In this paper, an experimental investigation into the machinability of AISI 316 alloy during finishing end milling operation under different cooling conditions and with varying process parameters is presented. Three environmental-friendly cooling strategies were utilized, namely, dry, minimal quantity lubrication (MQL) and MQL with nanoparticles (Al₂O₃), and the variable process parameters were cutting speed and feed rate. Power consumption and surface quality were utilized as the machining responses to characterize the process performance. Surface quality was examined by evaluating the final surface roughness and surface integrity of the machined surface. The results revealed a reduction in power consumption when MQL and MQL + Al₂O₃ strategies were applied compared to the dry case by averages of 4.7% and 8.6%, respectively. Besides, a considerable reduction in the surface roughness was noticed with average values of 40% and 44% for MQL and MQL + Al₂O₃ strategies, respectively, when compared to the dry condition. At the same time, the reduction in generated surface roughness obtained by using MQL + Al₂O₃ condition was marginal (5.9%) compared with using MQL condition. Moreover, the results showed that the improvement obtained in the surface quality when using MQL and MQL + Al₂O₃ coolants increased at higher cutting speed and feed rate, and thus, higher productivity can be achieved without deteriorating final surface quality, compared to dry conditions. From scanning electron microscope (SEM) analysis, debris, furrows, plastic deformation irregular friction marks, and bores were found in the surface texture when machining under dry conditions. A slight smoother surface with a nano-polishing effect was found in the case of MQL + Al₂O₃ compared to the MQL and dry cooling strategies. This proves the effectiveness of lubricant with nanoparticles in reducing the friction and thermal damages on the machined surface as the friction marks were still observed when machining with MQL comparable with the case of MQL + Al₂O₃.

This paper reports a fundamental investigation consisting of systematic trials into the response of Ti6Al4V alloy to high-speed machining using carbide inserts. It is a useful extension to work previously published, and aims at assessing the impact of the process parameters, depth of cut, cutting speed and feed rate in addition to cutting length, and their interrelations, on observed crater and flank wear and roughness of the machined surface. The results showed that abrasion was the most important flank wear mechanism at high speed. It also showed that increased cutting length accelerated crater wear more than exhibited flank wear and had considerable effect on surface roughness. In particular, crater wear increased by over 150% (on average), and flank wear increased by 40% (on average) when increasing cutting length from 40 to 120 mm. However, cutting the same length increased surface roughness by 50%, which helps explain how progression of tool wear leads to deteriorated surface quality. ANOVA was used to perform statistical analyses of the measured data and revealed that cutting length and depth of cut had the greatest effect on both crater and flank wear of the cutting tool. These results confirm that high-speed machining of Ti6Al4V alloy is a reliable process, with cutting speed identified as having a relatively small influence on the tool wear and resultant roughness of the machined surface relative to other parameters.

This article presents the results of an experimental investigation into the effect of process parameters in the precision hard turning of Ti-6Al-4V on chip morphology at both macro and micro levels. It also reports on the control of chip generation to improve chip evacuation and breakability at the macro level by varying the process parameters, namely, feed rate, cutting speed and depth of cut during turning tests. A scanning electron microscope (SEM) was used to examine the chips produced for a better understanding of chip curling mechanisms at the micro level. Surface roughness of the machined specimens was measured to assess the effect of chip evacuation on obtainable surface quality. From the results, it was found that the interaction of process parameters has a significant effect on the control of chip formation. In particular, the interaction of higher cutting speeds and greater depths of cut produced chip entanglement with the workpiece for all values of feed rates. Using relatively higher feed rates with a low depth of cut showed good results for chip breaking when machining at higher cutting speeds. Different chip curling mechanisms were identified from the SEM results. Chip side-curl formation showed different segmentation patterns with an approximately uniform chip thickness along the chip width, while chip up-curl occurred due to variations in chip thickness. Finally, it was found that the tangling of the chip with the workpiece has a significant effect on the final surface quality.

This article presents the results of an experimental investigation into the machinability of Ti6Al4V alloy during hard turning, including both conventional and high-speed machining, using polycrystalline diamond (PCD) inserts. A central composite design of experiment procedure was followed to examine the effects of variable process parameters; feed rate, cutting speed and depth of cut (each at five levels) and their interaction effects on surface roughness and cutting temperature as process responses. The results revealed that cutting temperature increased with increasing cutting speed and decreasing feed rate in both conventional and high-speed machining. It was found that high-speed machining showed an average increase in cutting temperature of 65% compared with conventional machining. Nevertheless, high-speed machining showed better performance in terms of lower surface roughness despite using higher feed rates compared to conventional machining. High-speed machining of Ti6Al4V showed an improvement in surface roughness of 11% compared with conventional machining, with a 207% increase in metal removal rate (MRR) which offered the opportunity to increase productivity. Finally, an inverse relationship was verified between generated cutting temperature and surface roughness. This was attributed mainly to the high cutting temperature generated, softening, and decreasing strength of the material in the vicinity of the cutting zone which in turn enabled smoother machining and reduced surface roughness.

This paper explores the impact of various milling strategies, including up-milling, down-milling, and hybrid approaches, on the surface roughness of AISI P20 mold steel. The study is methodically divided into three stages to comprehensively understand the effects of these strategies. The first stage involves milling single slots with varying cutting parameters to establish baseline effects. The second stage examines the effects of consistent milling strategies (up-up and down-down) on surface quality. The third stage probes into hybrid strategies (up-down and down-up) to assess their effectiveness. Central to this investigation is not only the type of milling strategy but also how cutting speed and feed rate influence the resultant surface roughness. Our findings indicate that up-milling generally leads to a 22% increase in surface roughness compared to down-milling. This trend is visually verified by surface texture analyses. When comparing consistent strategies, up-up milling tends to produce rougher surfaces than down-down milling by approximately 25%, characterized by distinctive scratches and feed mark overlays. Remarkably, while the hybrid milling strategies do not exhibit significant differences in surface roughness, variations in cutting speed and feed rate play a crucial role. Specifically, at lower speeds, hybrid milling achieves smoother surfaces than the identical double milling mode, while at a cutting speed of 100 m/min, the double mode demonstrates a notable decrease in roughness. Additionally, this study introduces a color mapping simulation for machined pockets, validated by experimental results, to predict surface roughness based on the strategic history of milling, thereby offering valuable insights for optimizing milling processes.