Aluminum alloys are widely used in many industries, including aerospace, automotive, civil, and electrical engineering. When compared to pure aluminum, most aluminum alloys have lower electrical and thermal conductivity, corrosion resistance, and weldability, as well as a low density and specific gravity. At the same time, the properties of aluminum alloys vary significantly depending on the group, which has a significant impact on their machinability. This review article is focused on the study of machining characteristics of aluminum alloys, such as machinability, surface integrity, tool wear and tool life, material removal rate (MRR), and chip morphology. The directions of increasing machinability by controlling cutting parameters, cutting environment, such as dry machining, conventional cooling systems, minimum quantity of lubricant (MQL), cryogenic lubrication (CL), with tool geometry, and textured tools, are also considered; tool materials include coating, vibration, thermally, and hybrid assisted machining. The article discusses the main types of machining, namely, turning, milling, drilling, and grinding. It shows ways to increase the machinability of machining on aluminum alloys, as well as the advantages and disadvantages. From the literature, it can be concluded that tool wear when machining aluminum alloys is 30–40% lower than when machining steel alloys due to their higher ductility and lower strength. Surface integrity, affected by the cutting parameters and cutting temperatures — which can reach between 200 and 400 °C — can vary by up to 15% in hardness and 20% in surface roughness. Cutting tool characteristics can enhance surface finish by up to 25% and extend tool life, reducing edge formation by up to 30%. Chip morphology, influenced by factors such as cutting parameters and tool material, can improve tool life by up to 35%. Vibration techniques can reduce thermal effects and improve surface finish by up to 40%, reducing cutting forces by around 30%.

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.