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Analysis and Optimization of Cutting Tool Coating Effects on Surface Roughness and Cutting Forces on Turning of AA 6061 Alloy
(Hindawi Ltd, 2021) Akgün, Mahir; Kara, Fuat
The present work has been focused on cutting force (Fc) and analysis of machined surface in turning of AA 6061 alloy with uncoated and PVD-TiB2 coated cutting inserts. Turning tests have been conducted on a CNC turning under dry cutting conditions based on Taguchi L-18 (2(1) x 3(3)) array. Kistler 9257A type dynamometer and equipment have been used in measuring the main cutting force (Fc) in turning experiments. Analysis of variance (ANOVA) has been applied to define the effect levels of the turning parameters on Fc and Ra. Moreover, the mathematical models for Fc and Ra have been developed via linear and quadratic regression models. The results indicated that the best performance in terms of Fc and Ra was obtained at an uncoated insert, cutting speed of 350 m/min, feed rate of 0.1 mm/rev, and depth of cut of 1 mm. Moreover, the feed rate is the most influential parameter on Ra and Fc, with 64.28% and 54.9%, respectively. The developed mathematical models for cutting force (Fc) and surface roughness (Ra) present reliable results with coefficients of determination (R-2) of 96.04% and 92.15%, respectively.
ANN and multiple regression method-based modelling of cutting forces in orthogonal machining of AISI 316L stainless steel
(Springer, 2015) Kara, Fuat; Aslantaş, Kubilay; Çiçek, Adem
In this study, predictive modelling was performed for the cutting forces generated during the orthogonal turning of AISI 316L stainless steel. An artificial neural network (ANN) and a multiple regression analysis were utilised. The input parameters of the ANN model were the cutting speed, feed rate and coating type. In the model, tungsten carbide cutting tools, uncoated and with two different coatings (TiCN + Al2O3 + TiN and Al2O3), were used. The ANN predictions closest to the experimental cutting forces were obtained for the main cutting force (F (c)) and the feed force (F (f)) by 3-7-1 and 3-6-1 network architectures with a single hidden layer, respectively. While the SCG learning algorithm provided the optimal results for F (c), the optimal results for F (f) were provided by the LM learning algorithm. A very good performance of the neural network, in terms of agreement with the experimental data, was achieved. With the developed model, the cutting forces could be precisely predicted depending on the cutting speed, feed rate and coating type. The prediction results showed that the ANN was superior to the multiple regression method in terms of prediction capability.
Artificial Intelligence-Based Surface Roughness Estimation Modelling for Milling of AA6061 Alloy
(Hindawi Ltd, 2021) Eser, Aykut; Ayyildiz, Elmas Askar; Ayyildiz, Mustafa; Kara, Fuat
This study introduces the improvement of mathematical and predictive models of surface roughness parameter (Ra) in milling AA6061 alloy using carbide cutting tools coated with CVD-TiCN in dry condition. An experimental model has been improved for estimating the surface roughness using artificial neural networks (ANN) and response surface methodology (RSM). For these models, cutting speed, depth of cut, and feed rate were evaluated as input parameters for experimental design. For the ANN modelling, the standard backpropagation algorithm was established to be the optimum selection for training the model. In the forming of the network construction, five different learning algorithms were used: the conjugate gradient backpropagation, Levenberg-Marquardt, scaled conjugate gradient, quasi-Newton backpropagation, and resilient backpropagation. The best consequent with single hidden layers for the surface roughness was obtained by 3-8-1 network structures. The statistical analysis was performed with RSM-based second-order mathematics model. The influences of the cutting parameters on surface roughness were defined by using analysis of variance (ANOVA). The ANOVA results show that the depth of cut is the most effective parameter on surface roughness. Prediction models developed using ANN and RSM were compared in terms of prediction accuracy R2, MEP, and RMSE. The data estimated from ANN and RSM were realized to be very close to the data acquired from experimental studies. The value R-2 of RSM model was higher than the values of the ANN model which demonstrated the stability and sturdiness of the RSM method.
