Improved measure of workpiece surface deterioration during turning using non-contact vision method

The surface finish quality of a machined workpiece is commonly measured using the average roughness parameter, R_a. This parameter, however, is insensitive to the lateral changes undergone by the surface in the feed direction as a consequence of tool wear. In this work, the effectiveness of four methods of workpiece surface analysis, namely autocorrelation, cross-correlation, and two new methods, called lateral material shift (LMS) ratio and profile slope ratio (PSR) analyses are investigated. Dry machining experiments were carried out on 316 stainless steel. Images of tool nose and workpiece profiles were captured using digital camera, and the edges were extracted using sub-pixel edge detection. In the autocorrelation approach, each workpiece profile was correlated with a shifted version of the same profile. In the cross-correlation approach, the workpiece profiles at different stages of machining were correlated with a reference profile generated using the unworn tool edge. In the LMS ratio method, the material shift ratios were determined from each waveform on the workpiece profile at various stages of tool wear, while in the PSR method the slopes at the right and left part of the waveform were compared. Among the four methods, the LMS ratio method produced the best correlation with tool flank wear with the maximum R-squared value of 0.9461, while average roughness R_a showed no correlation at all with both major and nose flank wear.     

Prediction of surface roughness of turned surfaces using neural networks

In this study, the prediction of surface roughness heights R_a and R_t of turned surfaces was carried out using neural networks with seven inputs, namely, tool insert grade, workpiece material, tool nose radius, rake angle, depth of cut, spindle rate, and feed rate. Coated carbide, polycrystalline and single crystal diamond inserts were used to conduct 304 turning experiments on a lathe, and surface roughness heights of the turned surfaces were measured. A systematic approach to obtain an optimal network was employed to consider the effects of network architecture and activation functions on the prediction accuracy of the neural network for this application. The reliability of the optimized neural network was further explored by predicting the roughness of surfaces turned on another lathe, and the results proved that the network was equally effective in predicting the R_a and R_t values of the surfaces machined on this lathe as well.     

Predicting surface roughness of hardened AISI 1040 based on cutting parameters using neural networks and multiple regression

In this study, models for predicting the surface roughness of AISI 1040 steel material using artificial neural networks (ANN) and multiple regression (MRM) are developed. The models are optimized using cutting parameters as input and corresponding surface roughness values as output. Cutting parameters considered in this study include cutting speed, feed rate, depth of cut, and nose radius. Surface roughness is characterized by the mean (R _a) and total (R _t) of the recorded roughness values at different locations on the surface. A total of 81 different experiments were performed, each with a different setting of the cutting parameters, and the corresponding R _a and R _t values for each case are measured. Input–output pairs obtained through these 81 experiments are used to train an ANN is achieved at the 200,00th epoch. Mean squared error of 0.002917120% achieved using the developed ANN outperforms error rates reported in earlier studies and can also be considered admissible for real-time deployment of the developed ANN algorithm for robust prediction of the surface roughness in industrial settings.     

Effect of the process parameters on the three-dimensional shape of molten pool during full-penetration laser welding process

Slow tool servo (STS) turning is superior in machining precision and in complicated surface. However, STS turning is a complex process in which many variables can affect the desired results. This paper focuses on surface roughness prediction in lenses STS turning. An exponential model, based on the five main cutting parameters including tool nose radius, feed rate, depth of cut, C-axis speed, and discretization angle, for surface roughness prediction of lenses is developed by means of orthogonal experiment regression analysis. Meanwhile, a prediction model of surface roughness based on least squares support vector machines (LS-SVM) with radial basis function is constructed. Orthogonal experiment swatches are studied, and chaotic particle swarm optimization and leave-one-out cross-validation are applied to determine the model parameters. The comparison of LS-SVM model and exponential model is also carried out. Predictive LS-SVM model is found to be capable of better predictions for surface roughness and has absolute fraction of variance R² of 0.99887, the mean absolute percent error e_M of 8.96 %, and the root mean square error e_R of 10.68 %. The experimental results and prediction of LS-SVM model show that effects of tool nose radius and feed rate are more significant than that of depth of cut on surface roughness of lenses turning.     

