High-Speed Machining of Ti–6Al–4V: RSM-GA based Optimization of Surface Roughness and MRR

Alam, Shah; Tomal, A. N. M. Amanullah; Nayeem, Moddassir Khan

doi:10.1016/j.rineng.2022.100873

Cited by 20 publications

(19 citation statements)

References 28 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Principal Component Analysis (PCA), a statistical tool, transformed this complex dataset The highest roughness (Ra) at the channel's roof could be caused by the curved surface inducing larger stair-step effects, particularly larger overhang features when the roof is at 0 • orientation, as depicted in Figure 3a. Furthermore, loose powder adhesion and the instability of the molten pool in overhanging layers, lacking solid substrate support, and efficient heat dissipation led to irregular solidification [23,25,26]. As the build angle was increased beyond 0 • , the roughness (Ra) decreased abruptly to 67.3 µm at the roof and 54.8 µm at the floor of the channel at 30 • .…”

Section: The Effects Of Process Parameters On Channel Surface Roughne...mentioning

confidence: 99%

“…Without a solid substrate to anchor it, the molten pool on an overhanging layer becomes unstable and its surface tension and fluid dynamics cause irregularities and a rougher finish compared to areas with a solid layer beneath [ 23 , 24 , 25 ]. Furthermore, channel roof overhangs lack this heat dissipation pathway, potentially causing inconsistent solidification and increased roughness [ 23 , 25 , 26 ]. The angle at which the laser strikes the overhang can also affects the melt pool’s behavior as non-optimal laser angles result in uneven melting and solidification, leading to a rougher surface texture [ 27 , 28 ].…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Surface Roughness of Interior Fine Flow Channels in Selective Laser Melted Ti-6Al-4V Alloy Components

Islam,

Hao,

Javaid

et al. 2024

Micromachines

View full text Add to dashboard Cite

A challenge remains in achieving adequate surface roughness of SLM fabricated interior channels, which is crucial for fuel delivery in the space industry. This study investigated the surface roughness of interior fine flow channels (1 mm diameter) embedded in SLM fabricated TC4 alloy space components. A machine learning approach identified layer thickness as a significant factor affecting interior channel surface roughness, with an importance score of 1.184, followed by scan speed and laser power with scores of 0.758 and 0.512, respectively. The roughness resulted from thin layer thickness of 20 µm, predominantly formed through powder adherence, while from thicker layer of 50 µm, the roughness was mainly due to the stair step effect. Slow scan speeds increased melt pools solidification time at roof overhangs, causing molten metal to sag under gravity. Higher laser power increased melt pools temperature and led to dross formation at roof overhangs. Smaller hatch spaces increased roughness due to overlapping of melt tracks, while larger hatch spaces reduced surface roughness but led to decreased part density. The surface roughness was recorded at 34 µm for roof areas and 26.15 µm for floor areas. These findings contribute to potential adoption of TC4 alloy components in the space industry.

show abstract

Section: The Effects Of Process Parameters On Channel Surface Roughne...mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Surface Roughness of Interior Fine Flow Channels in Selective Laser Melted Ti-6Al-4V Alloy Components

Islam,

Hao,

Javaid

et al. 2024

Micromachines

View full text Add to dashboard Cite

show abstract

“…Tool flank face wear is one of the important indicators to evaluate the tool life [12]. According to the literature [13], the dry milling of titanium alloy with an uncoated carbide end mill is studied in this paper. The end mill edge flank face wear and material removal rate are taken as performance indicators.…”

Section: Tool Equipment and Performance Indicators Of The Milling Pro...mentioning

confidence: 99%

The Dynamic Comprehensive Evaluation of the Importance of Cutting Parameters in the Side Milling TC4 Process Using an Integrated End Mill

Zhao,

Wang,

Jin

et al. 2024

Materials

View full text Add to dashboard Cite

In the cutting process, there are many parameters that affect the cutting effect, and the same parameter has different degrees of influence on different performance indicators, which makes it difficult to select key parameters for parameter optimization and parameter combination evaluation while considering multiple performance indicators at the same time. The process of titanium alloy milling with an integrated end mill is studied herein. The values of cutting tool flank face wear and material removal rates are obtained with experimental and analytical methods. Numerical characteristics and causes of the cutting tool flank face wear at different stages are also analyzed. The dynamic, comprehensive evaluation method based on the double incentives model is used to evaluate the dynamic, comprehensive importance of cutting parameters in view of the problem of considering multiple performance indicators and the characteristics of the dynamic change in performance indicators in the cutting process. According to the result of a dynamic, comprehensive evaluation, the cutting parameters with the highest comprehensive importance are selected. Finally, the radar map is used to plot the comprehensive importance of the cutting parameters. The overall comprehensive importance of each cutting parameter is intuitively displayed as well. As a result of the research, the dynamic, comprehensive evaluation method based on the double incentives model has a good application value in the evaluation of tool performance in the cutting process and can quickly select the best tool performance parameter combination; it is established that the most comprehensive parameter is the cutting speed, and the cutting width is the second most important. In turn, the comprehensive importance of the cutting depth is the lowest.

show abstract

“…The optimum machining parameters were estimated for minimizing both the power consumption and surface roughness during turning operation in [103] and it was concluded that feed rate is responsible for the increase in surface roughness, sound level, and the values of power consumption, and feed rate is the most effective parameter during machine operation affecting power consumption and surface roughness. An experimental investigation has been made for obtaining the lowest possible roughness on the workpiece surface during high-speed machining (HSM) cooperatively the highest feasible MRR [104]. The machining variables that have been investigated in experimental research include cutting speed (mm/min), feed rate (mm/min), and depth of cut (µm), and it has been illustrated that lowering the surface roughness by only 0.1 µm will increase the MRR (50.83%).…”

Section: Machining Parameters Optimizationmentioning

confidence: 99%

Recent Trends, Developments, and Emerging Technologies towards Sustainable Intelligent Machining: A Critical Review, Perspectives and Future Directions

Asif¹,

Shen²,

Zhou³

et al. 2023

Sustainability

View full text Add to dashboard Cite

Intelligent manufacturing is considered among the most important elements of the modern industrial revolution, which includes digitalization, networking, and the development of the intelligent manufacturing industry. With the progressive development of modern information technology, particularly the new generation of artificial intelligence (AI) technology, many new opportunities are coming into existence for intelligent machine tool (IMT) development. Intelligent machine tools offer diverse advantages, including learning and optimizing machining processes, error compensation, energy savings, and failure prevention. The paper focuses on the machine tool market in terms of global production, the leading machine tool-producing countries, and the leading countries’ market share in machine tool production. Moreover, the usage of various artificial intelligence techniques in intelligent machining operations is also considered in this comprehensive review, including machining parameter optimization, tool condition monitoring (TCM), and chatter vibration management of intelligent machine tools. Furthermore, future challenges for the machine tool industry are also highlighted.

show abstract

High-Speed Machining of Ti–6Al–4V: RSM-GA based Optimization of Surface Roughness and MRR

Cited by 20 publications

References 28 publications

Surface Roughness of Interior Fine Flow Channels in Selective Laser Melted Ti-6Al-4V Alloy Components

Surface Roughness of Interior Fine Flow Channels in Selective Laser Melted Ti-6Al-4V Alloy Components

The Dynamic Comprehensive Evaluation of the Importance of Cutting Parameters in the Side Milling TC4 Process Using an Integrated End Mill

Recent Trends, Developments, and Emerging Technologies towards Sustainable Intelligent Machining: A Critical Review, Perspectives and Future Directions

Contact Info

Product

Resources

About