Artificial neural network based modelling of performance of a beta-type Stirling engine
(Sage Publications Ltd, 2013) Özgören, Yaşar Önder; Çetinkaya, Selim; Sarıdemir, Suat; Çiçek, Adem; Kara, Fuat
In this article, artificial neural network has been used in order to predict the power (P) and torque (T) values obtained from a beta-type Stirling engine that uses air as working fluid. Experimental data have been obtained for different charge pressures and hot source temperatures using ZrO2-coated and uncoated displacers. The closest artificial neural network results to experimental torque and power values were obtained with double hidden layer 5-13-9-1 and 5-13-7-1 network architectures, respectively. The best prediction values were obtained by Levenberg-Marquardt learning algorithm. Correlation coefficient (R-2) for the torque values were 0.998331 and 0.997231 for the training and test sets, respectively, while R-2 value for power values were 0.998331 and 0.997231 for the training and test sets, respectively. R-2 values show that the developed artificial neural network is an acceptable and powerful modelling technique in predicting the torque and power values of the beta-type Stirling engine.
Bilgisayar destekli tasarımda parametrik dişli çark uygulamaları
(2010) Ayyıldız, Mustafa; Çiçek, Adem; Kara, Fuat
Bu çalışmada, dişli çark ve dişli çark çiftlerinin BDT (Bilgisayar Destekli Tasarım) ortamında parametrik olarak çizimi ve modellenmesi için bir yazılım geliştirilmiştir. Yazılım geliştirmede, yaygın bir kullanım alanına sahip ve diğer programlama dillerine göre oldukça basit olan Visual BASIC ve AutoLISP programlama dillerinin etkileşimli olarak kullanıldığı karma bir programlama yapısı tercih edilmiştir. Sistemde, modül, diş sayısı, iletim oranı vb. gibi parametreler kullanıcı tarafından girilerek, dişli çark veya dişli çark çiftleri boyutlandırılmakta ve daha sonra BDT ortamında dişli çarkların çizimi veya modellenmesi otomatik olarak yapılabilmektedir. Bu çalışma, dişli çark çizimi ve modellenmesi için tasarımcıya hızlı ve işlevsel bir yardımcı program alternatifi sunmaktadır.
Calculation and Estimation of Surface Roughness and Energy Consumption in Milling of 6061 Alloy
(Hindawi Ltd, 2020) Ozturk, Burak; Kara, Fuat
The best surface quality that can be achieved in manufactured products has become the main goal of industrial enterprises in recent years. Due to the subsequent increase in energy consumption costs from rising energy efficiency rates, manufacturers are contributing to this issue by applying advanced design functions for their machines. In line with the same objective, this study investigated the machinability of 6061 aluminum alloy, which has a high throughput rate and low machinability featuring built up edge. The aim of the research was to optimize the cutting parameters for minimum surface roughness (Ra) and energy consumption (EC) using a CNC milling machine. At the same time, measurements of power indices (A) of both the spindle and the X-axis motors were carried out with the goal of improved chip removal as compared to literature studies. The experiment was designed according to the Taguchi L-16 (2(1) x 4(3)) orthogonal index. Four different cutting speeds (60, 120, 180, and 240 m/min), feed rates (0.10, 0.15, 0.20, and 0.25 mm/rev), and cutting depths (0.5, 0.10, 0.15, and 0.20 mm) and two different cooling methods (coolant fluid and dry cutting) were selected as cutting parameters.
