On the prediction of surface roughness in the hard turning based on cutting parameters and tool vibrations

This research work concerns the elaboration of a surface roughness model in the case of hard turning by exploiting the response surface methodology (RSM). The main input parameters of this model are the cutting parameters such as cutting speed, feed rate, depth of cut and tool vibration in radial and in main cutting force directions. The machined material tested is the 42CrMo4 hardened steel by Al₂O₃/TiC mixed ceramic cutting tool under different conditions. The model is able to predict surface roughness of Ra and Rt using an experimental data when machining steels. The combined effects of cutting parameters and tool vibration on surface roughness were investigated while employing the analysis of variance (ANOVA). The quadratic model of RSM associated with response optimization technique and composite desirability was used to find optimum values of cutting parameters and tool vibration with respect to announced objectives which are the prediction of surface roughness. The adequacy of the model was verified when plotting the residuals values. The results indicate that the feed rate is the dominant factor affecting the surface roughness, whereas vibrations on both pre-cited directions have a low effect on it. Moreover, a good agreement was observed between the predicted and the experimental surface roughness. Optimal cutting condition and tool vibrations leading to the minimum surface roughness were highlighted.     

On-line surface roughness recognition system using artificial neural networks system in turning operations

In modern manufacturing environments, the quality assurance of machined parts has attracted great attention from manufacturers. The surface roughness of a workpiece is one of the most important factors to consider. The need for developing a surface recognition system that is able to replace stylus-style surface measuring systems has increased to improve the efficiency of production. In this research an on-line surface recognition system was developed based on artificial neural networks (OSRR-ANN) using a sensing technique to monitor the effect of vibration produced by the motions of the cutting tool and workpiece during the cutting process. Different combinations of cutting conditions were conducted to develop an OSRR system for a lathe. In order to determine the direction of the vibration which most significantly affects surface roughness, a triaxial accelerometer was employed. Three directional vibrations which were detected simultaneously by the accelerometer were analyzed using a statistical method. The radial direction vibration was found to be the most significant vibration in turning operations. The accuracy of the developed systems showed that the developed system could predict surface roughness efficiently. The developed system not only proposes a surface recognition system which is alternative to that using a traditional measurement instrument, but also provides an on-line surface recognition system for turning operations.     

Analysis and prediction of tool wear, surface roughness and cutting forces in hard turning with CBN tool

The main of the present study is to investigate the effects of process parameters (cutting speed, feed rate and depth of cut) on performance characteristics (tool life, surface roughness and cutting forces) in finish hard turning of AISI 52100 bearing steel with CBN tool. The cutting forces and surface roughness are measured at the end of useful tool life. The combined effects of the process parameters on performance characteristics are investigated using ANOVA. The composite desirability optimization technique associated with the RSM quadratic models is used as multi-objective optimization approach. The results show that feed rate and cutting speed strongly influence surface roughness and tool life. However, the depth of cut exhibits maximum influence on cutting forces. The proposed experimental and statistical approaches bring reliable methodologies to model, to optimize and to improve the hard turning process. They can be extended efficiently to study other machining processes.     

Determining the effect of cutting parameters on surface roughness in hard turning using the Taguchi method

This study focuses on optimizing turning parameters based on the Taguchi method to minimize surface roughness (Ra and Rz). Experiments have been conducted using the L9 orthogonal array in a CNC turning machine. Dry turning tests are carried out on hardened AISI 4140 (51 HRC) with coated carbide cutting tools. Each experiment is repeated three times and each test uses a new cutting insert to ensure accurate readings of the surface roughness. The statistical methods of signal to noise ratio (SNR) and the analysis of variance (ANOVA) are applied to investigate effects of cutting speed, feed rate and depth of cut on surface roughness. Results of this study indicate that the feed rate has the most significant effect on Ra and Rz. In addition, the effects of two factor interactions of the feed rate-cutting speed and depth of cut-cutting speed appear to be important. The developed model can be used in the metal machining industries in order to determine the optimum cutting parameters for minimum surface roughness.     

Self-excited vibrations in turning: cutting moment analysis

This work aims at analyzing the moment effects at the tool tip point and at the central axis, in the framework of a turning process. A testing device in turning, including a six-component dynamometer, is used to measure the complete torsor of the cutting actions in the case of self-excited vibrations. Many results are obtained regarding the mechanical actions torsor. A confrontation of the moment components at the tool tip and at the central axis is carried out. It clearly appears that analyzing moments at the central axis avoids the disturbances induced by the transport of the moment of the mechanical actions resultant at the tool tip point. For instance, the order relation between the components of the forces is unic. Furthermore, the order relation between the moments components expressed at the tool tip point is also unic and the same one. But at the central axis, two different order relations regarding moments are conceivable. A modifictation in the rolling moment localization in the (y, z) plan is associated to these two order relations. Thus, the moments components at the central axis are particularly sensitive at the disturbances of machining, here the self-excited vibrations.     

