Milling performance assessment of Ti-6Al-4V under CO<sub>2</sub> cooling utilizing coated AlCrN/TiAlN insert

Ross, Nimel Sworna; Sheeba, Paul T; Jebaraj, M.; Stephen, Hima

doi:10.1080/10426914.2021.2001510

Cited by 27 publications

(6 citation statements)

References 39 publications

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“…The equations to find R 2 , RMSE and MAPE are presented in equations (6)–(8). 27 Where, Ta is the actual experimental value, Tp is the predicted value, Te is the error value and n is the number of trials.…”

Section: Resultsmentioning

confidence: 99%

“…ANN is a powerful AI technique that aids in the development of predictive models for a variety of experimental data sources. 27 The number of layers and nodes used in the building of ANN was determined. ANN normally operates with three layers: i/p, hidden, and o/p.…”

Section: Prediction With Annmentioning

confidence: 99%

“…The equations to find R 2 , RMSE and MAPE are presented in equations ( 6)- (8). 27 Where, Ta is the actual experimental value, Tp is the predicted value, Te is the error value and n is the number of trials. and identifying the components influencing the examined response and their contributions, both ANN and RSM procedures seem compatible.…”

Section: Assessment Of Rsm and Ann Modelsmentioning

confidence: 99%

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Parametric assessment of surface behavior and the impact of heat in micro drilling of fiber laser machined AISI h13

Rubaiee

2022

Proceedings of the Institution of Mechanical Engineers, Part C:

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H13 is a tool steel with good strength to thermal cracking, and resistance to wear, and erosion. It is extensively employed in the manufacture of molds and dies. Because of its hardness, it is challenging to manufacture with traditional methods, and damages the surface during cutting, which lessens the effectiveness of components. Recently, laser cutting has become a possible method for achieving high rates of material removal. The use of laser cutting to manufacture complicated forms is a contemporary development in laser technique. The drilling on 0.44 mm thick H13 by 100 m fiber laser rays is highlighted in this study. Entropy using COPRAS is employed to optimize fiber laser settings for the drilled hole. Response surface analysis is utilized to determine correlation of fiber lasers process parameter and ANOVA validates the model. Further, ANN is used to predict the experimental results. In the current research, the laser’s output power, scanning speed, and duty cycle are all taken into account. The results of the tests revealed that laser power is the most important element affecting the quality of drilled holes. The optimum condition of COPRAS grade offers 4.27 µm of roughness, 89.89 µm of heat impacted zone, 139.595 µm of overcut, and 0.074113 mm of material removal rate. The observed and predicted figures are relatively near to each other, according to the experiments. The outcomes of the confirmation test (PO = 5 W, SOS = 10 mm/s, DC = 25%) demonstrate that optimal conditions provide greater surface precision than initial trails.

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Section: Resultsmentioning

confidence: 99%

Section: Prediction With Annmentioning

confidence: 99%

Section: Assessment Of Rsm and Ann Modelsmentioning

confidence: 99%

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Parametric assessment of surface behavior and the impact of heat in micro drilling of fiber laser machined AISI h13

Rubaiee

2022

Proceedings of the Institution of Mechanical Engineers, Part C:

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“…Modern manufacturing and process monitoring systems have undergone a full transformation, thanks to machine learning (ML). Artificial neural network (ANN) (Ross et al, 2022), hidden Markov model (HMM) (Li & Liu, 2019), support vector machine (SVM) (Lu et al, 2013), and other techniques were specifically used in feature identification of TW monitoring and prediction. A method for using machine vision during cutting to predict the escalating tool flank side wear was presented by (Dutta et al, 2016).…”

Section: Introductionmentioning

confidence: 99%

A novel approach of tool condition monitoring in sustainable machining of Ni alloy with transfer learning models

et al. 2023

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Cutting tool condition is crucial in metal cutting. In-process tool failures significantly influences the surface roughness, power consumption, and process endurance. Industries are interested in supervisory systems that anticipate the health of the tool. A methodology that utilizes the information to predict problems and to avoid failures must be embraced. In recent years, several machine learning-based predictive modelling strategies for estimating tool wear have been emerged. However, due to intricate tool wear mechanisms, doing so with limited datasets confronts difficulties under varying operating conditions. This article proposes the use of transfer learning technology to detect tool wear, especially flank wear under distinct cutting environments (dry, flood, MQL and cryogenic). In this study, the state of the cutting tool was determined using the pre-trained networks like AlexNet, VGG-16, ResNet, MobileNet, and Inception-V3. The best-performing network was recommended for tool condition monitoring, considering the effects of hyperparameters such as batch size, learning rate, solver, and train-test split ratio. In light of this, the recommended methodology may prove to be highly helpful for classifying and suggesting the suitable cutting conditions, especially under limited data situation. The transfer learning model with Inception-V3 is extremely useful for intelligent machining applications.

