Modeling Sustainable Manufacturing Practices Towards Economy Sustainability Performance

The purpose of this study is to present relationship between sustainable manufacturing practices (SMP) and economy sustainability performance (SP1_Economy). Eight practices that may affect SP1_Economy were studied. In the first phase, a multiple regression model considered a linear relationship between eight variables and SP1_Economy as performance was considered to the best fit of the observed values. It was perceived that multiple linear regression method indicated 24% from SP1_Economy of the variation in the observed data. Finally, it can be conclude that multiple linear regression method can predict the performance from observed practices in manufacturing firms

A Review Of Cloud Manufacturing: Issues And Opportunities

Cloud Manufacturing (CM) is the latest manufacturing paradigm that enables manufacturing to be looked upon as a service industry.The aim is to offer manufacturing as a service so that an individual or organization is willing to manufacture products and utilize this service without having to make capital investment.However,industry adoption of CM paradigm is still limited.This paper compared the current adoption of CM by the industry with the ideal CM environment.The gaps between the two were identified and related research topics were reviewed. This paper also outlined research areas to be pursued to facilitate CM adoption by the manufacturing industry.This will also improve manufacturing resource utilization efficiencies not only within an organization but globally.At the end,the cost benefits will be passed down to end customer

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%

Application of ANFIS in Predicting of TiAlN Coatings Hardness

In this paper, a new approach in predicting the hardness of Titanium Aluminum Nitrite (TiAlN) coatings using Adaptive Neuro-Fuzzy Inference System (ANFIS) is implemented. TiAlN coated cutting tool is widely used in machining due to its excellent properties. 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 hardness 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 and nonlinear RSM hardness models in terms of the root mean square error (RMSE) and model prediction accuracy. The result indicated that the ANFIS model using 3-3-3 triangular shapes membership function obtained better result compared to the fuzzy and nonlinear RSM hardness models. The result also indicated that the ANFIS model could predict the output response in high prediction accuracy even using limited training data

Predictive Modeling of TiN Coating Roughness

In this paper, an approach in modeling surface roughness of Titanium Nitrite (TiN) coating using Response Surface Method (RSM) is implemented. The TiN coatings were formed using Physical Vapor Deposition (PVD) sputtering process. N2 pressure, Argon pressure and turntable speed were selected as process variables. Coating surface roughness as an important coating characteristic was characterized using Atomic Force Microscopy (AFM) equipment. Analysis of variance (ANOVA) is used to determine the significant factors influencing resultant TiN coating roughness. Based on that, a quadratic polynomial model equation represented the process variables and coating roughness was developed. The result indicated that the actual coating roughness of validation runs data fell within the 90% prediction interval (PI) and the residual errors were very low. The findings from this study suggested that Argon pressure, quadratic term of N2 pressure, quadratic term of turntable speed, interaction between N2 pressure and turntable speed, and interaction between Argon pressure and turntable speed influenced the TiN coating surface roughness

Modeling of TiN Coating Thickness Using RSM Approach

In this paper, modeling of Titanium Nitrite (TiN) coating thickness using Response Surface Method (RSM) is implemented. Insert cutting tools were coated with TiN using Physical Vapor Deposition (PVD) sputtering process. N2 pressure, Argon pressure and turntable speed were selected as process variables while the coating thickness as output response. The coating thickness as an important coating characteristic was measured using surface profilometer equipment. Analysis of variance (ANOVA) was used to determine the significant factors influencing TiN coating thickness. Then, a polynomial linear model represented the process variables and coating thickness was developed. The result indicated that the actual validation data fell within the 90% prediction interval(PI) and the percentage of the residual errors were low. Findings from this study suggested that Argon pressure, N2 pressure and turntable speed influenced the TiN coating thicknes

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

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

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