Prediction and optimization of surface roughness and micro-hardness using grnn and MOORA-fuzzy-a MCDM approach for nitinol in WEDM

Majumder, Himadri; Maity, Kalipada

doi:10.1016/j.measurement.2018.01.003

Cited by 107 publications

(40 citation statements)

References 43 publications

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“…The cutting tool with higher nose radius creates a larger contact area with the workpiece, and a larger contact area means higher friction and thus an increase in the workpiece surface temperature. As a result, the workpiece material softens, while the cutting tools can keep their hardness at high temperatures [28][29][30][31][32][33]40]. Less tool wear and better tool life of the cutting tool with higher nose radius have been attributed to high strength of the cutting edge [45][46][47][48][70][71][72][73][74][75].…”

Section: Change In Tool Life Based On Cutting Speed and Feed Ratementioning

confidence: 99%

“…The NiTi alloy is in martensitic phase at low temperatures and in the austenitic phase at high temperatures [4]. A review of the literature showed that different manufacturing processes are used for the machining of NiTi alloy such as laser processing method [5][6][7][8][9][10][11][12][13][14][15][16], electrical discharge machining (EDM) [17][18][19][20][21][22][23][24][25][26][27][28][29][30][31], electrochemical polishing [32] and abrasive water jet processing method (AWJM) [33,34] for shaping NiTi alloy and also conventional machining methods (Turning [35][36][37][38][39][40][41][42][43][44][45][46][47][48][49][50], milling…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Investigation Of The Effects Of Machining Parameters On Tool Life And Surface Roughness During The Face Milling Of The NiTi Shape Memory Alloy With Uncoated Tools

Altaş¹,

Gökkaya²,

Özkan³

2020

Preprint

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Shape memory alloys (SMAs) are increasingly used in the fields of aviation, automotive and biomedicine due to their unique properties. Nickel-Titanium (NiTi) alloy materials, which are one of the shape memory alloys, are among the most frequently used alloy materials. The shape memory and super elastic effects of NiTi alloys, high ductility and deformation hardening make it difficult to shape burr. An additional problem is the formation of a white layer during machining. In this study, surface milling operations were performed in dry cutting conditions with uncoated cutting tools with different nose radii. The processing parameters were determined based on the experience gained as a result of the preliminary tests. Tungsten carbide cutting tools with different nose radii (0.4mm and 0.8mm) were used for the milling operations. Milling was carried out at three different cutting speeds (20, 35, 50 m/min), feed rates (0.03, 0.07, 0.14 mm/tooth), and a constant axial cutting depth (0.7 mm). As a result of our experimental studies, the best tool life was found to be in 0.8 mm nose radius cutting tools at 20 m/min cutting speed and 0.03 mm/tooth feed rate (0.264 mm). The minimum average surface roughness was found after milling with 0.8 mm nose radius cutting tool at 20 m/min cutting speed and 0.03 mm/tooth feed rate (0.346 μm). It has been determined that increasing the cutting tool nose radius reduces both the flank wear over the cutting tool and the average surface roughness.

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Section: Change In Tool Life Based On Cutting Speed and Feed Ratementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Investigation Of The Effects Of Machining Parameters On Tool Life And Surface Roughness During The Face Milling Of The NiTi Shape Memory Alloy With Uncoated Tools

Altaş¹,

Gökkaya²,

Özkan³

2020

Preprint

View full text Add to dashboard Cite

show abstract

“…Multi-objective optimization: Brauers and Zavadskas [25] proposed the MOORA method to simultaneously optimize the opposite objectives (n C c2). The calculation steps are as follows [26].…”