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“…1,2 This may be accredited to the significant beneficial characteristics that these alloys possess in terms of its excellent strength to weight ratio, capability to retain bulk hardness at extreme machining temperatures, superior corrosion and creep resistant features. 3 However, high strain hardening behavior, extreme chemical affinity, poor thermal conductivity and rapid generation of Built Up Edges (BUE) during machining may be identified as some of the major concerns that severely hamper its machinability. 4 Several machining strategies were adopted by researchers to overcome the machinability related issues of Ti-6Al-4V.…”

Section: Introductionmentioning

confidence: 99%

Tool surface modification methodology to improve the machinability of Ti6Al4V through combination of micro-EDM texturing and AlSi-10Mg soft coating techniques in dry turning

George

Sahu

Dhanish

et al. 2023

Proceedings of the Institution of Mechanical Engineers, Part C:

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Machinability of Ti-6Al-4V, despite its numerous applications, is challenging owing to its poor thermal conductivity, severe chemical affinity and strain hardening characteristics. Although various coating techniques and expensive lubrication strategies have been tried, a significant improvement in tool life characteristics are yet to be achieved. Hence this work aims at suggesting a novel cost-effective and ecofriendly tool treatment technique by exploring the impact of a dual combination of an AlSi-10Mg coating developed by an emerging pre-conditioning technique with the incorporation of micro-textures on tool face to observe significant improvements in tool life and machinability characteristics of the alloy. It was analyzed from the results that while the micro-textures played a vital role in reducing the severe contact stress and machining temperature due to its improved heat transfer by convection, AlSi-10Mg film effectively shielded the cutting edges of the tool by providing a dampening effect along the edges, thereby ensuring improved tool life and enhanced machinability characteristics at the same time. The proposed tool combination was observed to be successful in significantly reducing the flank wear by 35%, 43% and 30% where 75, 253, and 371 µm were observed at 80, 100, and 120 m/min as compared to 117, 449, and 531 µm in the case of uncoated non-textured tool at the same operating conditions. Surface roughness of Ti-6Al-4V also reduced by 25%, 34%, and 37% where 1394, 1168, and 872 nm as compared to plain tool where 1840, 1770, and 1370 nm were recorded. Moreover, average chip curl diameter was observed to be 2.62, 3.36, and 6.49 mm as compared to chips generated by uncoated tools which recorded values of 4.33, 11.9, and 18.87 mm at 80, 100, and 120 m/min respectively.

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Milling performance assessment of Ti-6Al-4V under CO₂ cooling utilizing coated AlCrN/TiAlN insert

Cited by 27 publications

References 39 publications

Parametric assessment of surface behavior and the impact of heat in micro drilling of fiber laser machined AISI h13

Parametric assessment of surface behavior and the impact of heat in micro drilling of fiber laser machined AISI h13

A novel approach of tool condition monitoring in sustainable machining of Ni alloy with transfer learning models

Tool surface modification methodology to improve the machinability of Ti6Al4V through combination of micro-EDM texturing and AlSi-10Mg soft coating techniques in dry turning

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Milling performance assessment of Ti-6Al-4V under CO2 cooling utilizing coated AlCrN/TiAlN insert

Cited by 27 publications

References 39 publications

Parametric assessment of surface behavior and the impact of heat in micro drilling of fiber laser machined AISI h13

Parametric assessment of surface behavior and the impact of heat in micro drilling of fiber laser machined AISI h13

A novel approach of tool condition monitoring in sustainable machining of Ni alloy with transfer learning models

Tool surface modification methodology to improve the machinability of Ti6Al4V through combination of micro-EDM texturing and AlSi-10Mg soft coating techniques in dry turning

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Milling performance assessment of Ti-6Al-4V under CO₂ cooling utilizing coated AlCrN/TiAlN insert