scholarly journals Experimental Analysis Of Coated And Uncoated Twist Drill; Review

2011 ◽  
pp. 22-25
Author(s):  
S. S. Pathak ◽  
V. R Kagade ◽  
M. S. Kadam
Keyword(s):  
Neural Network ◽  
Surface Roughness ◽  
Tool Wear ◽  
Cutting Tool ◽  
Back Propagation ◽  
Cutting Speed ◽  
Back Propagation Neural Network ◽  
Tool Selection ◽  
Radial Basis Neural Network ◽  
Different Types

Drilling is a process of making hole in a solid body with the help of multipoint cutting tool. Here to improve the life of tool, in order to minimize the production cost, to increase surface roughness different types of coating are applying over it, that coating soft/hard/ soft+ hard. Tool selection depends upon nature of drilling; dry, with coolant. This paper gives review of different coating techniques and its effectiveness by measuring deviation in hole diameter, surface roughness, and wear measurement. A back propagation neural network is preferred instead of radial basis neural network for the prediction of tool wear. It is considered that tool wear depends on cutting speed, feed, thrust force and torque.

Prediction of Surface Roughness Using Back-Propagation Neural Network in End Milling Ti-6Al-4V Alloy

2011 ◽  
Vol 325 ◽  
pp. 418-423 ◽  
Author(s):  
Song Zhang ◽  
Jian Feng Li
Keyword(s):  
Neural Network ◽  
Surface Roughness ◽  
End Milling ◽  
Back Propagation ◽  
Cutting Speed ◽  
Cutting Parameters ◽  
Back Propagation Neural Network ◽  
Depth Of Cut ◽  
Radial Depth ◽  
Bpnn Model

Surface roughness plays a significant role in machining industry for proper planning of process system and optimizing the cutting conditions. In this paper, a back-propagation neural network (BPNN) model has been developed for the prediction of surface roughness in end milling process. A large number of milling experiments were conducted on Ti-6Al-4V alloy using the uncoated carbide tools. Four cutting parameters including cutting speed, feed per tooth, radial depth of cut, and axial depth of cut are used as the inputs to develop the BPNN model, while surface roughness corresponding to these combinations of different cutting parameters is the output of the neural network model. The performance of the trained BPNN model has been verified with the experimental results, and it is found that the BPNN predicted and the experimental values are very close to each other.

Surface Roughness Modeling and Prediction by ANN when Drilling Udimet 720

2013 ◽  
Vol 581 ◽  
pp. 366-371 ◽  
Author(s):  
Marek Vrabel' ◽  
Ildikó Maňková ◽  
Jozef Beňo
Keyword(s):  
Neural Network ◽  
Surface Roughness ◽  
Tool Wear ◽  
Back Propagation ◽  
Test Sample ◽  
Back Propagation Neural Network ◽  
Cutting Conditions ◽  
Machined Surface ◽  
Machined Surface Roughness ◽  
Udimet 720

Article deals with design of artificial neural network (ANN) for prediction of the surface roughness as one of the important indicators of machined surface quality. Back propagation neural network was trained and tested for prediction of the machined surface roughness. Cutting conditions, selected monitoring indices and tool wear parameter were given as inputs to the ANN. Test sample was nickel based super alloy Udimet 720, which is used as material for highly stressed jet engine components. Experimental data collected from tests were used as input into ANN to identify the sensitivity among cutting conditions, monitoring indices and progressive tool wear and machined surface roughness.

scholarly journals Milling Inconel 718 Workpiece With Cryogenically Treated And Untreated Cutting Tools

2021 ◽  
Author(s):  
Hüseyin Gürbüz ◽  
Şehmus Baday
Keyword(s):  
Surface Roughness ◽  
Tool Wear ◽  
Abrasive Wear ◽  
Inconel 718 ◽  
Cutting Tool ◽  
Cutting Tools ◽  
Cutting Speed ◽  
Depth Of Cut ◽  
Cutting Tool Wear ◽  
Feed Force

