wear, and scanning electron microscopy. The purpose of the current study is to use Taguchi-based gray relational analysis (TGRA) to improve the wear parameters of aluminium matrix composite. Grey relationship study has shown that the optimal combinations for determining the hybrid composite’s wear rate and coefficient of friction are 40 N load (level 3), 6 m/s sliding speed (level 3), and 1000 m sliding distance (level 1). Hybrid composites are said to have better wear properties and to offer enhanced components for the automotive, marine, and aerospace industries as compared to earlier metal matrix composites.

Metal Matrix Composites (MMCs) are a class of innovative materials that are highly valued for their robustness and lightweight nature. They are used extensively in heavy-duty industries such as sports equipment, automotive, aerospace, and electronics. Even with their exceptional mechanical qualities, MMCs have problems when it comes to machining; these problems include low surface quality, rapid tool wear, and expensive material removal. Traditional cutting fluids present serious biological and environmental hazards. They are frequently employed as coolants and lubricants in machining operations. As a result, it is now essential to switch to sustainable machining methods. Sustainable practices include minimum quantity lubrication, cryogenic cooling, the use of vegetable oil, and the use of coated cutting tools, which are completely biodegradable and environmentally friendly. This research provides a thorough analysis of contemporary sustainable metal matrix composites machining methods. It emphasizes that when compared to traditional wet machining, these environmentally friendly techniques frequently produce better results, such as improved surface quality, longer tool life, lower cutting temperatures, and lower cutting forces. This review’s main goal is to find and promote different sustainable metal matrix composites machining techniques, encouraging both economical and environmentally friendly machining processes.