Roughness Prediction Model Research Articles

Selection of optimum tool geometry and cutting conditions using a surface roughness prediction model for end milling

Influence of tool geometry on the quality of surface produced is well known and hence any attempt to assess the performance of end milling should include the tool geometry. In the present work, experimental studies have been conducted to see the effect of tool geometry (radial rake angle and nose radius) and cutting conditions (cutting speed and feed rate) on the machining performance during end milling of medium carbon steel. The first and second order mathematical models, in terms of machining parameters, were developed for surface roughness prediction using response surface methodology (RSM) on the basis of experimental results. The model selected for optimization has been validated with the Chi square test. The significance of these parameters on surface roughness has been established with analysis of variance. An attempt has also been made to optimize the surface roughness prediction model using genetic algorithms (GA). The GA program gives minimum values of surface roughness and their respective optimal conditions.

Modeling SiC surface roughness using neural network and atomic force microscopy

A prediction model for surface roughness was constructed using a neural network and atomic force microscopy. The silicon carbide etch process was characterized by a 25 full factorial experiment. The experimental ranges of process parameters were 600–900W source power, 50–150W bias power, 4–16mTorr pressure, 0–80% O2 percentage, and 6–12cm gap. The model factors were optimized by means of a genetic algorithm. The optimized model had a root mean-squared error of 0.11nm. From the model, various plots were predicted while being supported by actual measurements. The dc bias induced by each process parameter was correlated to the surface roughness. Increasing the bias power increased the surface roughness. In contrast, the surface roughness decreased as the dc bias was larger than about 600V. The surface roughness was strongly correlated to the source power-induced dc bias only at low bias powers. The pressure effect was clear only as the dc bias was maintained at 480V. For the variations in the O2 percentage, the dc bias role was insignificant.

Surface Roughness Prediction Model in Machining of Carbon Steel by PVD Coated Cutting Tools

The surface roughness model in the turning of AISI 1040 carbon steel was developed in terms of cutting speed, feed rate and depth of cut using response surface methodology. Machining tests were carried out using PVD-coated tools under different cutting conditions. The surface roughness equations of cutting tools when machining the carbon steels were achieved by using the experimental data. The results are presented in terms of mean values and confidence levels. The established equation shows that the feed rate was found to be a main influencing factor on the surface roughness. It increased with increasing the feed rate, but decreased with increasing the cutting speed and the depth of cut, respectively. The variance analysis for the second-order model shows that the interaction terms and the square terms were statically insignificant. However, it could be seen that the first-order effect of feed rate was significant while cutting speed and depth of cut was insignificant. The predicted surface roughness of the samples was found to lie close to that of the experimentally observed ones with 95% confident intervals.

A new technique for using multivariate adaptive regression splines (MARS) in pavement roughness prediction

The paper presents the application of a new statistical technique, multivariate adaptive regression splines (MARS), to a flexible pavement roughness prediction model. MARS is a non-parametric function estimation technique that shows great promise for fitting non-linear multivariate functions. The MARS approach was used to develop a roughness equation, based on available input, and was able to identify the threshold values of each input and the most important variables contributing to the roughness equation. The MARS technique allows easy interpretation of the relative importance of pavement condition variables, environmental factors and traffic for the overall fit.

A new technique for using multivariate adaptive regression splines (MARS) in pavement roughness prediction

The paper presents the application of a new statistical technique, multivariate adaptive regression splines (MARS), to a flexible pavement roughness prediction model. MARS is a non-parametric function estimation technique that shows great promise for fitting non-linear multivariate functions. The MARS approach was used to develop a roughness equation, based on available input, and was able to identify the threshold values of each input and the most important variables contributing to the roughness equation. The MARS technique allows easy interpretation of the relative importance of pavement condition variables, environmental factors and traffic for the overall fit.

Surface roughness predictive modeling: neural networks versus regression

Surface roughness plays an important role in product quality and manufacturing process planning. This research focuses on developing an empirical model for surface roughness prediction in finish turning. The model considers the following working parameters: work-piece hardness (material), feed, cutter nose radius, spindle speed and depth of cut. Two competing data mining techniques, nonlinear regression analysis and computational neural networks, are applied in developing the empirical models. The values of surface roughness predicted by these models are then compared with those from some of the representative models in the literature. Metal cutting experiments and tests of hypothesis demonstrate that the models developed in this research have a satisfactory goodness of fit. It has also presented a rigorous procedure for model validation and model comparison. In addition, some future research directions are outlined.

A genetic algorithmic approach for optimization of surface roughness prediction model

Due to the widespread use of highly automated machine tools in the industry, manufacturing requires reliable models and methods for the prediction of output performance of machining processes. The prediction of optimal machining conditions for good surface finish and dimensional accuracy plays a very important role in process planning. The present work deals with the study and development of a surface roughness prediction model for machining mild steel, using Response Surface Methodology (RSM). The experimentation was carried out with TiN-coated tungsten carbide (CNMG) cutting tools, for machining mild steel work-pieces covering a wide range of machining conditions. A second order mathematical model, in terms of machining parameters, was developed for surface roughness prediction using RSM. This model gives the factor effects of the individual process parameters. An attempt has also been made to optimize the surface roughness prediction model using Genetic Algorithms (GA) to optimize the objective function. The GA program gives minimum and maximum values of surface roughness and their respective optimal machining conditions.

