Prediction of surface roughness and material removal rate in laser assisted turning of aluminium oxide using fuzzy logic

Saradhi, Vijay; Shashank, V; Teja, P. Sai; Anbarasu, G; Bharat, A; Jagadesh, T.

doi:10.1016/j.matpr.2018.06.409

Cited by 15 publications

(6 citation statements)

References 13 publications

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“…Finally, a comprehensive fitness-evaluation function R, is established as shown in Eq. (5). Here, W1…”

Section: Methods For Developing the Inverse Model Between Msr And Kmpmentioning

confidence: 99%

“…Both models were validated through experiments. Saradhi et al [5] developed an artificial intelligence-based model to understand the process mechanics and to predict the surface roughness and material removal rate (MRR) during laser-assisted turning of aluminum oxide using fuzzy logic. Zhang et al [6] developed a novel theoretical roughness prediction model, into which the components of kinematics, plastic side flow, material elastic recovery, and cracks effect were integrated, to determine the underlying mechanisms of the surface roughness variation during oblique diamond turning of the potassium dihydrogen phosphate (KDP) crystal.…”

Section: Introductionmentioning

confidence: 99%

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Forward and Inverse Relational Models between Machined Surface Roughness and Key Parameters of Machining Process

Pan

Ren

Wang

et al. 2021

Preprint

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The relational model between machined surface roughness (MSR) and the adopted key machining parameters (KMPs) significantly influences the predictability and controllability of the machining process; therefore, it has attracted considerable attention. However, two critical problems still persist in this field. First, although most existing studies focus on the prediction model for MSR (forward model), wherein the MSR is dependent on input KMPs values, the inverse model that can calculate the KMP based on input MSR value is equally important; however, the inverse model has not been investigated as extensively as the forward model. The second issue is that most of the existing forward models are mainly established based on mechanism analysis; however, due to the complexity of most machining processes, the accuracy and generality of the model are not optimal. Therefore, this paper proposes a universal method for mathematically establishing the inverse model of the relation between the MSR and KMP. Initially, based on the response surface methodology, orthogonal experiments were designed and conducted, and the results were used to establish the forward model between the MSR and KMP. Subsequently, by combining the forward model with a self-developed genetic algorithm-based multi-objective optimization algorithm, an establishing method for inverse model between MSR and KMPs was proposed. Finally, experiments were conducted to validate the developed models. The experimental results show that for the forward model, all the 10 experimental MSR values approach the MSR values predicted by the forward model, and the average deviation was only approximately 7%. Contrarily, for the inverse model, the average deviation was only approximately 7.64%. Both these results verify the accuracy and effectiveness of the proposed models. With this method, as long as the desired processing results and constraints are given, the process parameters can be accurately derived.

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“…Finally, a comprehensive fitness-evaluation function R, is established as shown in Eq. (5). Here, W1…”

Section: Methods For Developing the Inverse Model Between Msr And Kmpmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Forward and Inverse Relational Models between Machined Surface Roughness and Key Parameters of Machining Process

Pan

Ren

Wang

et al. 2021

Preprint

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show abstract

“…[15] utilised LAT and hybrid machining techniques to improve the machinability of Ti-6Al-4V, found that specific cutting energy and SR were reduced in comparison to conventional machining and reported the improvement in machining efficiency can lead to cost savings of approximately 30-40%. Saradhi et al [16] reported fuzzy logic-based artificial intelligence model to predict the material removal rate (MRR) and SR model in the context of laser-assisted turning (LAT) of aluminium oxide. Attia et al [17] aimed to enhance productivity by investigating the optimisation of the LAT of Inconel 718 through the utilisation of ceramic tools.…”

Section: Introductionmentioning

confidence: 99%

Comparative Analysis of RSM, ANN and ANFIS Techniques in Optimization of Process Parameters in Laser Assisted Turning of NITINOL Shape Memory Alloy

Naresh,

Sameer,

Bose

2024

Lasers Manuf. Mater. Process.

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The rationale behind the rising demand for orthogonal cutting in the processing of hard-to-cut materials is attributed to its advantages over discrete turning processes, which are achieved through the simultaneous application of two or more machining techniques. The utilisation of laser technology in machining processes is considered to be a sophisticated method for the processing of materials that are difficult to cut. The present study involved the utilisation of a Nd:YAG laser source to preheat a Nitinol shape memory alloy (SMA) work piece, which was subsequently subjected to machining using a laser-assisted Computer numerical control (CNC) turning centre. The objective of this investigation was to evaluate the properties of laser assisted machining (LAM) under varying machining conditions. The present study focuses on investigating the impact of process parameters on cutting force and surface roughness in the context of laser-assisted turning (LAT) of Nitinol SMA. To achieve this objective, statistical techniques such as the Response Surface Method (RSM), Adaptive Neurofuzzy Inference System (ANFIS), and Artificial Neural Networks (ANN) with Back Propagation (BP) algorithmbased numerical modelling are employed. The study was conducted utilising the Central Composite Design (CCD) methodology. The study examined the impact of process parameters, specifically cutting speed, feed, cutting depth, and laser power, on the response variables of cutting force (Fz) and surface roughness (SR). The results of the ANOVA analysis indicate that the cutting speed is the primary factor that significantly affects both Fz and SR, accounting for 31.39% and 60.36% of the variance, respectively. The feed rate is the second most influential factor, contributing 26.71% to Fz and 9.078% to SR. The optimisation of LAT parameters was ultimately achieved through the utilisation of a multi-objective desirability function, which effectively reduced both the SR and Fz simultaneously. The evaluation of the ANN model (4-24-2) involved its modelling and expected capabilities, which were compared to those of the RSM model using statistical measures such as root mean square error (RMSE) and absolute standard deviation. The outcomes of Fz and SR anticipated by RSM, ANFIS, and ANN exhibited a high degree of agreement with the empirical findings.

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“…Extensive research has been conducted on various ceramics and has proved in the past few decades that LAM is effective for machining brittle and hard materials [4][5][6][7][8][9][10][11][12][13][14][15][16][17][18][19][20]. In the early 1990s, scholars began to study the LAM of engineering ceramics [21].…”

Section: Introductionmentioning

confidence: 99%

Process Parameters Optimization Using Taguchi-Based Grey Relational Analysis in Laser-Assisted Machining of Si3N4

Zhao

Meng

et al. 2021

Materials

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Despite extensive research over the past three decades proving that laser-assisted machining (LAM) is effective for machining ceramic materials, which are affected by many machining parameters, there has been no systematic study of the effects of process parameters on surface quality in LAM ceramic materials. In this paper, the effects and optimization of laser power, spindle speed, feed rate, and cutting depth on surface roughness and work hardening of LAM Si3N4 were systematically studied, using grey relational analysis coupled with the Taguchi method. The results show that the combination of machining parameters determines the material removal mode at the material removal location, and then affects the surface quality. In ductile material removal mode, both the value of surface roughness and work hardening degree are smaller. Decreased surface roughness and work hardening degree can be obtained with smaller cutting depth and higher laser power.

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Prediction of surface roughness and material removal rate in laser assisted turning of aluminium oxide using fuzzy logic

Cited by 15 publications

References 13 publications

Forward and Inverse Relational Models between Machined Surface Roughness and Key Parameters of Machining Process

Forward and Inverse Relational Models between Machined Surface Roughness and Key Parameters of Machining Process

Comparative Analysis of RSM, ANN and ANFIS Techniques in Optimization of Process Parameters in Laser Assisted Turning of NITINOL Shape Memory Alloy

Process Parameters Optimization Using Taguchi-Based Grey Relational Analysis in Laser-Assisted Machining of Si3N4

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