In-process surface roughness measurement of bulk metallic glass using an adaptive optics system for aberration correction

Bulk metallic glasses (BMGs) have received extensive attention recently due to amorphous-related extraordinary properties such as high strength, elasticity, and excellent corrosion resistance. In particular, Zr-based BMGs are recognized as a biocompatible material and surface roughness may affect many aspects of cell attachment, proliferation and differentiation. Therefore, this study presents an in-process measurement of surface roughness by combining an optical probe of laser-scattering phenomena and adaptive optics (AO) for aberration correction. Measurement results of six Zr-based BMGs samples with a roughness ranging from 0.06 to 0.98 μm demonstrate excellent correlation between the peak power and average roughness with a determination coefficient (R²) of 0.9974. The proposed adaptive-optics-assisted (AO-assisted) system is in good agreement with the stylus method, and less than 8.42% error values are obtained for average sample roughness in the range of 0.05–0.58 μm. The proposed system can be used as a rapid in-process roughness monitor/estimator to further increase the precision and stability of manufacturing processes for all classes of BMGs materials in situ.     

Surface roughness and cutting force prediction in MQL and wet turning process of AISI 1045 using design of experiments

This paper presents an investigation into the MQL (minimum quantity lubrication) and wet turning processes of AISI 1045 work material with the objective of suggesting the experimental model in order to predict the cutting force and surface roughness, to select the optimal cutting parameters, and to analyze the effects of cutting parameters on machinability. Fractional factorial design and central composite design were used for the experiment plan. Cutting force and surface roughness according to cutting parameters were measured through the external cylindrical turning based on the experiment plan. The measured data were analyzed by regression analysis and verification experiments were conducted to confirm the results. From the experimental results and regression analysis, this research project suggested the experimental equations, proposed the optimal cutting parameters, and analyzed the effects of cutting parameters on surface roughness and cutting force in the MQL and wet turning processes.     

Experimental investigation of surface roughness and cutting ratio in a spraying cryogenic turning process

Abstract

Surface roughness is one of the most common criteria indicating the surface finish of the part, which depends on various factors including cutting parameters, geometry of the tool, and cutting fluid. One of the goals of using cutting fluids in machining processes is to achieve improved surface finish. In addition to high costs, commonly used cutting fluids cause dermal and respiratory problems to the operators as well as environmental pollution. The present article aims at investigating the effect of spray cryogenic cooling via liquid nitrogen on surface roughness and cutting ratio in turning process of AISI 304 stainless steel. Through conducting experimental tests, the effects of cutting speed, feed rate, and depth of cut on surface roughness and cutting ratio have been compared in dry and cryogenic turning. A total number of 72 tests have been carried out. Results show that cryogenic turning of AISI 304 stainless steel reduces surface roughness 1%–27% (13% on the average), compared to dry turning. The obtained results showed that the cutting ratio in cryogenic turning is averagely increased by 32% in comparison with dry turning, also that chip breakage is improved in cryogenic turning.     

A novel approach for prediction of surface roughness in turning of EN353 steel by RVR-PSO using selected features of VMD along with cutting parameters

The abrupt changes in tool-workpiece interaction during machining process induce variation in the surface quality of work material. These interactions include built-up edge formation and their break-off, environmental conditions (use of coolant, rise of temperature etc.), material imperfections, improper structural fitness of machine & tool components, etc. This study presents prediction of surface roughness in turning of EN353 steel implementing the variational mode decomposition (VMD) for processing the vibration data, followed by estimation of the surface roughness using the relevance vector regression (RVR) optimized by particle swarm optimization (PSO). The raw vibration data has been decomposed in five discrete sets of frequency components known as variational mode functions (VMFs). A set of twenty-one statistical features in each three axes have been extracted for raw data and each VMF. The RVR has been trained using these 21×3 = 63 features and 3 cutting parameters — cutting speed, feed depth of cut. The RVR has also been trained separately using top 5 features selected through RreliefF algorithm. The optimal decomposition level has been determined to minimize the noise and predict the surface finish accurately. The results obtained in 1st VMF (high frequency, low amplitude) using its top 5 features for prediction have been found to be reliable with higher prediction accuracy.

     

A neural-network-based methodology for the prediction of surface roughness in a turning process

A neural-network-based methodology is proposed for predicting the surface roughness in a turning process by taking the acceleration of the radial vibration of the tool holder as feedback. Upper, most likely and lower estimates of the surface roughness are predicted by this method using very few experimental data for training and testing the network. The network model is trained using the back-propagation algorithm. The learning rate, the number of neurons in the hidden layer, the error goal, as well as the training and the testing dataset size, are found automatically in an adaptive manner. Since the training and testing data are collected from experiments, a data filtration scheme is employed to remove faulty data. The validation of the methodology is carried out for dry and wet turning of steel using high speed steel and carbide tools. It is observed that the present methodology is able to make accurate prediction of surface roughness by utilising small sized training and testing datasets.     