Artificial Neural Network Modeling of Pavement Performance using Expert Judgement

ABSTRACT The application of Artificial Neural Networks (ANN) in pavement performance modeling for a case where the period of data collection is relatively short is presented in this report. For this purpose, the Canadian Long-Term Pavement Performance (C-LTPP) database is used to develop a roughness prediction model. To overcome the deficiency of poor predictive results beyond the period of the available data, a simple approach based on expert judgements is proposed. The result of the proposed deterministic approach is compared with those of the Bayesian probabilistic method. The latter is developed by the Canadian Strategic Highway Research Program. It is shown that, for the case under study, the neural network roughness prediction model utilizing expert judgements seems to be more realistic when compared to the Bayesian model. The reason is attributed to the fact that for the neural network model, the expert judgements are used only as a complement to the existing data. Therefore, neither data is extrapolated nor a judgement is used when actual data exists.

Modeling the surface roughness and cutting force for turning

In this paper, an abductive network is adopted to construct a prediction model for surface roughness and cutting force. This network is composed of a number of functional nodes, which are self-configured to form an optimal network hierarchy by using a predicted square error (PSE) criterion. Once the process parameters (cutting speed, feed rate and depth of cut) are given, the surface roughness and cutting force can be predicted by this network. To verify the accuracy of the abductive network, regression analysis has been adopted in the paper to develop a second prediction model for surface roughness and cutting force. Comparison of the two models indicates that the prediction model developed by the abductive network is more accurate than that by regression analysis. Experimental results are provided to confirm the effectiveness of this approach.

Modelling and optimization of turning processes for slender parts

In this paper, an abductive network is adopted in order to construct a prediction model for surface roughness and error-of-roundness in the turning operation of slender parts. The abductive network is composed of a number of functional nodes. These functional nodes are self-organized to form an optimal network architecture by using a predicted square error (PSE) criterion. Once the process parameters (workpiece length L, spindle speed n, feed rate f and depth of cut t) are given, the surface roughness and error-of-roundness can be predicted by this developed network. To verify the feasibility of the abductive network, regression analysis has been adopted to develop a second prediction model for surface roughness and error-of-roundness. Comparison of the two models indicates that the prediction model developed by the abductive network is more accurate than regression analysis. It can be found that the use of the abductive network for surface roughness and error-of-roundness is feasible. A simulated annealing optimization algorithm with a performance index is then applied to the developed network for searching the optimal process parameters. The optimal cutting condition can be obtained with the object of maximizing the metal removal rate and minimizing the surface roughness and error-of-roundness to the lowest/smallest extent permissible.

A dynamic surface roughness model for face milling

This paper presents a newly developed mathematical model for surface roughness prediction in a face-milling operation. The model considers the static and the dynamic components of the cutting process. The former includes of cutting conditions as well as the edge profile and the amount of runout of each insert set into a cutter body. The latter introduces the dynamic characteristics of the milling process. It is verified that such a model predicts the maximum or the arithmetic mean surface roughness value through the cutting experiments. The model can evaluate the surface texture of the precision parts machined with face milling.

Surface roughness prediction in the turning of high-strength steel by factorial design of experiments

This paper discusses the development of surface roughness prediction models for turning EN 24T steel (290 BHN) utilising response surface methodology. A factorial design technique has been used to study the effects of the main cutting parameters such as cutting speed, feed, and depth of cut, on surface roughness. The tests have been carried out using uncoated carbide inserts without any cutting fluid. A first-order prediction model within the speed range of 36–117 m min −1 and a second-order model covering the speed range of 28–150 m min −1 have been presented. The results reveal that response surface methodology combined with factorial design of experiments is a better alternative to the traditional one-variable-at-a-time approach for studying the effects of cutting variables on responses such as surface roughness and tool life. This significantly reduces the total number of experiments required.

Deterioration modelling for lateritic-base flexible pavements

Pavement roughness prediction models are generally simplifications of the actual relationships because of the complexity associated with the interaction between the various factors that affect deterioration. Models for roughness progression for flexible pavements using simplified incremental algorithms with actual field data of primary variables are presented. The field data used is obtained for flexible pavements with lateritic gravel bases and subbases with surface treatment as wearing course in Ghana, West Africa. The data covered major primary and secondary highways carrying a wide spectrum of traffic loading. The results indicate that environmental factors and structural capacity have significant influence on roughness progression. The strength of the pavement has a greater influence than the traffic loading on roughness progression, other factors remaining the same. Restoring the structural capacity of flexible pavements through timely maintenance intervention may help arrest the rate of deterioration. Direct transferability of models between different environmental, physical and operating conditions has its limitations and is not advisable.

Surface roughness prediction model in end milling of Al/SiCp MMC by carbide tools

The advancement in automation and accuracy of machine tool made it possible to produce high quality industrial products. One of the main perceptions of quality in mechanical products is its physical appearance. One of the most important factors in physical appearance is the surface roughness. A number of research publications addressed this issue of surface roughness measurement and analyses. This research focuses on study and analyses of surface quality improvement in end milling operation of Al/SiCp metal matrix composite. These materials are selected as they are most widely used in automobile and aerospace industry. This research paper develops an improved mathematical model for surface roughness (Ra) prediction in end milling of Al/SiCp MMC. The impacts of spindle speed, feed rate, depth of cut and various percentage weight of silicon carbide are studied on surface roughness. The result obtained using Response Surface Methodology (RSM) gives a good prediction of surface roughness when compared with actual surface roughness.