Comparison and optimization of PVD and CVD method on surface roughness and flank wear in hard-machining of DIN 1.2738 mold steel
(Emerald Group Publishing Ltd, 2019) Kara, Fuat; Öztürk, Burak
Purpose This paper aims to examine the performance of the machining parameters used in the hard-turning process of DIN 1.2738 mold steel and identify the optimum machining conditions. Design/methodology/approach Experiments were carried out via the Taguchi L18 orthogonal array. The evaluation of the experimental results was based on the signal/noise ratio. The effect levels of the control factors on the surface roughness and flank wear were specified with analysis of variance performed. Two different multiple regression analyses (linear and quadratic) were conducted for the experimental results. A higher correlation coefficient (R-2) was obtained with the quadratic regression model, which showed values of 0.97 and 0.95 for Ra and Vb, respectively. Findings The experimental results indicated that generally better results were obtained with the TiAlN-coated tools, in respect to both surface roughness and flank wear. The Taguchi analysis found the optimum results for surface roughness to be with the cutting tools of coated carbide using physical vapor deposition (PVD), a cutting speed of 160 m/min and a feed rate of 0.1 mm/rev, and for flank wear, with cutting tools of coated carbide using PVD, a cutting speed of 80 m/min and a feed rate of 0.1 mm/rev. The results of calculations and confirmation tests for Ra were 0.595 and 0.570 mu m, respectively, and for the Vb, 0.0244 and 0.0256 mm, respectively. Developed quadratic regression models demonstrated a very good relationship. Originality/value Optimal parameters for both Ra and Vb were obtained with the TiAlN-coated tool using PVD. Finally, confirmation tests were performed and showed that the optimization had been successfully implemented.
Dynamic Behavior Analysis of Rotor Supported by Damped Rolling Element Bearing Housing
(Gazi Univ, 2017) Saruhan, Hamit; Kam, Menderes; Kara, Fuat
A typical rotating machinery system consists various components, such as rotor, support bearing, and disks. These components pass out energy into the system when coincident to critical speeds. Ignoring such event might lead to disastrous breakdown of the system. Due to necessity and vital contribution to most rotating machineries, the requirements on rolling element bearings have become stricter every day. In this experimental study, the dynamic behavior and displacement of rotor supported by damped rolling element bearing housing for different running speeds and load levels are analyzed and compared.
Effect of cryogenic treatment on wear behavior of Sleipner cold work tool steel
(Elsevier Ltd, 2023) Kara, Fuat; Küçük, Yılmaz; Özbek, Onur; Özbek, Nursel Altan; Gök, Mustafa Sabri; Altaş, Emre; Uygur, İlyas
Cryogenic treatment, also known as subzero heat treatment, is a cooling process that complements conventional heat treatment to improve the properties of metals. Unlike coatings, it is a one-time, inexpensive, permanent operation that affects the entire part. This method is mainly applied to tool steels used in mold making. In this study, the changes caused by the effects of shallow and deep cryogenic treatment on Sleipner cold work tool steel were investigated in terms of microhardness, microstructure, coefficient of friction (COF), and wear rate (WR). For this purpose, the test specimens were subjected to the cryogenic treatments performed at two different temperatures (?80 ºC for the shallow cryogenic treatment (SCT) and ? 180 ºC for the deep cryogenic treatment (DCT)) and various retention times (12 h, 24 h for SCT and 12 h, 24 h, 36 h for DCT). Dry sliding wear tests were carried out under different loads (10 N and 20 N) and varying test durations (60, 120, and 240 min) at a constant sliding speed of 0.075 m/s. According to the microhardness results, it was determined that the cryogenic treatment increased the hardness by 6.53 %. According to the microstructure investigations, a more homogeneous structure was observed with the cryogenic treatment, and secondary carbide precipitations were detected. It was observed that the conventional heat-treated (CHT) sample gave the highest COF value with an average coefficient of friction of 0.63. The lowest COF value of 0.58 was observed in the DCT-12 sample. After the wear tests, the lowest wear rate value for both load values was obtained from the DCT-36 specimen. © 2023 Elsevier Ltd
Effect of Deep Cryogenic Treatment on Wear Resistance of AISI 52100 Bearing Steel
(Springer India, 2014) Güneş, İbrahim; Çiçek, Adem; Aslantaş, Kubilay; Kara, Fuat
In this study, the effects of deep cryogenic treatment (DCT) on the wear resistance of AISI 52100 bearing steel were investigated. For this purpose, a number of bearing steel samples were held for different times (12, 24, 36, 48, 60 h) at deep cryogenic temperatures (-145 degrees C). The wear experiments were carried out in a ball-disk arrangement, by applying loads of 10 and 20 N and a sliding velocity of 0.15 m/s. After conducting the experimental studies, 36 h was found to be the optimal holding time. At this holding time, the wear rate and friction coefficient were decreased, while the hardness reached to maximum values. It was observed that DCT led to significant microstructural changes, which resulted in improved tribological properties.
