Parameter optimization on rubberized fly ash geopolymer in milling process in minimizing tool wear

2019 ◽  
Author(s):  
Tan Chun Yu ◽  
M. Fathullah ◽  
M. M. A. Abdullah ◽  
Z. Shayfull ◽  
Faheem Tahir
2015 ◽  
Vol 667 ◽  
pp. 231-236 ◽  
Author(s):  
Xiao Fan Yang ◽  
You Sheng Li ◽  
Guo Hong Yan ◽  
Ju Dong Liu ◽  
Dong Min Yu

Carbon fiber-reinforced plastics (CFRP) are typical difficult-to-machine materials, which is easy to produce many defects such as burrs, dilacerations, layering in milling process. And selecting the appropriate cutting tool has become the key to machining CFRP with high quality and efficiency. In the paper, the machining principle of milling CFRP with new type end mill was analyzed. The diamond coating of general right-hand end mill, cross-flute router and fine-cross-nick router were used to cutting CFRP under the same cutting condition. Through the comparative analysis of the workpiece’s surface quality and tool wear, it concluded that: compared with right-hand diamond coated end mill, cross-flute diamond coated router or fine-cross-nick diamond coated router could effectively suppress the appearance of burrs and dilacerations; abnormal coating peeling appeared in the flank face of right-hand diamond coated end mill, forming the boundary wear, which accelerated wear failure; the flank wear of diamond coated cross-flute router and fine-cross-nick router were both abrasive wear. Due to having more cutting edge than cross-flute router in cutting process, the flank wear of fine-cross-nick router was slower, and the tool life was longer. So it was more suitable for cutting CFRP.


2015 ◽  
Vol 105 (11-12) ◽  
pp. 805-811
Author(s):  
E. Uhlmann ◽  
D. Oberschmidt ◽  
A. Löwenstein ◽  
M. Polte ◽  
I. Winker

Die Prozesssicherheit beim Mikrofräsen lässt sich mit einer gezielten Schneidkantenverrundung erheblich steigern. Dabei werden durch verschiedene Präparationstechnologien unterschiedliche Geometrien und Einflüsse auf den Fräsprozess erzeugt. Der Fachbeitrag behandelt den Einsatz präparierter Mikrowerkzeuge in Zerspanversuchen, in denen auf die Zerspankräfte, den Verschleiß sowie die Oberflächengüten eingegangen wird.   Process reliability in micro milling can be increased by a defined cutting edge preparation. Different cutting edge preparations cause different effects on tool behavior in the downstream micro milling process. In this paper, the process forces, the tool wear and the surface quality of prepared micro milling tools are characterized in cutting tests.


Sensors ◽  
2019 ◽  
Vol 19 (18) ◽  
pp. 3817 ◽  
Author(s):  
Xuefeng Wu ◽  
Yahui Liu ◽  
Xianliang Zhou ◽  
Aolei Mou

Monitoring of tool wear in machining process has found its importance to predict tool life, reduce equipment downtime, and tool costs. Traditional visual methods require expert experience and human resources to obtain accurate tool wear information. With the development of charge-coupled device (CCD) image sensor and the deep learning algorithms, it has become possible to use the convolutional neural network (CNN) model to automatically identify the wear types of high-temperature alloy tools in the face milling process. In this paper, the CNN model is developed based on our image dataset. The convolutional automatic encoder (CAE) is used to pre-train the network model, and the model parameters are fine-tuned by back propagation (BP) algorithm combined with stochastic gradient descent (SGD) algorithm. The established ToolWearnet network model has the function of identifying the tool wear types. The experimental results show that the average recognition precision rate of the model can reach 96.20%. At the same time, the automatic detection algorithm of tool wear value is improved by combining the identified tool wear types. In order to verify the feasibility of the method, an experimental system is built on the machine tool. By matching the frame rate of the industrial camera and the machine tool spindle speed, the wear image information of all the inserts can be obtained in the machining gap. The automatic detection method of tool wear value is compared with the result of manual detection by high precision digital optical microscope, the mean absolute percentage error is 4.76%, which effectively verifies the effectiveness and practicality of the method.


2020 ◽  
Vol 21 ◽  
pp. 189-193 ◽  
Author(s):  
S.V. Alagarsamy ◽  
M. Ravichandran ◽  
M. Meignanamoorthy ◽  
S. Sakthivelu ◽  
S. Dineshkumar

2018 ◽  
Vol 26 ◽  
pp. 383-393 ◽  
Author(s):  
Xiaona Luan ◽  
Song Zhang ◽  
Jianfeng Li ◽  
Gamini Mendis ◽  
Fu Zhao ◽  
...  

2019 ◽  
Author(s):  
Tan Chun Yu ◽  
M. Fathullah ◽  
M. M. A. Abdullah ◽  
Z. Shayfull ◽  
Faheem Tahir
Keyword(s):  
Fly Ash ◽  

2012 ◽  
Vol 6 (4-5) ◽  
pp. 431-437 ◽  
Author(s):  
Jilin Zhang ◽  
Chen Zhang ◽  
Song Guo ◽  
Laishui Zhou

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