Effect of Feed Rate On Difficult to Cut Metals on Surface Roughness and Tool Wear Using Surface Treated and Untreated Tools

Thamizhmanii, S.; Yuvaraj, C.; Senthilkumar, J.S.; Arun, Ilangovan; Sulaiman,

doi:10.1016/j.promfg.2019.02.032

Cited by 7 publications

(5 citation statements)

References 10 publications

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“…A greater depth of cut may benefit machining by allowing for a faster material removal rate. However, if the contact condition of cutting tool is not properly controlled, it is more likely to break due to the greater force and bending effect [22,23].…”

Section: Resultsmentioning

confidence: 99%

Optimization and wear characteristics of TiAlN coated carbide when machining hardened high thermal conductivity (HTCS-150) die steel.

Bakar¹,

Azhar²,

Wahab³

et al. 2021

Surf. Topogr.: Metrol. Prop.

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High Thermal Conductivity Steel-150 (HTC-150) is a specific steel designed for use in the hot stamping process as a stamping die. HTCS-150 die steel was difficult to machine due to its high strength, hardened state, and high thermal conductivity characteristics, which necessitated parameter control for a fine surface finish and maximum tool life. The characteristics of tool wear when machining HTCS-150 hardened steel (52 HRC) with a ball nose end mill TiAlN coated carbide insert is presented in this study. Cutting speed, feed rate, and axial depth of cut have all been varied in machining trials. Response surface methodology experimental design was used to create a parametric optimisation model. The results indicate that the model develops an accurate prediction, with comparisons between measured and expected results suggesting that the model operates within the 90% prediction interval with an error of less than 10%. The lowest tool wear was achieved at 130 m min−1 cutting speed, 0.4 mm/tooth feed rate, and 0.1 mm axial depth of cut, according to the optimisation results. The most influenced cutting parameters were found to be feed rate and depth of cut, followed by cutting speed. The wear surface texture analysis revealed coating delamination, adhesion, built-up edge formation, and tool edge chipping. The findings of this experimental study can be used to machine the HTCS-150 for the longest possible tool life while maintaining a fine surface finish.

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Section: Resultsmentioning

confidence: 99%

Optimization and wear characteristics of TiAlN coated carbide when machining hardened high thermal conductivity (HTCS-150) die steel.

Bakar¹,

Azhar²,

Wahab³

et al. 2021

Surf. Topogr.: Metrol. Prop.

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“…The static cutting variables generally remain constant throughout a typical machining operation but several studies have shown their significant role in tool wear dynamics. [1][2][3] Despite the options in information usage, TCM is not a trivial task due to the challenge posed by continuous tool-work interaction, making the on-line wear determination task onerous. Artificial intelligence techniques, SSL, the representation of the structure of unlabeled data is learned through a pretext task, essentially turning an unsupervised learning problem into a supervised one.…”

Section: Background and Literature Reviewmentioning

confidence: 99%

“…The information used in a typical wear monitoring task is derived from two sources; the dynamic monitoring sensors signals and the static cutting parameters, such as cutting speed, feed, depth of cut, among others. The static cutting variables generally remain constant throughout a typical machining operation but several studies have shown their significant role in tool wear dynamics 1‐3 . Despite the options in information usage, TCM is not a trivial task due to the challenge posed by continuous tool‐work interaction, making the on‐line wear determination task onerous.…”

Section: Introductionmentioning

confidence: 99%

Generative and self‐supervised ensemble modeling for multivariate tool wear monitoring

Joanes Agung',

James,

Samuel

et al. 2023

Engineering Reports

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Development of an effective tool wear monitor requires maximum utilization of information from associated data, especially in machine learning based modeling. However, vastly varied annotated training data is required, which is not only expensive but impractical to obtain. In the present work, a contiguous approach of artificial data generation followed up by self‐supervised pre‐training before supervised model fine tuning and final stacked generalized ensembling, has been adopted to develop an effective tool wear monitor in a low data regime. Cross‐validated results of proposed methodology adoption in tool wear prediction on an experimental data set of few labeled samples attained an averaged MAE of 0.035, RMSE of 0.045 and MAPE of on the best case ensemble, which was comparatively superior to a purely supervised‐only trained deep model on the same data set, with an overall accuracy enhancement of over . The proposed approach provides an effective experimental data augmentation technique while simultaneously minimizing aleatoric uncertainty and allowing for utilization of information from often ignored static cutting parameters.

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“…Jika hanya melihat satu faktor saja, yaitu kecepatan spindle, maka seharusnya dengan kecepatan spindle tinggi akan menghasilkan kekasaran permukaan yang rendah. Dari hasil ANOVA diketahui kecepatan spindle rendah tersebut hanya memberikan persen kontribusi terhadap kekasaran permukaan sebesar 16 Berdasarkan hasil tersebut diperoleh data bahwa eigenvalue untuk Principal Component (PC) 1 adalah 1,4004 dan untuk Principal Component (PC) 2 adalah 0,5996. Menurut dasar teori yang disebutkan di Bab 2.…”

Section: Responunclassified

“…Penggunaan Penelitian Thamizhmanni, et. al [16] terhadap pemesinan turning untuk Titanium dan Innconel menggunakan CBN cutting tool menemukan hasil bahwa diantara tiga faktor (kecepatan pemakanan, kecepatan spindle dan kedalaman makan) yang diamati ternyata kecepatan makan berpengaruh signifikan untuk nilai optimal keausan pahat maupun kekasaran benda kerja.…”

Section: Responunclassified

Optimasi nilai keausan pahat dan kekasaran permukaan benda kerja terhadap parameter pemesinan milling dengan benda kerja magnesium menggunakan kombinasi metode taguchi dan grey relational analysis

Dwiastanto

Burhanuddin

Ibrahim

2020

Turbo

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The Taguchi method is widely used to determine the optimal quality of a process with a single response. While for multi-response cases, Grey Relational Analysis (GRA) and Principal Component Analysis (PCA) are widely used. With multi-phone GRA, it can be simplified into one single response. Tool wear and surface roughness of the workpiece in the milling machining process is affected by the diameter of the tool, depth of feed, spindle speed, and feeding speed. This study examines the response of tool wear and work surface roughness with three levels and four factors. For tool diameters, using 6.8 mm and 10 mm. The depth of feed used 1; 1,5 mm and 2 mm. Spindle speeds at 910, 1280, and 1700 rpm. While the feeding speed is 75 mm/minute, 145 mm/minute, and 220 mm/minute. Optimal results obtained in conditions of 6 mm tool diameter with 1,5 mm depth of feeding, spindle speed of 1280 rpm, and feeding speed of 75 mm/minute. The optimal response obtained is 0,059 mm for tool wear and 0,364 µm for work surface roughness. The significant factor influencing is the feeding speed which influences 76,14 %.

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Effect of Feed Rate On Difficult to Cut Metals on Surface Roughness and Tool Wear Using Surface Treated and Untreated Tools

Cited by 7 publications

References 10 publications

Optimization and wear characteristics of TiAlN coated carbide when machining hardened high thermal conductivity (HTCS-150) die steel.

Optimization and wear characteristics of TiAlN coated carbide when machining hardened high thermal conductivity (HTCS-150) die steel.

Generative and self‐supervised ensemble modeling for multivariate tool wear monitoring

Optimasi nilai keausan pahat dan kekasaran permukaan benda kerja terhadap parameter pemesinan milling dengan benda kerja magnesium menggunakan kombinasi metode taguchi dan grey relational analysis

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