Effect of Eco-Friendly Minimum Quantity Lubrication in Hard Machining of Vanadis 10: A High Strength Steel
(Wiley-V C H Verlag Gmbh, 2022) Altan Özbek, Nursel; Özbek, Onur; Kara, Fuat; Saruhan, Hamit
Vanadis 10 SuperClean is a high vanadium alloyed powder metallurgy tool steel offering a unique combination of an excellent abrasive wear resistance in combination with a good chipping resistance. In this study, the effects of the eco-friendly (100% biodegradable plant-based) Minimum Quantity Lubrication (MQL) system on the cutting temperature, cutting tool vibration amplitude, tool wear, average surface roughness, and tool life in the turning of Vanadis 10 steel (50 HRC) used in the automotive industry are investigated. In the experiments, TiCN/Al2O3/TiN-coated cemented carbide tools are used. Experimental results showed that, compared to Dry machining, MQL produced remarkable improvements in terms of cutting temperature, cutting tool vibration amplitude, tool wear, and surface roughness. In addition, the Taguchi experimental design, ANOVA, and linear and quadratic regression analyses are applied to the experimental data. The statistical analysis found the most effective parameter on average surface roughness to be the cutting environment (86.31%). It is determined that the cutting speed was the most effective on vibration amplitude and tool wear (46.22% and 32.41%). The correlation coefficients for the linear and quadratic regression analysis were 0.9 and 0.95, respectively.
Effect of machinability, microstructure and hardness of deep cryogenic treatment in hard turning of AISI D2 steel with ceramic cutting
(Elsevier, 2020) Kara, Fuat; Karabatak, Mustafa; Ayyildiz, Mustafa; Nas, Engin
This study examined the hard turning of AISI D2 cold work tool steel subjected to deep cryogenic processing and tempering and investigated the effects on surface roughness and tool wear. In addition, the effects of the deep cryogenic processes on mechanical properties (macro and micro hardness) and microstructure were investigated. Three groups of test samples were evaluated: conventional heat treatment (CHT), deep cryogenic treatment (DCT-36) and deep cryogenic treatment with tempering (DCTT-36). The samples in the first group were subjected to only CHT to 62 HRc hardness. The second group (DCT-36) underwent processing for 36 h at -145 degrees C after conventional heat treatment. The latter group (DCTT-36) had been subjected to both conventional heat treatment and deep cryogenic treatment followed by 2 h of tempering at 200 degrees C. In the experiments, Al2O3 + TiC matrix-based untreated mixed alumina ceramic (AB30) and Al2O3 + TiC matrix-based TiN-coated ceramic (AB2010) cutting tools were used. The artificial intelligence method known as artificial neural networks (ANNs) was used to estimate the surface roughness based on cutting speed, cutting tool, workpiece, depth of cut and feed rate. For the artificial neural network modeling, the standard back-propagation algorithm was found to be the optimum choice for training the model. Three different cutting speeds (50, 100 and 150 m/min), three different feed rates (0.08, 0.16 and 0.24 mm/rev) and three different cutting depths (0.25, 0.50 and 0.75 mm) were selected. Tool wear experiments were carried out at a cutting speed of 150 m/min, a feed rate of 0.08 mm/rev and a cutting depth of 0.6 mm. As a result of the experiments, the best results for both surface roughness and tool wear were obtained with the DCTT-36 sample. When cutting tools were compared, the best results for surface roughness and tool wear were obtained with the coated ceramic tool (AB2010). The macroscopic and micro hardness values were highest for the DCT-36. From the microstructural point of view, the DCTT-36 sample showed the best results with homogeneous and thinner secondary carbide formations. (C) 2019 The Authors. Published by Elsevier B.V.