Abstract Although Inconel 718 is an important material for modern aircraft and aerospace, it is a kind material, which is known to have low machinability. Especially, while these types of materials are machined, high cutting temperatures, BUE on cutting tool, high cutting forces and work hardening occur. Therefore, in recent years, instead of producing new cutting tools that can withstand these difficult conditions, cryogenic process, which is a heat treatment method to increase the wear resistance and hardness of the cutting tool, has been applied. In this experimental study, feed force, surface roughness, vibration, cutting tool wear, hardness and abrasive wear values that occurred as a result of milling of Inconel 718 material by means of cryogenically treated and untreated cutting tools were investigated. Three different cutting speeds (35-45-55 m/min) and three different feed rates (0.02-0.03-0.04 mm/tooth) at constant depth of cut (0.2 mm) were used as cutting parameters in the experiments. As a result of the experiments, lower feed forces, surface roughness, vibration and cutting tool wear were obtained with cryogenically treated cutting tools. As the feed rate and cutting speed were increased, it was seen that surface roughness, vibration and feed force values increased. At the end of the experiments, it was established that there was a significant relation between vibration and surface roughness. However, there appeared an inverse proportion between abrasive wear and hardness values. While BUE did not occur during cryogenically treated cutting tools, it was observed that BUE occurred in cutting tools which were not cryogenically treated.

scholarly journals CUTTING TOOL PERFORMANCE IN TURNING OF AL 7075-T651 ALUMINIUM ALLOY

2020 ◽  
Vol 21 (2) ◽  
pp. 177-185
Author(s):  
Natasha A. Raof ◽  
Nur Sofwati Daud @Ab Aziz ◽  
Abdul Rahman A. Ghani ◽  
Aishah Najiah Dahnel ◽  
Suhaily Mokhtar ◽  
...  
Keyword(s):  
Surface Roughness ◽  
Tool Wear ◽  
Aluminium Alloy ◽  
Wear Mechanism ◽  
Cutting Tool ◽  
Flank Wear ◽  
Cutting Speed ◽  
Tool Wear Rate ◽  
Tool Performance ◽  
Al 7075

