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%.

Depth sensing indentation is a powerful experimental technique for determining mechanical properties of materials. In this work a computational routine was developed based on Oliver-Pharr method for measuring a more precise values of elastic modulus using a Fischerscope H100 depth sensing indentation apparatus, with a Vickers indenter. This computational routine aims also to measure Vickers hardness, as the equipment does not have software for this purpose. From indentation data it was possible to determine initial unloading stiffness, contact depth, projected contact area, reduced modulus, elastic modulus and Vickers hardness of materials. The validity of the routine was verified analyzing two coatings and nine bulk specimens with different elastic-plastic behaviors. It was verified that the elastic moduli determined through the software of the equipment resulted in great discrepancies when low loads were applied. A good estimate of the elastic moduli of the tested materials is given by the developed routine. For several testing loads, the diagonals determined by means of analytical procedure were compared with the same diagonals measured by image analysis. A good estimate of the Vickers hardness of the above-mentioned materials is given by the developed routine using different testing loads

Hardness is a powerful property to evaluate the deformation behavior of materials. It serves as confident quality control for several processes, especially in the heat treatment of metals. With the advent of depth-sensing indentation, this technique embraces the determination of other mechanical properties. As proof, recognized standards are available to guide the evaluation of Young’s modulus using instrumented indentation. However, there are continuous efforts to describe the strength using hardness apparatus. This critical review aims to compile all ways of correlation between hardness and uniaxial strength. This relationship is usually addressed by a single value, called constraint factor, vastly recognized in metals as approximately 3. From a theoretical point of view, this value works well for materials with rigid-plastic behavior, where hardening effects can be discharged. Divergent variations presented herein show difficulties in incorporating the effect of plastic properties on the constraint factor determination. In the same way, the empirical determinations did not consider the differences in hardening exponents, putting in the same statistical analysis diverse microstructures. A specific section discusses the constraint factor for nonmetallic materials. There are critical doubts for determining strength from hardness values in this case. The existence of several approaches to estimate the constraint factor in brittle materials did not assure yet a unique value for the same material, which put in evidence the lack of a robust physical basis to understand the plastic deformation under indentation. Future trends are indicated along with these observations to become practical the recent developments that have allied hardness and strength. The most important aspect is to combine adequately the experimental and simulation approaches, which can be supported by an analysis of residual imprints of hardness and finite element model.

Gas tungsten arc welding (GTAW) technology is widely used in industry and has advantages, including high precision, excellent welding quality, and low equipment cost. However, the inclusion of a large number of process parameters hinders its application on a wider scale. Therefore, there is a need to implement the prediction and optimization models that effectively enhance the process performance of the GTAW process in different applications. In this study, a five-factor five-level central composite design (CCD) matrix was used to conduct GTAW experiments. AISI 1020 steel blank was used as a substrate; UTP AF Ledurit 60 and UTP AF Ledurit 68 were used as the materials of two tubular wires. Further, an artificial neural network (ANN) was used to simulate the GTAW process and then combined with a genetic algorithm (GA) to determine welding parameters that can provide an optimal weld. In welding experiments, five different welding current levels, welding speed, distance to the nozzle, angle of movement, and frequency of the wire feed pulses were used. Using GA, optimal welding parameters were determined: welding current = 222 A, welding speed = 25 cm/min, nozzle deflection distance = 8 mm, travel angle = 25°, wire feed pulse frequency = 8 Hz. The determination coefficient (R²) and RMSE value of all response parameters are satisfactory, and the R² of all the data remained higher than 0.65.