Section: Experimental Methodsmentioning

confidence: 99%

Multi-objective optimization of process parameter in EDM using low-frequency vibration of workpiece assigned for SKD61

et al. 2019

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Integrated vibration in electrical discharge machining (EDM) plays a key role in achieving high efficiency. High levels of variables can be employed in this approach due to integration. However, simultaneous optimization of the EDM parameters to achieve multi-objectives is still very complex and challenging. Studies on integrated vibration are still in a preliminary stage. This report addresses multi-objective optimization in EDM for SKD61 die steel using low-frequency vibration. MOORA (Multi-objective optimization based on ratio analysis) was chosen to resolve this multi-objective optimization problem. The material removal rate (MRR), tool wear rate (TWR) and surface roughness (SR) were selected as performance measures in the EDM process. An analytical hierarchical process (AHP) was used to determine the weight value of the quality indicators. The results indicate that low-frequency vibrations significantly improve machining efficiency. When the frequency of the vibrations increased, MRR increased significantly such that MRR MAX = 64.48%. TWR and SR are smaller and their increase are given as TWR MAX = 20.3% and SR MAX = 18.47%. MOORA has been identified as a suitable alternative to multi-objective optimization in an EDM process using low-frequency vibrations for an assigned workpiece. The optimum parameters required to achieve the multi-objective were Ton = 25 ls, I = 8 A, Tof = 5.5 ls and F = 512 Hz, at the resultant quality criteria of MRR = 9.564 mm 3 /min, TWR = 1.944 mm 3 /min and SR = 3.24 lm with a maximum error of 8.24%.

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“…18 The GRNN is composed of the input layer, pattern layer, summation layer, and the output layer. [19][20][21] Using pattern layer Gaussian function operation we can create output that is near nonlinear. The GRNN framework is shown in Figure 3.…”

Section: Grnnmentioning

confidence: 99%

Lathe tool chatter vibration diagnostic using general regression neural network based on Chua’s circuit and fractional-order Lorenz master/slave chaotic system

Tian

et al. 2018

Journal of Low Frequency Noise, Vibration and Active Control

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As product quality and production capacity requirements for precision processing become higher, making machine tools smart and improving performance is becoming the trend. In terms of machine tool processing quality, tool wear and chatter vibration are the factors that most directly affect processing quality. Traditionally, operators rely on their experience in determining whether the tool is worn or if there is chatter vibration. However, experience is not quantified data. Experience does not have a uniform judgment standard and can easily lead to wrong judgment. In recent years, many scholars have conducted in-depth studies of the two aforementioned phenomenons. These studies mainly use time and frequency domains to search for phenomenon features as the determination basis. Of time and frequency domain, studies on frequency domain are the most common. However, the frequency domain method requires a large amount of calculation, and the analysis process requires an excessive quantity of data dimensions, making this method unsuitable for real-time analysis. Thus, we propose a general regression neural network analysis method based on Chua's circuit and a fractional-order Lorenz master/slave composite chaotic system for detecting lathe tool turning chatter vibration in this study. We compared the dynamic error features produced by various fractionalorder chaotic systems and chose fractional orders with more obvious feature changes. We then substituted general regression neural network categorization. Compared to the frequency domain analysis method, the method proposed in this study requires fewer data dimensions, fewer calculations, and higher efficiency. Our proposed method also has higher precision and a higher discrimination rate. The result of this study shows that our proposed method has a 100% discrimination rate for determining turning chatter vibration.

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Prediction and optimization of surface roughness and micro-hardness using grnn and MOORA-fuzzy-a MCDM approach for nitinol in WEDM

Cited by 107 publications

References 43 publications

Investigation Of The Effects Of Machining Parameters On Tool Life And Surface Roughness During The Face Milling Of The NiTi Shape Memory Alloy With Uncoated Tools

Investigation Of The Effects Of Machining Parameters On Tool Life And Surface Roughness During The Face Milling Of The NiTi Shape Memory Alloy With Uncoated Tools

Multi-objective optimization of process parameter in EDM using low-frequency vibration of workpiece assigned for SKD61

Lathe tool chatter vibration diagnostic using general regression neural network based on Chua’s circuit and fractional-order Lorenz master/slave chaotic system

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