 Recently, almost 70% of a commercial jetliner’s airframe is made of aluminium alloys. It is predicted that the application of aluminium alloy is to increase up to 65% by the year 2025. They are typically used because of their high strength to weight ratio. However, there are some drawbacks during machining aluminium alloy such as the adhesion wear and built-up edge (BUE) formation that can shorten tool life. As the tool wears, the machining performance, surface roughness, and cutting tool life are affected significantly. A lot of studies were conducted in order to minimize this critical issue. This project presents a study of the cutting tool performance of an uncoated carbide tool in dry turning operation on Al 7075-T651, in which the tool wear rate, volume of material removed, wear mechanism, and surface roughness were investigated. The machining tests were conducted on a CNC lathe machine to obtain the tool wear and surface roughness of the machined work piece. The average flank wear was measured using a digital microscope, whereas the wear mechanism was observed using a Scanning Electron Microscope (SEM). The average surface roughness (Ra) was measured using a surface roughness tester. The cutting time for this experiment was fixed at 40 minutes and all the results were analysed within this time range to evaluate the tool performance in the turning of Al 7075-T651. The results revealed that the tool performs better at low cutting speed, 250 m/min, by reducing the tool wear rate by 33%. The cutting speed of 250 m/min also contributed to 71% higher volume of material removed during the machining tests. The dominant type of wear found was flank wear, while the main principal of wear mechanism is adhesion. At higher cutting speed, the surface roughness was improved. Based on the results, it can be concluded that high cutting tool performance is achieved when low tool wear growth rate, high volume of material removal, and low surface roughness during turning operation are obtained. ABSTRAK: Kebelakangan ini, hampir 70% kerangka pesawat udara komersil diperbuat daripada aloi aluminium. Penggunaan aloi aluminum ini dijangka meningkat sehingga 65% pada tahun 2025. Ia biasa digunakan kerana nisbah kekuatan kepada berat yang tinggi. Walau bagaimanapun, terdapat beberapa kekurangan semasa pemesinan aloi aluminum ini iaitu pemakaian pelekat dan pembentukan binaan tepi (BUE) yang mengurangkan jangka hayat mata alat. Apabila mata alat menjadi haus, prestasi mesin, kekasaran permukaan, dan jangka hayat mata alat pemotong terjejas dengan ketara. Banyak kajian telah dijalankan bagi mengurangkan isu kritikal ini. Projek ini mengkaji prestasi mata alat pemotong karbida tidak bersalut dalam operasi mesin larik kering pada Al 7075-T651, di mana kadar haus mata alat, kuantiti bahan yang dibuang, mekanisme haus dan kekasaran permukaan telah diselidiki. Ujian pemesinan dijalankan pada mesin CNC mesin larik bagi mendapatkan kadar haus mata alat dan kekasaran permukaan material yang dimesin. Purata haus pengapit mata alat diukur dengan menggunakan mikroskop digital, manakala mekanisme haus dipantau menggunakan Mikroskop Elektronik Pengimbas (SEM). Purata kekasaran permukaan (Ra) diukur menggunakan alat penguji kekasaran permukaan. Tempoh masa pemotongan bagi eksperimen ini telah ditetapkan pada 40 minit dan semua keputusan telah dianalisa dalam tempoh masa ini bagi menilai prestasi mata alat dalam melarik Al 7075-T651. Hasil menunjukkan prestasi mata alat lebih baik pada kelajuan pemotongan rendah, 250 m/min dengan mengurangkan kadar haus mata alat sehingga 33%. Kelajuan pemotongan 250 m/min juga menyumbang kepada 71% peningkatan ke atas jumlah bahan yang dibuang semasa ujian pemesinan. Jenis haus yang dominan telah ditemui pada pengapit mata alat, manakala mekanisme haus yang utama adalah lekatan. Pada kelajuan pemotongan yang tinggi, kekasaran permukaan didapati lebih baik. Berdasarkan keputusan, dapat disimpulkan bahawa prestasi mata alat pemotong yang bagus dapat dicapai apabila kadar haus mata alat adalah rendah, jumlah penyingkiran bahan yang tinggi dan kekasaran permukaan yang rendah semasa operasi pelarikan dijalankan. ABSTRAK: Kebelakangan ini, hampir 70% kerangka pesawat udara komersil diperbuat daripada aloi aluminium. Penggunaan aloi aluminum ini dijangka meningkat sehingga 65% pada tahun 2025. Ia biasa digunakan kerana nisbah kekuatan kepada berat yang tinggi. Walau bagaimanapun, terdapat beberapa kekurangan semasa pemesinan aloi aluminum ini iaitu pemakaian pelekat dan pembentukan binaan tepi (BUE) yang mengurangkan jangka hayat mata alat. Apabila mata alat menjadi haus, prestasi mesin, kekasaran permukaan, dan jangka hayat mata alat pemotong terjejas dengan ketara. Banyak kajian telah dijalankan bagi mengurangkan isu kritikal ini. Projek ini mengkaji prestasi mata alat pemotong karbida tidak bersalut dalam operasi mesin larik kering pada Al 7075-T651, di mana kadar haus mata alat, kuantiti bahan yang dibuang, mekanisme haus dan kekasaran permukaan telah diselidiki. Ujian pemesinan dijalankan pada mesin CNC mesin larik bagi mendapatkan kadar haus mata alat dan kekasaran permukaan material yang dimesin. Purata haus pengapit mata alat diukur dengan menggunakan mikroskop digital, manakala mekanisme haus dipantau menggunakan Mikroskop Elektronik Pengimbas (SEM). Purata kekasaran permukaan (Ra) diukur menggunakan alat penguji kekasaran permukaan. Tempoh masa pemotongan bagi eksperimen ini telah ditetapkan pada 40 minit dan semua keputusan telah dianalisa dalam tempoh masa ini bagi menilai prestasi mata alat dalam melarik Al 7075-T651. Hasil menunjukkan prestasi mata alat lebih baik pada kelajuan pemotongan rendah, 250 m/min dengan mengurangkan kadar haus mata alat sehingga 33%. Kelajuan pemotongan 250 m/min juga menyumbang kepada 71% peningkatan ke atas jumlah bahan yang dibuang semasa ujian pemesinan. Jenis haus yang dominan telah ditemui pada pengapit mata alat, manakala mekanisme haus yang utama adalah lekatan. Pada kelajuan pemotongan yang tinggi, kekasaran permukaan didapati lebih baik. Berdasarkan keputusan, dapat disimpulkan bahawa prestasi mata alat pemotong yang bagus dapat dicapai apabila kadar haus mata alat adalah rendah, jumlah penyingkiran bahan yang tinggi dan kekasaran permukaan yang rendah semasa operasi pelarikan dijalankan.

scholarly journals Optimisation of Cutting Tool and Cutting Parameters in Face Milling of Custom 450 through the Taguchi Method

2019 ◽  
Vol 2019 ◽  
pp. 1-10 ◽  
Author(s):  
Harun Gokce
Keyword(s):  
Surface Roughness ◽  
Stainless Steel ◽  
Tool Wear ◽  
Taguchi Method ◽  
Cutting Tool ◽  
Cutting Tools ◽  
Cutting Speed ◽  
Cutting Parameters ◽  
Cutting Tool Wear ◽  
Wide Range

