Machining performance of TiSiN(Ag) coated tools during dry turning of TiAl6V4 aerospace alloy

Cavaleiro, D.; Figueiredo, Daniel; Moura, Cristina; Cavaleiro, A.; Carvalho, S.; Fernandes, Filipe

doi:10.1016/j.ceramint.2021.01.021

Cited by 23 publications

(5 citation statements)

References 31 publications

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“…Cavaleiro et. al., argued that to increase the tool life in dry machining process tools inserts needs to be coated with coatings having self-lubricating ability [21]. Iqbal et.…”

Section: Introductionmentioning

confidence: 99%

Coupling Taguchi Experimental Designs with Deep Adaptive Learning Enhanced Artificial Intelligence Process Models: A Novel Case in Promising Experimental Cost Savings Possibilities in Manufacturing Process Development

Zubair,

Arafat,

Khan

et al. 2024

Preprint

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The Aluminum alloy AA7075 workpiece material is observed under dry finishing turning operation. This work is an investigation reporting promising potential of deep adaptive learning enhanced artificial intelligence process models for L18 (6133) Taguchi orthogonal array experiments and major cost saving potential in machining process optimization. Six different tool inserts are used as categorical parameter along with three continuous operational parameters i.e., depth of cut, feed rate and cutting speed to study their effect on output. Workpiece surface roughness and tool life are considered as output parameters. The data obtained from special L18 (6133) orthogonal array experimental design in dry finishing turning process is used to train AI models. Multi-layer perceptron based artificial neural networks (MLP-ANNs), support vector machines (SVMs) and decision trees are compared for better understanding ability of low resolution experimental design. Seven model evaluation criteria and external validation is used for final model selection. The AI models can be used with low resolution experimental design to obtain causal relationships between input and output variables. The best performing operational input ranges for surface roughness and tool life are identified keeping workpiece surface roughness as primary criteria of range selection in aerospace industry. TiN and TiCN are top two tool insets for obtaining low surface finish with maximum tool life under specified conditions. AI-response surfaces indicate different tool life behavior for alloy based coated tool inserts and non-alloy based coated tool inserts. The AI-Taguchi hybrid modelling and optimization technique helped in achieving 26% of experimental savings (obtaining causal relation with 26% less number of experiments) compared to conventional Taguchi design combined with two screened factors three levels full factorial experimentation.

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“…Cavaleiro et. al., argued that to increase the tool life in dry machining process tools inserts needs to be coated with coatings having self-lubricating ability [21]. Iqbal et.…”

Section: Introductionmentioning

confidence: 99%

Coupling Taguchi Experimental Designs with Deep Adaptive Learning Enhanced Artificial Intelligence Process Models: A Novel Case in Promising Experimental Cost Savings Possibilities in Manufacturing Process Development

Zubair,

Arafat,

Khan

et al. 2024

Preprint

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“…This is especially true with titanium nitride-based superhard coatings [ 12 ], in which silver is present in the polycrystalline TiN matrix as well-separated Ag nanoparticles [ 13 ], and a control of its release is crucial to obtain a constant and long-lasting lubrication. TiN:Ag coatings, with the inclusion of SiN in the matrix [ 14 ] and in multilayered architectures, have shown very good performance in terms of lubrication and wear protection [ 15 , 16 ]. To improve the performance of these coatings, which strongly depends upon their structure, and thus, on the Ag release rate, a deeper understanding of the Ag lubricating and protective action during the cutting/machining process is required.…”

Section: Introductionmentioning

confidence: 99%

Molecular Dynamics Simulation of Ti Metal Cutting Using a TiN:Ag Self-Lubricating Coated Tool

Lenzi

Marques

2023

Materials

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Silver-ceramic nanocomposite coatings, such as TiN:Ag, are among the most interesting solutions to improve the machining and cutting process of hard-to-cut Ti alloys, since they combine the TiN matrix hardness with the lubricating and protective action of Ag nanoparticles. Therefore, it is important to understand how, when present, Ag distributes at the tool-workpiece interface and how it affects the tribolayer formation and the tool wear. Molecular dynamics simulation results, obtained using a MEAM-based force field, are presented here for the cutting process of a Ti workpiece with a TiN tool, with and without the presence of Ag at the interface, for different cutting speeds. Ag is shown to form a thin protective layer at the workpiece-tool interface that prevents a direct contact between the parts and greatly reduces the tool degradation. Our simulations confirm the importance of Ag in self-lubricating nanocomposite coatings to realize the machining of otherwise hard-to-cut materials.