In-process measurement of surface roughness during cylindrical grinding process

An in-process surface roughness sensor developed by the author has been applied to cylindrical grinding operations. The sensor utilizes fibre optics to illuminate a workpiece surfacec and to detect the intensity of the reflected light. A change of surface roughness during one plunge grinding cycle is measured for various grinding conditions. It is confirmed by the measurements that the surface roughness is closely related to changes of the workpiece radius during a grinding cycle. The result is quite simple and it is useful in determining the cycle time of a grinding operation.     

Surface roughness prediction in turning of femoral head

This paper is dealing with the development of a surface roughness model for turning of femoral heads from AISI 316L stainless steel. The model is developed in terms of cutting speed, feed rate and depth of cut, using response surface methodology. Machining tests were carried out with TiN–Al₂O₃–TiC-coated carbide cutting tools under various conditions. First-order and second-order models predicting equations for surface roughness have been established by using the experimental results. The established equation shows that the depth of cut was the main influencing factor on the surface roughness. It increased with increasing the depth of cut and feed rate, respectively, but it decreased with increasing the cutting speed. In addition, analysis of variance for the second-order model shows that the interaction terms and the square terms are statistically insignificant. The predicted surface roughness of the samples was found close to the experimentally obtained results within a 95% confident interval.     

Analytical study of surface roughness in turning

Instruments for the evaluation of the surface finish of a machined part are sophisticated and costly, so theoretical equations which can predict approximately the magnitude of surface roughness under given cutting conditions are required.An analytical study of the roughness on surfaces turned by a singlepoint tool is presented. The effect of tool wear is also taken into account. A new technique is proposed by which the level of finish, conventionally obtained through a finishing cut after a roughing cut, can be improved. Alternatively, this technique, which is based on the principle of overlapping of cuts, can reduce the machining time in comparison with that required for the conventional roughing and finishing cut method to achieve a desired level of finish.     

Optimisation of surface roughness on turning fibre-reinforced plastics (FRPs) with diamond cutting tools

Fibre-reinforced plastics (FRPs) are used in structural components in various fields of application of mechanical engineering, such as automobile, biomechanics and aerospace industries. Their own properties, particularly the high strength and stiffness and simultaneously low weight, allows the substitution of the metallic materials in many cases. As a result of these properties and potential applications, exist a great necessity to investigate the machining of these composite materials.This paper presents an optimisation study of surface roughness in turning FRPs tubes manufacturing by filament winding and hand lay-up, using polycrystalline diamond cutting tools. A plan of experiments was performed with cutting parameters prefixed in the FRP tubes. The objective was establishing the optimal cutting parameters to obtain a certain surface roughness (R_a and R_t/R_max), corresponding to international dimensional precision (ISO) IT7 and IT8 in the FRP workpieces, using multiple analysis regression (MRA). Additionally, the optimal material removal rates have been obtained.     

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.     

Analysis of surface roughness and cutting force components in hard turning with CBN tool: Prediction model and cutting conditions optimization

In this study, the effects of cutting speed, feed rate, workpiece hardness and depth of cut on surface roughness and cutting force components in the hard turning were experimentally investigated. AISI H11 steel was hardened to (40; 45 and 50) HRC, machined using cubic boron nitride (CBN 7020 from Sandvik Company) which is essentially made of 57% CBN and 35% TiCN. Four-factor (cutting speed, feed rate, hardness and depth of cut) and three-level fractional experiment designs completed with a statistical analysis of variance (ANOVA) were performed. Mathematical models for surface roughness and cutting force components were developed using the response surface methodology (RSM). Results show that the cutting force components are influenced principally by the depth of cut and workpiece hardness; on the other hand, both feed rate and workpiece hardness have statistical significance on surface roughness. Finally, the ranges for best cutting conditions are proposed for serial industrial production.     

Multi-objective optimization of surface roughness and cutting forces in high-speed turning of Inconel 718 using Taguchi grey relational analysis (TGRA)

In this paper, a new effective approach, Taguchi grey relational analysis has been applied to experimental results in order to optimize the high-speed turning of Inconel 718 with consideration to multiple performance measures. The approach combines the orthogonal array design of experiments with grey relational analysis. Grey relational theory is adopted to determine the best process parameters that give lower magnitude of cutting forces as well as surface roughness. The response table and the grey relational grade graph for each level of the machining parameters have been established. The parameters: cutting speed, 475?m/min; feed rate, 0.10?mm/rev; depth of cut, 0.50?mm; and CW2 edge geometry have highest grey relational grade and therefore are the optimum parameter values producing better turning performance in terms of cutting forces and surface roughness. Depth of cut shows statistical significance on overall turning performance at 95% confidence interval.