In conventional machining, one of the main tasks is to ensure that the required dimensional accuracy and the desired surface quality of a part or product meet the customer needs. The successful accomplishment of these parameters in milling, turning, milling, drilling, grinding and other conventional machining operations directly depends on the current level of tool wear and cutting edge conditions. One of the proven non-contact methods of tool condition monitoring (TCM) is measuring systems based on image processing technologies that allow assessing the current state of the machined surface and the quantitative indicators of tool wear. This review article discusses image processing for tool monitoring in the conventional machining domain. For the first time, a comprehensive review of the application of image processing techniques for tool condition monitoring in conventional machining processes is provided for both direct and indirect measurement methods. Here we consider both applications of image processing in conventional machining processes, for the analysis of the tool cutting edge and for the control of surface images after machining. It also discusses the predominance, limitations and perspectives on the application of imaging systems as a tool for controlling machining processes. The perspectives and trends in the development of image processing in Industry 4.0, namely artificial intelligence, smart manufacturing, the internet of things and big data, were also elaborated and analysed.

The right choice of polymeric materials plays a vital role in the successful design and manufacture of flexible fluidic systems, as well as heat transfer devices such as pulsating heat pipes. The decision to choose an acceptable polymeric material entails a variety of evaluation criteria because there are numerous competing materials available today, each with its own properties, applications, benefits, and drawbacks. In this study, a comparative hybrid multi-criteria decision-making (MCDM) model is proposed for evaluating suitable polymeric materials for the fabrication of flexible pulsating heat pipes. The decision model consists of fourteen evaluation criteria and twelve alternative materials. For this purpose, three different hybrid MCDM methods were applied to solve the material selection problems (i.e., AHP-GRA, AHP-CoCoSo, and AHP-VIKOR). According to the results obtained, PTFE, PE, and PP showed promising properties. In addition, Spearman’s rank correlation analysis was performed, and the hybrid methods used produced consistent rankings with each other. By applying MCDM methods, it was concluded that PTFE is the most suitable material to be preferred for manufacturing flexible pulsating heat pipes. In addition to this result, PE and PP are among the best alternatives that can be recommended after PTFE. The study supports the use of MCDM techniques to rank material choices and enhance the selection procedure. The research will greatly assist industrial managers and academics involved in the selection process of polymeric materials.

This research study employs a comparative Multi-Criteria Decision-Making (MCDM) approach to select optimal thermoplastic materials for hybrid vehicle battery packs in the automotive industry, addressing the challenges posed by high-temperature environments. Through a detailed evaluation of materials based on criteria such as thermal stability, mechanical strength, chemical resistance, and environmental impact, the research identifies materials that enhance battery efficiency, longevity, and vehicle performance. Utilizing SWARA-ARAS, SWARA-EDAS, and SWARA-TOPSIS methods, the study systematically assesses and ranks various polymers, providing recommendations that prioritize safety, performance, and sustainability. The findings offer valuable insights for manufacturers in making informed material selection decisions, contributing to the advancement of sustainable automotive technologies. This research not only highlights the importance of material selection in the context of hybrid vehicle battery packs but also sets a foundation for future studies to explore emerging materials and decision-making frameworks, aiming to further enhance the efficiency and sustainability of hybrid vehicles.

Aluminum Matrix Composite (AMC) represents an innovative class of materials that is extensively utilized in industries such as automotive, defense, aerospace, structural engineering, sports, and electronics. This study investigates the thrust force, exit burr formation, changes in the micro-tool, and drilled hole diameters during the micro-drilling of an aluminum-polyethylene composite panel (Al–PE). The panel consists of 3501 series aluminum skin materials bonded to a polyethylene (PE) core. Micro-drilling test parameters were designed using Taguchi’s L16 (4² 2³) orthogonal array. Tests were conducted with five control parameters: cutting speed with four levels (10 m/min, 20 m/min, 30 m/min, 40 m/min), feed rate with four levels (0.5 µm/rev, 1 µm/rev, 2 µm/rev, 4 µm/rev), the tool diameter with two levels (0.7 mm, 1 mm), and tool point angle with two levels (100°, 140°) using both AlTiN-coated and uncoated drills. The maximum thrust force (F_z), maximum burr height, and changes in both the drill tool and hole diameters were measured for analysis of variance (ANOVA). The results showed that, in terms of impact on F_z, tool point angle had the highest positive influence (64.54%) on the micro-drill at the entrance of composite (upper aluminum plate). The cutting speed had the highest positive influence (45.32%) on the tool in the core layer (PE core layer). The tool point angle also had the highest positive influence (68.95%) on the micro-drill at the lower layer of the composite (the lower aluminum plate). There was noticeable chip adhesion on the major cutting edge and nose area under micro-drilling conditions with higher thrust forces and burr height. The AlTiN coating had a positive effect on tool wear and hole diameter deviations, but it adversely affected the burr height.