Stainless steels with unique corrosion resistance are used in applications with a wide range of fields, especially in the medical, food, and chemical sectors, to maritime and nuclear power plants. The low heat conduction coefficient and the high mechanical properties make the workability of stainless steel materials difficult and cause these materials to be in the class of hard-to-process materials. In this study, suitable cutting tools and cutting parameters were determined by the Taguchi method taking surface roughness and cutting tool wear into milling of Custom 450 martensitic stainless steel. Four different carbide cutting tools, with 40, 80, 120, and 160 m/min cutting speeds and 0.05, 0.1, 0.15, and 0.2 mm/rev feed rates, were selected as cutting parameters for the experiments. Surface roughness values and cutting tool wear amount were determined as a result of the empirical studies. ANOVA was performed to determine the significance levels of the cutting parameters on the measured values. According to ANOVA, while the most effective cutting parameter on surface roughness was the feed rate (% 50.38), the cutting speed (% 81.15) for tool wear was calculated.

Influence of Cutting Parameters and Tool Wear on Martensitic Stainless Steel Surface Integrity After a Face Milling Process

2009 ◽  
Author(s):  
Patricia Mun˜oz de Escalona ◽  
Paul G. Maropoulos
Keyword(s):  
Surface Roughness ◽  
Stainless Steel ◽  
Tool Wear ◽  
Surface Integrity ◽  
Surface Finish ◽  
Cutting Tool ◽  
Fatigue Behavior ◽  
Cutting Speed ◽  
Stainless Steel Surface ◽  
Workpiece Material

Surface finish is one of the most relevant aspects of machining operations, since it is one of the principle methods to assess quality. Also, surface finish influences mechanical properties such as fatigue behavior, wear, corrosion, etc. The feed, the cutting speed, the cutting tool material, the workpiece material and the cutting tool wear are some of the most important factors that affects the surface roughness of the machined surface. Due to the importance of the martensitic 416 stainless steel in the petroleum industry, especially in valve parts and pump shafts, this material was selected to study the influence of the feed per tooth and cutting speed on tool wear and surface integrity. Also the influence of tool wear on surface roughness is analyzed. Results showed that high values of roughness are obtained when using low cutting speed and feed per tooth and by using these conditions tool wear decreases prolonging tool life.

Performance Investigation for Precision Mold Turning by PCBN Tool

2009 ◽  
Vol 83-86 ◽  
pp. 746-755
Author(s):  
Shen Yung Lin ◽  
C.C. Tang ◽  
J.C. Shih ◽  
S.S. Chi
Keyword(s):  
Neural Network ◽  
Surface Roughness ◽  
Tool Wear ◽  
Cutting Temperature ◽  
Cutting Speed ◽  
Cutting Performance ◽  
Assessment Model ◽  
Depth Of Cut ◽  
Measuring Instruments ◽  
Pcbn Tool

In order to ascertain the superior characteristics of PCBN tool for hardened material machining, to promote the cutting performance and efficiency of a mold manufacturing, to investigate the wear mechanism of the cutting tool, and to investigate the dimensional accuracy and surface finish of the machined molds, SKD11 die steel and the polycrystalline cubic boron nitride are used as the workpiece and tool materials, respectively, in this study for turning experiments. After some proper surface layers removed from the workpiece in the experiment, the tool wear was measured through the toolmaker’s microscope and the roughness of the machined surface was measured by the roughness measuring instruments. So that, the associated sampling data prepared for training pattern of a neural network can be obtained. Besides, the noise-mediator was used to detect cutting noise during each surface layer removal for the cutting performance judgment in the machining processes additionally. An assessment model of cutting process is thus developed using a neural network system if the reliable and sufficient data is taken from the experiments. Based on the developed neural network, the complicated relationships between the cutting parameters (cutting speed, depth of cut and feed rate) and the cutting performance (surface roughness, tool wear and cutting temperature) can be clearly clarified. The best surface roughness of 0.29μm Ra is obtained from these experiments under the cutting conditions of d =0.2mm, f =0.05mm/rev and V=120m/min. This surface quality is equivalent to the manufacturing process of chemical-mechanical polishing (CMP), and the surface roughness of 0.2~0.5μm Ra may be attained by CMP. The CMP is always applied to high precision surface processing such as the valve piping and connector components in semiconductor/LED manufacturing.

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