Application of ANFIS in predicting TiAlN coatings flank wear

In this paper, a new approach in predicting the flank wear of Titanium Aluminum Nitrite (TiAlN) coatings using Adaptive Network Based Fuzzy Inference System (ANFIS) is implemented. TiAlN coated cutting tool is widely used in machining due to its excellent resistance to wear. The TiAlN coatings were formed using Physical Vapor Deposition (PVD) magnetron sputtering process. The substrate sputtering power, bias voltage and temperature were selected as the input parameters and the flank wear as an output of the process. A statistical design of experiment called Response Surface Methodology (RSM) was used in collecting optimized data. The ANFIS model was trained using the limited experimental data. The triangular, trapezoidal, bell and Gaussian shapes of membership functions were used for inputs as well as output. The results of ANFIS model were validated with the testing data and compared with fuzzy rule-based and RSM flank wear models in terms of the root mean square error (RMSE), coefficient determination (R2) and model accuracy (A). The result indicated that the ANFIS model using three bell shapes membership function obtained better result compared to the fuzzy and RSM flank wear models. The result also indicated that the ANFIS model could predict the output response in high prediction accuracy even using limited training data

Performance analysis of multi-spindle drilling of Al2024 with TiN and TiCN coated drills using experimental and artificial neural networks technique

Multi-spindle drilling simultaneously produces multiple holes to save time and increase productivity. The assessment of hole quality is important in any drilling process and is influenced by characteristics of the cutting tool, drilling parameters and machine capacity. This study investigates the drilling performance of uncoated carbide, and coated carbide (TiN and TiCN) drills when machining Al2024 aluminium alloy. Thrust force and characteristics of hole quality, such as the presence of burrs and surface roughness, were evaluated. The results show that the uncoated carbide drills performed better than the TiN and TiCN coated tools at low spindle speeds, while TiCN coated drills produced better hole quality at higher spindle speeds. The TiN coated drills gave the highest thrust force and poorest hole quality when compared with the uncoated carbide and TiCN coated carbide drills. Additionally, a multi-layer perceptron neural network model was developed, which could be useful for industries and manufacturing engineers for predicting the surface roughness in multi-hole simultaneous drilling processe

Performance analysis of multi-spindle drilling of Al2024 with TiN and TiCN coated drills using experimental and artificial neural networks technique

Multi-spindle drilling simultaneously produces multiple holes to save time and increase productivity. The assessment of hole quality is important in any drilling process and is influenced by characteristics of the cutting tool, drilling parameters and machine capacity. This study investigates the drilling performance of uncoated carbide, and coated carbide (TiN and TiCN) drills when machining Al2024 aluminium alloy. Thrust force and characteristics of hole quality, such as the presence of burrs and surface roughness, were evaluated. The results show that the uncoated carbide drills performed better than the TiN and TiCN coated tools at low spindle speeds, while TiCN coated drills produced better hole quality at higher spindle speeds. The TiN coated drills gave the highest thrust force and poorest hole quality when compared with the uncoated carbide and TiCN coated carbide drills. Additionally, a multi-layer perceptron neural network model was developed, which could be useful for industries and manufacturing engineers for predicting the surface roughness in multi-hole simultaneous drilling processe

Grey-Fuzzy Hybrid Optimization and Cascade Neural Network Modelling in Hard Turning of AISI D2 Steel

Nowadays hard turning is noticed to be the most dominating machining activity especially for difficult to cut metallic alloys. Attributes of dry hard turning are highly influenced by the amount of heat generation during cutting. Some major challenges are rapid tool wear, lower tool-life span, and poor surface finish but simultaneously generated heat is enough to provide thermal softening of hard work material and facilitates easier shear deformation thus easy cutting. Also, plenty of works reported the utilization of various cooling methods as well as coolants which successfully retard the intensity of cutting heat but this leads to additional cost as well as environmental and health issues. However, still, there is scope to select proper cutting tool materials, its geometry, and appropriate values of cutting parameters to get favorable machining outcomes under dry hard turning and avoid the cooling cost, environmental and health issue. Considering these challenges, current work utilizes PVD-coated (TiAlN) carbide insert in dry hard turning of AISI D2 steel. The multi-responses like tool-flank wear, chip morphology and chip reduction coefficient are considered. Further, to get the best combination of input cutting terms, grey-fuzzy hybrid optimization (Type I and Type II) is utilized considering the Gaussian membership function. Type II grey-fuzzy system attributed to 15 % less error (between GRG and GFG) compared to Type I. Hence, Type II grey-fuzzy system is utilized to get the optimal set of input terms. The optimal combination of input terms is found as t-1 (0.15 mm), s-4 (0.25 mm/rev) and is Vc-2 (100 m/min) which is comparable to the results obtained under spray impingement cooling using CVD tool in the literature. However, hard turning can be assessed under the dry condition with a PVD tool at the obtained optimal input condition for industrial uses. Further, six different types of cascade-forward-back propagation neural network modelling are accomplished. Among all models, CFBNN-4 model exhibited the best prediction results with a mean absolute error of 2.278% for flank wear (VBc) and 0.112% for the chip reduction coefficient (CRC). However, this model can be recommended for other engineering modelling problems