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“…For engineering ceramic materials, because they are mostly used in high-tech fields, such as insulation tiles, high-precision optical devices required for aviation technology, higher requirements are put forward for machining accuracy, surface quality and surface integrity. The traditional machining method can not fully meet the processing requirements, such as: using the traditional turning process to process ceramic materials, due to the high hardness and brittleness of the ceramic material itself, the machining process is easy to lead to workpiece damage and poor surface quality; If the traditional grinding process is used to process ceramic materials, although it will not cause serious damage to the workpiece, there are many micro cracks,the surface quality is general, and the processing efficiency is too low [10][11][12][13]. Until the 1970s, the laser-assisted cutting process was proposed to solve the machining problems of difficult-to-machine materials [14,15].…”

Section: Introductionmentioning

confidence: 99%

Analysis of surface quality and optimization of process parameters in laser-assisted cutting of SiC ceramics

Dai¹,

zhao²,

Cao³

et al. 2022

Surf. Topogr.: Metrol. Prop.

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In order to improve the surface quality of laser-assisted turning silicon carbide (SiC) ceramics, the effects of laser power, rotational speed, cutting depth and feed speed on surface quality are investigated. Taking the surface roughness as the characterization value, the reasonable selection range of each factor is determined by the numerical simulation results of temperature field and the single factor experiment results; orthogonal experiments and range analysis are performed on laser power, rotational speed, cutting depth and feed speed, the primary and secondary order of influence on surface roughness and the optimal combination of process parameters are obtained. The surface topography of the cutting area was observed by 3D digital microscope and the surface roughness is calculated. Taking surface roughness as the evaluation index, the reasonable selection range of each factor is obtained through temperature field simulation and single factor experiment results analysis as follows: laser power 230~245 W, rotational speed 1500~1650 r/min, cutting depth 0.1~0.2 mm, and feed speed 2~4 mm/min. Through orthogonal experiment and range analysis, the order of the significant degree of the influence of various process parameters on surface roughness and the optimal combination of process parameters are obtained. The order of the significant degree of the influence of various process parameters on surface roughness Ra is as follows: laser power (P) > cutting depth (ap) > feed speed (f) > rotational speed (n); the optimal combination of process parameters is as follows: laser power 235 W, rotational speed 1620 r/min, cutting depth 0.1 mm and feed speed 2 mm/min. Compared with the surface quality of original workpiece, the surface of the SiC workpiece processed under the optimal combination of process parameters has no cracks and damage marks, and the surface roughness is reduced to 0.293μm, which significantly improves the surface quality of the SiC workpiece.

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Machining performance of TiSiN(Ag) coated tools during dry turning of TiAl6V4 aerospace alloy

Cited by 23 publications

References 31 publications

Coupling Taguchi Experimental Designs with Deep Adaptive Learning Enhanced Artificial Intelligence Process Models: A Novel Case in Promising Experimental Cost Savings Possibilities in Manufacturing Process Development

Coupling Taguchi Experimental Designs with Deep Adaptive Learning Enhanced Artificial Intelligence Process Models: A Novel Case in Promising Experimental Cost Savings Possibilities in Manufacturing Process Development

Molecular Dynamics Simulation of Ti Metal Cutting Using a TiN:Ag Self-Lubricating Coated Tool

Analysis of surface quality and optimization of process parameters in laser-assisted cutting of SiC ceramics

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