Toroidal versus ball nose and flat bottom end mills

This paper compares the surface roughness along and across the feed directions produced by toroidal, ball nose, and flat bottom end mills. The study is conducted numerically and by cutting tests of aluminium. The results show that the toroidal cutter inherits the merits of the other two cutters; it produces small scallops across the feed direction, and low roughness along the feed direction.Nomenclature h scallop height - R _s radius of curvature of surface - inclination angle - 2a _c cross-feed - 2 subtended angle between the point of contact on the tool profile and the surface - R _a surface roughness - e offset distance of insert from tool axes for toroidal cutter - r _c cutter radius - r _i radius of insert for toroidal cutter - f _t feed per tooth - h _u undercut height - y, , intermediate variables     

Experimental observations on surface roughness, chip morphology, and tool wear behavior in machining Fe-based amorphous alloy overlay for remanufacture

Fe-based amorphous alloy, a new-type material, was developed as a special-purpose welt overlay for remanufacture. It was deposited on the worn-out part for resuming and upgrading part performance. The microstructure characteristics of the overlay was characterized, including microstructure, phase composition, thermostability, and microhardness. In order to get a comprehensive insight to the machining process of amorphous overlay, this paper presents an experimental investigation into the effect of various machining parameters and tool geometry (Edge) on the surface roughness, tool wear, chip morphology, and surface damage. Comparing larger rake angle of 15°and smaller nose radius of 0.4 mm with 5° and 0.8 mm at the same cutting parameters, we found that larger rake angle of 15° and smaller nose radius of 0.4 mm increased the R _a surface roughness parameter. In the tests, crater wear was not observed, and the friction and wear on the minor cutting edge wear were heavy due to the spring back of the machined surface. In brief,abrasion, adhesion, fatigue, and chipping are the main wear mechanism. As the feed rate reduced and the depth of cut increased (from feed rate?=?0.06 mm/rev and depth of cut?=?0.3 mm to feed rate?=?0.09 mm/rev and depth of cut?=?0.2 mm), a number of physical changes occurred in the chip including reduced distance between serrations, increased shear band angle, and changed chip morphology from spiral to ribbon shape. The results show that strain and strain rate rises in the chips’ inside with the increase in cutting temperature. When the thermal softening exceeded strain hardening, the shear resistance decreased rapidly. Thus, the free surface of the chip presents the nodular and lamella structure. It was noted that specimens generated by larger rake angle of 15° and smaller nose radius of 0.4 mm showed poor surface roughness as well as extensive surface damage.     

Assessment of flank wear and nose radius wear from workpiece roughness profile in turning operation using machine vision

Although literature on the measurement of flank wear and crater wear in single-point turning tools using machine vision is well documented, the study on the effect of nose radius wear on the roughness profile and dimensional changes of workpiece is less explored. The measurement of flank wear using the 2-D profile of the tool nose region or the roughness profile of the workpiece has also not been attempted in the past. In this work, the nose radius wear of cutting tools and roughness profile of turned parts in a lathe operation were measured using the machine vision method. The flank wear width VB_C in the nose area was determined from the nose radius wear using the tool setup and machining geometry. The nose radius wear was also determined from the roughness profile of the workpiece and used in calculating the flank wear width. Comparison between the maximum flank wear width VB_Cmax determined from the roughness profile and that obtained using a toolmaker’s microscope showed a mean deviation of 5.5%. This result indicates that flank wear can be determined fairly accurately from the workpiece roughness profile if the tool and machining geometry are known.     