Surface roughness modelling in super duplex stainless steel turning

Super duplex stainless steels are alloys that have good corrosion resistance properties and are intended for applications in corrosive environments. Due to their chemical composition and microstructure providing high strength and thermal resistance as well as high ductility, the machinability of these alloys is difficult, resulting in longer production cycles and higher costs in terms of more frequent replacement of tools. In this paper, the machinability of the super duplex EN 1.4410 was investigated under environmentally friendly machining process by using cold compressed air as a coolant. Experimental data were generated using the range of selected input parameters and correspondingly analysed surface roughness as output data. Predictive models were developed in order to make a comparison of their prediction performance. In addition, this paper also describes the methodology for optimised development of a particular predictive model. Finally, comparative analysis of the accuracy of predictive models was performed in order to define which model represents the best fit for the analysed experimental data, and also to show validity of the optimisation process

Thin Film Roughness Optimization In The Tin Coatings Using Genetic Algorithms

Optimization is important to identify optimal parameters in many disciplines to achieve high quality products including optimization of thin film coating parameters. Manufacturing costs and customization of cutting tool properties are the two main issues in the process of Physical Vapour Deposition (PVD). The aim of this paper is to find the optimal parameters get better thin film roughness using PVD coating process. Three input parameters were selected to represent the solutions in the target data, namely Nitrogen gas pressure (N2), Argon gas pressure (Ar), and Turntable speed (TT), while the surface roughness was selected as an output response for the Titanium nitrite (TiN). Atomic Force Microscopy (AFM) equipment was used to characterize the coating roughness. In this study, an approach in modeling surface roughness of Titanium Nitrite (TiN) coating using Response Surface Method (RSM) has been implemented to obtain a proper output result. In order to represent the process variables and coating roughness, a quadratic polynomial model equation was developed. Genetic algorithms were used in the optimization work of the coating process to optimize the coating roughness parameters. Finally, to validate the developed model, actual data were conducted in different experimental run. In RSM validation phase, the actual surface roughness fell within 90% prediction interval (PI). The absolute range of residual errors (e) was very low less than 10 to indicate that the surface roughness could be accurately predicted by the model. In terms of optimization and reduction the experimental data, GAs could get the best lowest value for roughness compared to experimental data with reduction ratio of 46.75%

Particle swarm optimization algorithm to enhance the roughness of thin film in tin coatings

Nowadays, lots of disciplines require optimization to determine optimal parameters to accomplish top quality services which include parameters optimization of thin film coating. Modification of sharp tool characteristics and costs are two primary matters in the procedure of Physical Vapour Deposition (PVD). The purpose of this study is to figure out the optimal parameters in PVD coating process for better thin-film roughness. Three input parameters are chosen to describe the solutions over the target data, such as Nitrogen gas pressure (N2), Turntable speed (TT), and Argon gas pressure (Ar), although the surface roughness had been chosen being a result response of the Titanium nitrite (TiN). Atomic Force Microscopy (AFM) tools were applied to describe the roughness of coating layer. Within this research, a process of modelling using Response Surface Method (RSM) was applied for surface roughness of Titanium Nitrite (TiN) coating to get a best result. Particle Swarm Optimization (PSO) was applied as an optimization technique for the coating process to enhance characteristics of thin film roughness. In validation process, different experimental runs of actual data were conducted. It was found that residual error (e) is less than 10, to indicate that the model can accurately predict the surface roughness. Also, PSO could reduce the value of coating roughness at reduction of ≈ 48% to get a minimum value compared to actual data