Effect of PVD-TiN and CVD-Al2O3 Coatings on Cutting Force, Surface Roughness, Cutting Power, and Temperature in Hard Turning of AISI H13 Steel
(Springer, 2022) Akgün, Mahir; Özlü, Bariş; Kara, Fuat
The present work focusses on the hard turning of AISI H13 tool steel with PVD-TiN- and CVD-Al2O3-coated ceramic cutting tools. In this context, hard turning tests have been performed under dry cutting conditions at five different cutting speeds (120, 165, 210, 255, and 300 m/min), three different feeds (0.12, 0.18, and 0.24 mm/rev), and a constant depth of cut of 0.6 mm. The main cutting force (Fc), surface roughness (Ra), cutting power (Pc), and temperature (T), as well tool wear mechanisms, have been investigated under these subjected conditions. The outcomes of this study show that while feed plays an important role in the main cutting force and surface roughness, cutting speed also plays an important role in cutting power and temperature. The average main cutting force, surface roughness, cutting power, and temperature are 13, 15, 14, and 11% better when AISI H13 alloy is machined with the PVD-TiN-coated inserts than those in the CVD-Al2O3-coated inserts, respectively. SEM examination also revealed that the abrasion and adhesion mechanism is more effective when AISI H13 alloy is machined with the CVD-Al2O3-coated inserts compared to those in the PVD-TiN-coated inserts.
Effect of vibration and cutting zone temperature on surface topography during hybrid cooling/lubrication assisted machining of Vanadis 10
(Walter De Gruyter Gmbh, 2023) Ozbek, Onur; Ozbek, Nursel Altan; Kara, Fuat; Saruhan, Hamit
New alloy materials developed to meet the increasing technological needs of people come into our lives with some difficulties in terms of machinability. New cooling and lubrication techniques have been developed to facilitate the workability of such difficult-to-process materials and protect the world ecologically and the quality of the produced product. The workpiece used in this study, Vanadis 10 SuperClean, is a high vanadium alloyed powder metallurgy tool steel offering a unique combination of excellent abrasive wear resistance in combination with a good chipping resistance. The present study investigated the effects of dry, cryo, and CryoMQL cutting conditions on cutting tool vibration amplitude, cutting temperature, surface roughness, tool wear, and tool life in turning of Vanadis 10 tool steel used in the automotive industry. The experiments were performed using TiCN/Al2O3/TiN coated cemented carbide tools and cutting parameters as the constant depth of cut (1 mm), feed rates (0.08, 0.1, 0.12 mm rev(-1)), and cutting speeds (80, 100, 120 m min(-1)). The results obtained from experiments showed that spraying liquid nitrogen into the cutting zone provided significant improvements on cutting temperature, tool wear, cutting tool vibration amplitude, and surface roughness. The best results in terms of all output were achieved in the CryoMQL cutting environment. CryoMQL environment has reduced surface roughness up to 65.03 %, flank wear 56.99 %, cutting temperature 32.77 %, and cutting tool vibration amplitude up to 42.76 % compared to dry machining.
Effects of Deep Cryogenic Treatment on the Wear Resistance and Mechanical Properties of AISI H13 Hot-Work Tool Steel
(Springer, 2015) Çiçek, Adem; Kara, Fuat; Kıvak, Turgay; Ekici, Ergün; Uygur, İlyas
In this study, a number of wear and tensile tests were performed to elucidate the effects of deep cryogenic treatment on the wear behavior and mechanical properties (hardness and tensile strength) of AISI H13 tool steel. In accordance with this purpose, three different heat treatments (conventional heat treatment (CHT), deep cryogenic treatment (DCT), and deep cryogenic treatment and tempering (DCTT)) were applied to tool steel samples. DCT and DCTT samples were held in nitrogen gas at -145 degrees C for 24 h. Wear tests were conducted on a dry pin-on-disk device using two loads of 60 and 80 N, two sliding velocities of 0.8 and 1 m/s, and a wear distance of 1000 m. All test results showed that DCT improved the adhesive wear resistance and mechanical properties of AISI H13 steel. The formation of small-sized and uniformly distributed carbide particles and the transformation of retained austenite to martensite played an important role in the improvements in the wear resistance and mechanical properties. After cleavage fracture, the surfaces of all samples were characterized by the cracking of primary carbides, while the DCT and DCTT samples displayed microvoid formation by decohesion of the fine carbides precipitated during the cryo-tempering process.