Surface roughness measurement in turning carbon steel AISI 1045 using wiper inserts

Surface roughness of the workpiece is an important parameter in machining technology. Wiper inserts have emerged as a significantly class of cutting tools, which are increasingly being utilized in last years. This study considers the influence of the wiper inserts when compared with conventional inserts on the surface roughness obtained in turning. Experimental studies were carried out for the carbon steel AISI 1045 because of its great application in manufacturing industry. Surface roughness is represented by different amplitude parameters (R_a, R_zD, R_3z, R_q, R_t, R_a/R_q, R_q/R_t, R_a/R_t). With wiper inserts and high feed rate it is possible to obtain machined surfaces with R_a < 0.8 μm (micron). Consequently it is possible to get surface quality in workpiece of mechanics precision without cylindrical grinding operations.     

Investigation for optimal parametric combination for achieving better surface finish during turning of Al/SiC-MMC

This paper presents an experimental investigation of the influence of cutting conditions on surface finish during turning of Al/SiC-MMC. In this study, the Taguchi method, a powerful tool for experiment design,is used to optimise cutting parameters for effective turning of Al/SiC-MMC using a fixed rhombic tooling system. An orthogonal L₂₇(3¹³) array is used for 3³ factorial design and analysis of variance (ANOVA) is employed to investigate the influence of cutting speed, feed and depth of cut on the surface roughness height R _a and R _t respectively. The influence of the interaction of cutting speed/feed on the surface roughness height R _a and R _t and the effect of cutting speed on cutting speed/feed two factor cell total interaction for surface roughness height R _a and R _t are analysed through various graphical representations. Taking significant cutting parameters into consideration and using multiple linear-regression, mathematical models relating to surface roughness height R _a and R _t are established to investigate the influence of cutting parameters during turning of Al/SiC-MMC. Confirmation test results established the fact that the mathematical models are appropriate for effectively representing machining performance criteria, e.g. surface roughness heights during turning of Al/SiC-MMC.     

Comparative assessment of cooling conditions,including MQL technology on machining factors in an environmentally friendly approach

Manufacturers need to continuously improve productivity and reduce the most disadvantages. In the current work, an experimental study has been carried out in order to evaluate the influence of different cutting parameters on the various machining factors such as surface roughness, cutting force, cutting power, metal removal rate, and tool wear during turning of X210Cr12 steel using a multilayer-coated tungsten carbide insert with various nose radii (r). Tests are designed according to Taguchi’s L₁₈ (2¹ × 3⁴) orthogonal array. ANOVA has been performed to determine the effect of the cutting conditions, and mathematical models have been developed through response surface methodology (RSM). The results indicate that the feed rate and the tool nose radius are the main affecting factors on surface roughness while both tangential force and cutting power are affected mainly by the depth of cut followed by the feed rate and the nose radius. Other special tests of long term have been established in order to study the wear evolution and consequently to determine the tool life. The results indicate also that minimum quantity lubrication (MQL) leads to an important improvement in terms of the cutting tool life by a gain of 23~40% compared to wet and dry machining. It has been found that the MQL is an interesting way to minimize lubrication cost and protect operator health and the environment while keeping better machining quality.     

An integrated optimization of cutting parameters and tool path generation in ultraprecision raster milling

This paper provides a new methodology for the integrated optimization of cutting parameters and tool path generation (TPG) based on the development of prediction models for surface roughness and machining time in ultraprecision raster milling (UPRM). The proposed methodology simultaneously optimizes the cutting feed rate, the path interval, and the entry distance in the feed direction to achieve the best surface quality in a given machining time. Cutting tests are designed to verify the integrated optimization methodology. The experimental results show that, in the fabrication of plane surface, the changing of entry distance improves surface finish about 40 nm (R _a ) and 200 nm (R _t ) in vertical cutting and decreases about 8 nm (R _a ) and 35 nm (R _t ) in horizontal cutting with less than 2 s spending extra machining time. The optimal shift ratio decreases surface roughness about 7 nm (R _a ) and 26 nm (R _t ) in the fabrication of cylinder surfaces, while the total machining time only increases 2.5 s. This infers that the integrated optimization methodology contributes to improve surface quality without decreasing the machining efficiency in ultraprecision milling process.     

