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Titanium alloys are broadly used in the medical and aerospace sectors. However, they are categorized within the hard-to-machine alloys ascribed to their higher chemical reactivity and lower thermal conductivity. This aim of this research was to study the impact of the dry-end-milling process with an uncoated tool on the produced surface roughness of Ti6Al4V alloy. This research aims to study the impact of the dry-end milling process with an uncoated tool on the produced surface roughness of Ti6Al4V alloy. Also, it seeks to develop a new hybrid neural model based on the training back propagation neural network (BPNN) with swarm optimization-gravitation search hybrid algorithms (PSO-GSA). Full-factorial design of the experiment with L27 orthogonal array was applied, and three end-milling parameters (cutting speed, feed rate, and axial depth of cut) with three levels were selected (50, 77.5, and 105 m/min; 0.1, 0.15, and 0.2 mm/tooth; and 1, 1.5, and 2 mm) and investigated to show their influence on the obtained surface roughness. The results revealed that the surface roughness is significantly affected by the feed rate followed by the axial depth. A 0.49 µm was produced as a minimum surface roughness at the optimized parameters of 105 m/min, 0.1 mm/tooth, and 1 mm. On the other hand, a neural network having a single hidden layer with 1–20 hidden neurons, 3 input neurons, and 1 output neuron was trained with both PSO and PSO–GSA algorithms. The hybrid BPNN–PSO–GSA model showed its superiority over the BPNN–PSO model in terms of the minimum mean square error (MSE) that was calculated during the testing stage. The best BPNN–PSO–GSA hybrid model was the 3–18–1 structure, which reached the best testing MSE of 3.8 × 10−11 against 2.42 × 10−5 of the 3–8–1 BPNN–PSO hybrid model.

This paper presents the experimental investigations to identify the effect of different machining parameters on the microstructure and microhardness alterations beneath the machined surface of Inconel 718. The high-speed milling experiments were conducted using PVD multi-coated ball nose carbide inserts, under cryogenic CO2 condition at controlled cutting speed of 120-140 m/min, feed rate of 0.15-0.25 mm/tooth, depth of cut of 0.3-0.7 mm and a fixed width of cut at 0.4 mm. The experimental results discovered that the formation of plastic deformation could only be observed when cutting with the highest value of feed rate and depth of cut, deepened until 8.87 µm from the machined surface. At the same time, those factors also significantly increased machined surface hardness, made the workpiece harder than its bulk hardness. However, the hardness values were found reduced towards the sub-surface and back to its bulk hardness approximately 250 - 300 µm from the top surface. This study suggests that the finishing milling process of Inconel 718 under cryogenic CO2 condition and at predetermined parameters can be performed at a depth of cut less than 0.3 mm as its machined sub-surface microhardness and microstructure alteration were observed to be inconsequential. Thus, a more economical machining process can be performed. [ABSTRACT FROM AUTHOR]

The automotive industry is looking to adopt environmentally-friendly machining processes for automotive components. This study intends to investigate the machining parameters affecting the machinability of hypereutectic Al-Si alloys in the context of surface roughness and tool life via a DLC coated face milling cutter inserted under dry cutting conditions. The machining parameters used in this study were cutting speeds of 250 m/min and 350 m/min, feed rates of 0.02 mm/tooth and 0.04 mm/tooth, and a constant depth of cut of 0.3 mm. The orthogonal full factorial (23) method was used for the experimental trials. A commercial software called Minitab 17 was used to generate the analysis of variance (ANOVA) and the mathematical prediction model for each machining response. The experimental results confirmed that an excellent surface finish was achieved with a value of as low as 0.140 µm, while the highest value for tool life of 105.47 minute was realized with face milled aluminium alloy A390. From the analyses, it was confirmed that the feed rate is the most significant machining factor affecting surface roughness, while in the case of tool life; cutting speed is the most influential machining factor. The main effect plot showed that the optimum cutting condition for realizing low surface roughness and longer tool life is at 250 m/min, a feed rate of 0.02 mm/tooth, and radial depth of cut 12.5 mm. The prediction model for surface roughness and tool life was developed and reported low percentage errors. [ABSTRACT FROM AUTHOR]