A theoretical and experimental investigation of thrust and torque in vibration microdrilling

Wang, L.-P.; Wang, J.-S.; Ye, P.-Q.; Wang, L.-J.

doi:10.1243/0954405011519448

Cited by 12 publications

(7 citation statements)

References 8 publications

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“…Zhang et al [107] noticed the effect of vibrations and developed a model based on the mechanics of vibrations and the continuous distribution of thrust and torque along the cutting edge and the chisel edge of the twist drill. Wang et al [108,109] concluded that vibration drilling is different from conventional drilling and presented a method, which involves the development of a dynamic uncut chip thickness for each cutting element at the cutting edge and chisel edge. The model described by the authors the dynamic cutting process, where the mean thrust and torque increased as feed increases under constant vibration.…”

Section: Figure 17 Cutting Forces During Drillingmentioning

confidence: 99%

Review on Investigations Carried out on Burr Formation in Drilling During 1975 To 2020

Sreenivasulu¹,

SrinivasaRao

2019

Technological Engineering

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Burr formation during machining process is a vital role in the assembly lines, even though it is a non value added process but also care should be taken while machining due to non avoiding output generated at the end of material removal process. At present almost all manufacturing sectors faces lot of problems due to these issues and invest more money towards deburring still advanced manufacturing methods available. So, complete burr removal is not possible and only thing is reducing utmost by applying better optimizing techniques, to develop good mechanization methods, selecting optimum process parameters and their conditions. The aim this paper deals about research methods implemented by earlier authors on burr formation especially in drilling. The reason why the present authors selected the drilling is number of automotive and aircraft engineers struggling during structural building works because of these burrs wherever precise measurement needed. In this connection, the authors concentrate their study on previous researcher works related to investigations on experimentation, developing new theoretical mechanisms to minimize burrs, adapt a new technologies available to modify drill bit geometries such that improvement in the minimization of burrs. Finally found that research contributions by changing their drill bit geometry and cutting process parameters have been focused on utilizing the methodologies, changing time to time. In analyzing the performance characteristics with that of input process parameters, several mathematical and empirical models were developed by many researchers so far in their works. Efforts have been made in the direction of optimization of process parameters in drilling for minimizing burr size.

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Section: Figure 17 Cutting Forces During Drillingmentioning

confidence: 99%

Review on Investigations Carried out on Burr Formation in Drilling During 1975 To 2020

Sreenivasulu¹,

SrinivasaRao

2019

Technological Engineering

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“…(Affecting forces to the cutting edge) [24] delme işlemi esnasında, matkap ağız bölgesini esas, ikincil kesme kenarı ve matkap uç bölgesi şeklinde üç bölge olarak belirterek, talaş oluşumu ve matkabın ağızlaması ile ilgili, Zhang ve ark., [25], matkabın kesme kenarı boyunca, ilerleme kuvveti, moment ve titreşim mekaniği ile ilgili, Wang ve ark., delme esnasında oluşan talaş kalınlığına bağlı olarak dinamik kesilmemiş talaş kalınlığı arasındaki ilişkiyi belirten modeller geliştirmişlerdir. Sabit titreşim parametrelerinin yanı sıra ilerlemenin artması ile moment ve ilerleme kuvvetleri artmaktadır [26][27]. Coz ve ark [28]., Ti6Al4V alaşımının MQL (minimal quantity of lubricant) ile delinmesinde kesme hızının artması ile sıcaklık ve moment değerlerinde artış olduğunu ve bu değerlerin Zeilmann and Weingaertner [29]' in yaptığı benzer çalışmaya göre daha yüksek olduğunu belirtmişlerdir.…”

Section: Kesme Kuvveti Ve Moment (Cutting Force and Moment)unclassified

Delik delme prosesi: bir araştırma

Bayraktar¹,

Sıyambaş²,

Turgut³

2017

SAÜ Fen Bilimleri Enstitüsü Dergisi

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Delik delme işlemi, talaşlı imalat işlemleri arasında önemli bir yere sahiptir. Delme işlemi esnasında, aşırı yüzey pürüzlülüğü, çapak oluşumu, dairesellik, eksenel kaçıklık ve hızlı takım aşınması gibi istenmeyen sonuçlarla karşılaşılmaktadır. Bu sonuçlar, ürün kalitesinin ve üretim maliyetlerinin belirlenmesinde önemli bir etkiye sahiptir. Buna göre, makine parçalarının birbirleri arasında hassas çalışabilmeleri ve üretimde verimliliğin arttırılabilmesi için istenmeyen bu sonuçların en aza indirgenmesi gerekmektedir. Kesme esnasında oluşan ilerleme kuvveti, moment, takım aşınması ve yüzey pürüzlülüğü, optimum kesme parametrelerinin belirlenmesi ile kontrol edilebilmektedir. Böylelikle, optimum kesme parametreleri belirlenerek, üretimde verimliliğin ve hassasiyetin arttırılması sağlanmaktadır. Bu çalışmada, farklı malzemeler üzerinde delik delme işlemi ile ilgili yapılan çalışmalar araştırılarak elde edilen sonuçlar bir arada sunulmuştur.

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“…According to the research of Amini et al, 13 the vibration and drilling force signals have more influence than other processing parameters and signals. Wang et al 14 found that multisignals and multifeatures can better correlate the predicted value with the actual value by analyzing the characteristic values of thrust force and vibration in microdrilling research. Jain and Lad 15 proposed a new integrated tool condition monitoring system to help improve system performance and diagnostic reliability and predict tool wear better.…”

Section: Introductionmentioning

confidence: 99%

Prediction of tool wear in CFRP drilling based on neural network with multicharacteristics and multisignal sources

Guo-qiang

Tang

2021

Composites and Advanced Materials

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Carbon fiber-reinforced polymer (CFRP) drilling is a typical process in the aircraft industry. Because the components of CFRP are different and uneven, it is difficult to extract tool wear characteristics from the machining signals, which are composed of the processing characteristics of various materials and the tool state characteristics. The aim of this work is to present a new comprehensive approach based on multicharacteristics and multisignal sources to predict the tool wear state during CFRP drilling through a combination of a backpropagation (BP) artificial neural network (ANN) model and an efficient automatic system depending on the sliding window algorithm. It was verified that the peak factor and Kurtosis coefficient of different signals and the energy value of the d5 layer of the thrust force signal and the d3 layer of the vibration signal after wavelet decomposition were related to tool wear. Among them, the energy value of the d3 layer of the vibration signal was selected as the wear indicator and was able to describe the state of the tool during the CFRP drilling process regardless of the drilling conditions and individual tool differences. A confirmatory drilling experiment using 6-mm-diameter polycrystalline diamond twist drilling under different processing parameters was conducted to verify the ANN model based on multicharacteristics and multisignal sources. A lower feed speed and a higher cutting speed were both highly correlated with the VB value of flank wear. Drill wear accelerated because of the occurrence of adhesive wear when the number of drilled holes reached around 90. The accuracy of the neural network model is 80–87% when using the value of only one characteristic but clearly increases based on multicharacteristics and multisignal sources in real time, indicating that the BP ANN model has higher accuracy in predicting the tool state in CFRP drilling through the sensor signal fusion method.

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A theoretical and experimental investigation of thrust and torque in vibration microdrilling

Cited by 12 publications

References 8 publications

Review on Investigations Carried out on Burr Formation in Drilling During 1975 To 2020

Review on Investigations Carried out on Burr Formation in Drilling During 1975 To 2020

Delik delme prosesi: bir araştırma

Prediction of tool wear in CFRP drilling based on neural network with multicharacteristics and multisignal sources

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