Surface finish prediction models for precision grinding of silicon

Conventional grinding of silicon substrates results in poor surface quality unless they are machined in ductile mode on expensive ultra-precision machine tools. However, precision grinding can be used to generate massive ductile surfaces on silicon so that the polishing time can be reduced immensely and surface quality improved. However, precision grinding has to be planned with reliability in advance and the process has to be performed with high rates of reproducibility. Therefore, this work reports the empirical models developed for surface parameters R _a, R _max, and R _t with precision grinding parameters, depths of cut, feed rates, and spindle speeds using conventional numerical control machine tools with Box–Behnken design. Second-order models are developed for the surface parameters in relation to the grinding parameters. Analysis of variance is used to show the parameters as well as their interactions that influence the roughness models. The models are capable of navigating the design space. Also, the results show large amounts of ductile streaks at depth of cut of 20?μm, feed rate of 6.25?mm/min, and spindle speed of 70,000?rpm with a 43-nm R _a. Optimization experiments by desirability function generate 37-nm R _a, 400-nm R _max, and 880-nm R _t with massive ductile surfaces.     

Multiple-response optimization of turning machining by the taguchi method and the utility concept using uni-directional glass fiber-reinforced plastic composite and carbide (k10) cutting tool

This paper presents a utility concept for multi-response optimization in turning uni-directional glass fiber-reinforced plastics composite using Carbide (K10) cutting tool. The single response optimization resulted in the non-optimization of other responses. The Taguchi method (Orthogonal L₁₈ array) was employed in the experimental work. The process parameters selected for this study were tool nose radius, tool rake angle, feed rate, cutting speed, depth of cut, and cutting environment. Statistically significant parameters were found to simultaneously minimize surface roughness and maximize the material removal rate by ANOVA. The results were further verified by confirmation experiments.     

Influence of eccentricity on roughness distributions in side milling

In this paper, a numerical model was used based on the tool-piece geometric intersection, which enables the determination of a roughness profile as a function of feed, tool radii and tool eccentricity in side milling processes. Average roughness R_a and peak-to-valley roughness R_t were obtained for a group or family of tools, defined by an average radius value and a standard deviation value of all tool radii. The Monte Carlo method was used to generate N random combinations of radius values for each family, according to a normal distribution. The model was validated by means of experimental tests. For each family of tools, roughness distributions were obtained at different feed values and for different eccentricity values. It was found that the higher eccentricity, the more asymmetrical roughness distributions become.     

The study of machined surface roughness by random analysis

The effects of various parameters on surface roughness were studied by measuring R_a (c.l.a. value) or R_t (peak-to-valley height). The effect of cutting speed, feed, rake angle and depth of cut on the randomness of the surface profile were studied from the auto-correlation function of the surface profile.     

Investigation of machining parameters for the multiple-response optimization of micro electrodischarge milling

This paper investigated the influence of three micro electrodischarge milling process parameters, which were feed rate, capacitance, and voltage. The response variables were average surface roughness (R _a ), maximum peak-to-valley roughness height (R _y ), tool wear ratio (TWR), and material removal rate (MRR). Statistical models of these output responses were developed using three-level full factorial design of experiment. The developed models were used for multiple-response optimization by desirability function approach to obtain minimum R _a , R _y , TWR, and maximum MRR. Maximum desirability was found to be 88%. The optimized values of R _a , R _y , TWR, and MRR were 0.04, 0.34 μm, 0.044, and 0.08 mg min^?1, respectively for 4.79 μm s^?1 feed rate, 0.1 nF capacitance, and 80 V voltage. Optimized machining parameters were used in verification experiments, where the responses were found very close to the predicted values.     