Evaluation of machinability of hardened and cryo-treated AISI H13 hot work tool steel with ceramic inserts
(Elsevier Sci Ltd, 2013) Çiçek, Adem; Kara, Fuat; Kıvak, Turgay; Ekici, Ergün
The positive effects of deep cryogenic treatment on the wear resistance of cutting tools and workpiece material are well known; however, no information has been reported about the effect on the machinability of cryo-treated tool steel in hard turning. In order to investigate the effects of cryogenic treatment on the machinability of hardened and cryo-treated tool steel, a number of investigations were performed on the hard turning of cryo-treated AISI H13 hot-work tool steel with two ceramic inserts under both dry and wet cutting conditions. Three categories of the hot-work tool steel were turned in the machinability studies: conventional heat treated (CHT), cryo-treated (CT) and cryo-treated and tempered (m). Experimental results showed that the lowest wear and surface roughness (Ra) values were obtained in the turning of the CTT samples. Additionally, in terms of main cutting force (Fc), surface roughness (Ra) and tool wear, Ti[C, N]-mixed alumina inserts (CC650) showed a better performance than SiC whisker-reinforced alumina inserts (CC670) under both dry and wet cutting conditions. The use of cutting fluid slightly improved the machinability of the tool steel. (C) 2013 Elsevier Ltd. All rights reserved.
Examination of Machining Parameters and Prediction of Cutting Velocity and Surface Roughness Using RSM and ANN Using WEDM of Altemp HX
(Hindawi Limited, 2022) Manoj, I.V.; Soni, Hargovind; Narendranath, S.; Mashinini, P.M.; Kara, Fuat
The Altemp HX is a nickel-based superalloy having many applications in chemical, nuclear, aerospace, and marine industries. Machining such superalloys is challenging as it may cause both tool and surface damage. WEDM, a non-contact machining technique, can be employed in the machining of such alloys. In the present study, different input parameters which include pulse on time, wire span, and servo gap voltage were investigated. The cutting velocity, surface roughness, recast layer, and microhardness variations were examined on the WEDMed surface. The genetic algorithm was used to optimize the cutting velocity and surface roughness, thereby improving the overall quality of the product. The highest recast layer values were recorded as 25.8 ?m, and the lowest microhardness was 170 HV. Response surface methodology and artificial neural network were employed for the prediction of cutting velocity and surface roughness. Artificial neural network prediction technique was the most efficient method for the prediction of response parameters as it predicted an error percentage lesser than 6%. © 2022 I. V. Manoj et al.
Experimental and Statistical Investigation of Machinability of AISI D2 Steel Using Electroerosion Machining Method in Different Machining Parameters
(Hindawi Ltd, 2021) Nas, Engin; Ozbek, Onur; Bayraktar, Furgan; Kara, Fuat
This study investigated the effects of machining parameters on the experimental and statistical results using the electric discharge method in the machining of AISI D2 cold work tool steel. The design of the experiment was established using the Taguchi L-18 method. The effect of the experiment parameters on the performance characteristics was analyzed by analysis of variance (ANOVA). As a result of the study, it was determined that increasing amperage and pulse time affected the surface roughness and hole diameter on the surface of the material. The lowest values for surface roughness, machining time, hole diameter, and crater diameter were determined as 2.085 mu m, 47 minutes, 12.010 mm, and 81.007 mu m, respectively. The highest wear amount was obtained as 0.604 grams with the processed parameters in the ninth experiment. When the signal-to-noise ratios were examined, the optimum combinations of the control factors for surface roughness, hole diameter, crater diameter, wear amount, wear rate, and processing time were determined as A(1)B(1)C(3), A(1)B(1)C(3), A(1)B(1)C(3), A(1)B(3)C(1), A(2)B(1)C(1), and A(1)B(3)C(3), respectively. According to the ANOVA results, the most important parameters affecting the test results for surface roughness, hole diameter, crater diameter, wear amount, material wear loss, and processing time were determined as amperage (49.34%), time-on (59.38%), amperage (55.65%), time-on (56.92%), amperage (51.42%), and amperage (78.02%), respectively. When the gray relational degree was calculated for the maximum and minimum values, the ideal factors for all output results were found to be the parameters applied in the third experiment.