Modeling And Optimization Of Physical Vapour Deposition Coating Process Parameters For Tin Grain Size Using Combined Genetic Algorithms With Response Surface Methodology

Optimization of thin film coating parameters is important in identifying the required output.Two main issues of the process of physical vapor deposition (PVD) are manufacturing costs and customization of cutting tool properties.The aim of this study is to identify optimal PVD coating process parameters.Three process parameters were selected, namely nitrogen gas pressure (N2),argon gas pressure (Ar),and Turntable Speed (TT),while thin film grain size of titanium nitrite (TiN) was selected as an output response.Coating grain size was characterized using Atomic Force Microscopy (AFM) equipment.In this paper,to obtain a proper output result,an approach in modeling surface grain size of Titanium Nitrite (TiN)coating using Response Surface Method (RSM) has been implemented. Additionally,analysis of variance(ANOVA) was used to determine the significant factors influencing resultant TiN coating grain size.Based on that,a quadratic polynomial model equation was developed to represent the process variables and coating grain size.Then,in order to optimize the coating process parameters, genetic algorithms (GAs) were combined with the RSM quadratic model and used for optimization work.Finally,the models were validated using actual testing data to measure model performances in terms of residual error and prediction interval (PI).The result indicated that for RSM,the actual coating grain size of validation runs data fell within the 95% (PI) and the residual errors were less than 10 nm with very low values, the prediction accuracy of the model is 96.09%.In terms of optimization and reduction the experimental data,GAs could get the best lowest value for grain size then RSM with reduction ratio of ≈6%, ≈5%, respectively

Intelligence Integration Of Particle Swarm Optimization And Physical Vapour Deposition For Tin Grain Size Coating Process Parameters

Optimization of thin film coating parameters is important in identifying the required output.Two main issues of the process of physical vapor deposition (PVD) are manufacturing costs and customization of cutting tool properties.The aim of this study is to identify optimal PVD coating process parameters.Three process parameters were selected,namely nitrogen gas pressure (N2),argon gas pressure (Ar),and Turntable Speed (TT),while thin film grain size of titanium nitrite (TiN) was selected as an output response.Coating grain size was characterized using Atomic Force Microscopy (AFM) equipment.In this paper,to obtain a proper output result,an approach in modeling surface grain size of Titanium Nitrite (TiN)coating using Response Surface Method (RSM) has been implemented. Additionally,analysis of variance (ANOVA) was used to determine the significant factors influencing resultant TiN coating grain size.Based on that,a quadratic polynomial model equation was developed to represent the process variables and coating grain size.Then,in order to optimize the coating process parameters,genetic algorithms (GAs) were combined with the RSM quadratic model and used for optimization work.Finally,the models were validated using actual testing data to measure model performances in terms of residual error and prediction interval (PI).The result indicated that for RSM,the actual coating grain size of validation runs data fell within the 95% (PI) and the residual errors were less than 10 nm with very low values, the prediction accuracy of the model is 96.09%.In terms of optimization and reduction the experimental data,GAs could get the best lowest value for grain size then RSM with reduction ratio of ≈6%, ≈5%, respectively

Prediction of surface roughness in low speed turning of AISI316 austenitic stainless steel

Surface roughness is an important quality in manufacturing, as it affects the product’s tribological, frictional and assembly characteristics. Turning stainless steel at low cutting speeds may result in a rougher surface due to built up edge formation, where as speed increases the surface roughness improves, due to the low contact time between the chip and the tool to allow bonding to occur.However, this increase in cutting speed produces higher tool wear rates, which increases the machining costs. Previous studies have indicated that savings in cost and manufacturing time are obtained when predicting the surface roughness, prior to the machining process. In this paper, experimental data are used to develop prediction models using Multiple Linear Regression and Artificial Neural Network methodologies. Results show that the neural network outperforms the linear model by a fair margin (1400%). Moreover, the developed Artificial Neural Network model has been integrated with an optimisation algorithm, known as Simulated Annealing (SA),this is done in order to obtain a set of cutting parameters that result in low surface roughness. A low value of surface roughness and the set of parameters resulting on it, are successfully yielded by the SA algorithm