Modeling and prediction for 3D surface topography in finish turning with conventional and wiper inserts

Wiper insert have the characteristics of achieving an excellent surface finish and improving the productivity in turning processes. Wiper insert can provide twice feed rate while maintaining the comparable surface roughness compared to that provided by the conventional insert. In the present study, surface topographies in finish turning with conventional and wiper inserts are investigated. The key element of this work is that the cutting edge path equation in the cutting tool coordinate system is transformed into the machine tool and workpiece coordinate system by the use of spatial coordinate transformation. Following that a surface topography simulation algorithm based on the cutting edge path equation and cutting parameters is put forward. The output of this work is that both the simulated surface topography and surface roughness profile are good agreement with the experimental results. Both the simulated and the actual machined surface results show that better surface topography is obtained in finish turning with the wiper insert than that with conventional insert. Burnishing effect of the wiper insert leads to half decrease of the Ra and Rz. The actual surface profiles are no longer regular wave shapes due to ploughing effect and side flow existing in the cutting zone. In addition, a surface roughness map has also been developed to optimize the selection of wiper radius and feed rate to satisfy the requirement of surface finishing with higher productivity. From the viewpoint of cutting tool design, the wiper radius with five times larger than tool nose radius can fully come into its role. This provides a novel insight into the design of wiper insert over conventional techniques. Above all, the proposed model gives a better prediction of surface roughness in finish turning process compared to the previous empirical and regression roughness models. The prediction of surface roughness in finish turning with wiper insert is also realized.     

Modeling and optimization of Al/SiCp MMC machining using Taguchi approach

This paper presents the influence of process parameters like cutting speed, feed and depth of cut on flank wear (VBc) and surface roughness (Ra) in turning Al/SiCp metal matrix composites using uncoated tungsten carbide insert under dry environment. The experiments have been conducted based on Taguchi’s L₉ orthogonal array. Abrasion and adhesion are observed to be the principal wear mechanism from images of tool tip. No premature tool failure by chipping and fracturing was observed and machining was steady using carbide insert. Built-up-edge formation is noticed at low and higher cutting speed and at high feed combination and consequently surface quality affected adversely. The optimal parametric combination for flank wear and surface roughness are found to be v1–f1–d3 and v3–f1–d3 respectively and is greatly improved through Taguchi approach. Mathematical models for flank wear and surface roughness are found to be statistically significant.     

Using specially designed high-stiffness burnishing tool to achieve high-quality surface finish

This paper is focused on the process of ball burnishing. The influence of tool stiffness on surface roughness parameters was considered theoretically, while experimental investigation was conducted to establish the influence of initial surface roughness (previous machining) on the effects of ball burnishing as the finishing process. Experimental investigations were conducted over a wide interval of most influential process parameters (burnishing forces, burnishing feed, and number of burnishing passes). The material used in the experiments was aluminum alloy EN AW-6082 (AlMgSi1) T651. Burnishing was performed using a specially designed tool of high stiffness. Statistical analysis of experimental data revealed strong correlation between roughness, R _a, and burnishing force, burnishing feed, and number of passes for the three surfaces, each with different roughness parameters. Particular combinations of process parameters yielded very low surface roughness, R _a, equivalent to polishing. It is worth noting that high surface quality can be achieved with relatively small burnishing forces, which differs from the investigations published so far. Contrary to conventional approaches, which are based on elastic tool systems, the authors propose the burnishing process to be conducted with high-stiffness tools. Further investigation shall be focused on optimization of burnishing process parameters in order to achieve surface finish equivalent to high polish.     

The effect of feed rate and of tool nose radius on the roughness of oblique finish turned surfaces

The influence of feed rate and tool nose radius on surface roughness in oblique finish turning of a carbon steel is studied for both sharp and worn cutting tools. A comparison between the experimentally determined values of surface roughness and the theoretical values computed from eqns. (1) and (2) is performed. Linear regression analysis is applied to the experimental data obtained in an attempt to establish the laws relating surface roughness to feed rate and to the expression $\text{s}{}^2\text{R}$, for roughness prediction purposes.