Finite Element-Based Simulation of Cooling Rate on the Material Properties of an Automobile Silent Block
(Hindawi Ltd, 2020) Ozturk, Burak; Kara, Fuat
The aluminum silent block is the part that connects the front suspension mounting and the road wheels. These products are used in high-speed cars and are subject to high engineering stresses. Over time, fractures occur in the connection part of these products due to insufficient strength. These problems are related to production metallurgy, which led to the concept of this study. During mass production, these parts are manufactured using the aluminum extrusion method. In this study, a rapid cooling process using water was applied, with the aim of improving the mechanical properties of the connecting part exposed to high dynamic loads. Samples were taken from the regions of these products which differed in thickness and width, and microhardness and tensile tests were performed for each region. The effects of both the extrusion cooling rate and the regional flash cooling on the material properties were then characterized. As a result of the isothermal transformation, the grain size in the microstructure of the material had shrunk. According to the findings, in this type of production, an average increase in strength of 25% was observed in the parts of the material subjected to maximum stress. The stress and safety coefficient values were found using finite element analysis, and curves were then drawn showing the differences in the safety coefficient values from the different points. As a result of cooperation between university and industry, the material and mechanical properties of an automobile part were improved in this study. This research has shown that, in terms of the accuracy of the results, it is very important to consider the variations in different regions of the product when defining the mechanical properties of any material produced by applying casting, heat treatment, and plastic forming methods.
Influence of Support Vector Regression (SVR) on Cryogenic Face Milling
(Hindawi Ltd, 2021) Karthik, Rao M. C.; Malghan, Rashmi L.; Kara, Fuat; Shettigar, Arunkumar; Rao, Shrikantha S.; Herbert, Mervin A.
The paper aims to investigate the processing execution of SS316 in manageable machining cooling ways such as dry, wet, and cryogenic (LN2-liquid nitrogen). Furthermore, one parametric approach was utilized to study the influence and carry out the comparative analysis of LN(2)over dry and LN(2)over wet machining conditions. Response surface methodology (RSM) is incorporated to build a relationship model among the considered independent variables (spindle speed: (S, rpm), feed rate (F, mm/min), and depth of cut (doc) (D, mm)) and the dependent variable (surface roughness (Ra)). Since there is the involvement of more than one independent variable, the generation of regression equation is multiple linear regression. Based on the attained coefficient value of the independent variable, the respective impact on surface roughness is identified. The results of comparative analysis of LN(2)over dry and LN(2)over wet machining states revealed that LN2 machining yielded better surface finish with up to 64.9%, 54.9% over dry and wet machining, respectively, indicating the benefits of LN2 for achieving better Ra. The benchmark function of the proposed mode hybrid-bias (BNN-SVR) algorithm showcases the propensity to emerge out of the local minimum and coincide with the optimal target value. The performance of the (BNN-SVR) is a prevalent new ability to fetch the partially trained weights from the BNN model into the SVR model, thus leading to the conversion of static learning capability to dynamic capability. The performances of the adopted prediction approaches are compared through a range of attained error deviation, i.e., (RA: 3.95%-8.43%), (BNN: 2.36%-5.88%), (SVR: 1.04%-3.61%), respectively. Hybrid-bias (BNN-SVR) is the best suitable prediction model as it provides significant evidence by attaining less error in predicting Ra. However, SVR surpasses BNN and RSM approaches because of the convergence factor and narrow margin error.

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