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.     

Measurement of nose radius wear in turning tools from a single 2D image using machine vision

Flank and crater wear in cutting tools have been extensively studied in the past using direct and indirect methods. In finish and hard turning, however, nose radius wear plays a greater role in determining the surface quality of the finished product. This is due to the direct interaction between the tool nose area and the workpiece during machining. Nose radius wear can be measured from the 2D profiles of the tool before and after machining using a vision method. This approach, however, requires two images of cutting tools to be recorded and precisely aligned before subtraction. This work proposes a new method of measuring nose wear area from a single 2D image of the worn tool. The nose profile is extracted in the Cartesian coordinates and converted to a polar-radius plot. From the plot, the nose wear area is determined by simple subtraction.     

超精密车削表面微观形貌的几何建模与仿真研究

在综合考虑刀具几何参数，刀具振动和最小切削厚度等因素对已加工表面形貌影响的前提下编写了表面微观形貌的仿真程序，在仿真时把一个随机振动信号成功地叠加在理论表面粗糙度中。提出一种建立圆弧刃金刚石车刀超精密车削表面粗糙度模型的新方法。结果表明，仿真得到的表面微观形貌，能够比较正确地反映出超精密加工将要获得的表面轮廓。     

In-cycle monitoring of tool nose wear and surface roughness of turned parts using machine vision

Tool wear has been extensively studied in the past due to its effect on the surface quality of the finished product. Vision-based systems using a CCD camera are increasingly being used for measurement of tool wear due to their numerous advantages compared to indirect methods. Most research into tool wear monitoring using vision systems focusses on off-line measurement of wear. The effect of wear on surface roughness of the workpiece is also studied by measuring the roughness off-line using mechanical stylus methods. In this work, a vision system using a CCD camera and backlight was developed to measure the surface roughness of the turned part without removing it from the machine in-between cutting processes, i.e. in-cycle. An algorithm developed in previous work was used to automatically correct tool misalignment using the images and measure the nose wear area. The surface roughness of turned parts measured using the machine vision system was verified using the mechanical stylus method. The nose wear was measured for different feed rates and its effect on the surface roughness of the turned part was studied. The results showed that surface roughness initially decreased as the machining time of the tool increased due to increasing nose wear and then increased when notch wear occurred.     

Image processing for chatter identification in machining processes

Identifying chatter or intensive self-excited relative tool–workpiece vibration is one of the main challenges in the realization of automatic machining processes. Chatter is undesirable because it causes poor surface finish and machining accuracy, as well as reducing tool life. The identification of chatter is performed by evaluating the surface roughness of a turned workpiece undergoing chatter and chatter-free processes. In this paper, an image-processing approach for the identification of chatter vibration in a turning process was investigated. Chatter is identified by first establishing the correlation between the surface roughness and the level of vibration or chatter in the turning process. Images from chatter-free and chatter-rich turning processes are analyzed. Several quantification parameters are utilized to differentiate between chatter and chatter-free processes. The arithmetic average of gray level G _a is computed. Intensity histograms are constructed and then the variance, mean, and optical roughness parameter of the intensity distributions are calculated. The surface texture analysis is carried out on the images using a second-order histogram or co-occurrence matrix of the images. Analysis is performed to investigate the ability of each technique to differentiate between a chatter-rich and a chatter-free process. Finally, a machine vision system is proposed to identify the presence of chatter vibration in a turning process.     

Optimization of tool geometry parameters for turning operations based on the response surface methodology

This investigation focuses on the influence of tool geometry on the surface finish obtained in turning of AISI 1040 steel. In order to find out the effect of tool geometry parameters on the surface roughness during turning, response surface methodology (RSM) was used and a prediction model was developed related to average surface roughness (Ra) using experimental data. The results indicated that the tool nose radius was the dominant factor on the surface roughness. In addition, a good agreement between the predicted and measured surface roughness was observed. Therefore, the developed model can be effectively used to predict the surface roughness on the machining of AISI 1040 steel with in 95% confidence intervals ranges of parameters studied.     

Tool vibration in internal turning of hardened steel using cBN tool

The machining of hardened materials with hardness over 45 HRC has been an alternative to grinding since the 1970s, with the commercial availability of cubic boron nitride (cBN) and ceramic tools. However, the low toughness of these types of tool materials makes them very sensitive to damages caused by vibrations, which are critical for operations like internal turning, where the tool resembles a cantilever beam and therefore is susceptible to large deflections. This work aims to contribute to the study of tool performance in internal turning of long holes in hardened AISI 4340 steel in finishing conditions. Different machining conditions, two different tool holders (steel and carbide), and several tool overhangs were tested. The surface finish, acceleration (vibration) signals, and tool wear of cBN inserts were evaluated. The results show that vibration and the material of the tool holder may play a secondary role in the surface finish for stable turning, but the use of carbide tool holders makes the process stable for longer tool overhangs. Moreover, when the cutting becomes unstable, surface roughness is increased severely.     

Notch wear detection in cutting tools using gradient approach and polynomial fitting

Cutting tool wear is well known to affect the surface finish of a turned part. Various machine vision methods have been developed in the past to measure and quantify tool wear. The two most widely measured parameters in tool wear monitoring are flank wear and crater wear. Works carried out by several researchers recently have shown that notch wear has a more severe effect on the surface roughness compared to flank or crater wear. In this work, a novel gradient detection approach has been developed to detect the presence of micro-scale notches in the nose area of the cutting tool. This method is capable of detecting the location of the notch accurately from a single worn cutting tool image.     

Characterization of Tool Wear Measurement with Relation to the Surface Roughness in Turning

In turning, an accurate gauging of tool wear condition is an essential part of process control due to adverse effects on dimensional tolerance and surface finish quality. When the surface roughness is the primary concern, the conventional measure of tool wear is found to be imprecise because it provides very little information on the wear patterns in tool nose and flank. A tool wear model, developed in this study, represents the wear condition more comprehensively and accurately with relation to the surface roughness. Experimental results validate the model, showing 92% accuracy between the predicted surface roughness and the actual measurements.     

Analysis of residual stress and work-hardened profiles on Inconel 718 when face turning with large-nose radius tools

Surface integrity (SI) and, particularly, the residual stress profile, has a great influence on the fatigue life of machined aeronautical critical parts. Among the different cutting parameters that affect the final SI, tool geometry is one of the most important factors. In particular, tool nose radius determines the surface roughness, as well as the thermoplastic deformation of the workpiece. Indeed, the use of large tool nose radius in the industry enables (1) increasing the feed rate while keeping the roughness values below specifications and (2) reducing the influence of the tool wear in the surface roughness. Therefore, in this study, the influence of tool nose radius in the induced residual stress profile and work-hardened layer when face turning Inconel 718 is analysed for a cutting speed range between (30–70 m/min) and a feed rate range of (0.15–0.25 mm/rev). For this purpose, residual stress profiles and work-hardened layer were measured by x-ray diffraction method after machining with a 4 mm nose radius. Then, results have been compared against different tool nose radius studies carried out by other authors for the specified working conditions. Results revealed that residual stress profiles varied when machining with different nose radius for the studied range. In particular, the increase of the nose radius brought to a higher difference between surface tensile stress and subsurface compressive peak stress, which is attributed to an increase of the thermal effect. Moreover, thicker work-hardened layer (around 100 μm) was observed when machining with large-nose radius for the studied working conditions.     

Influence of tool wear on surface roughness in hard turning using differently shaped ceramic tools

Hard turning has been applied in many cases in producing bearings, gears, cams, shafts, axels, and other mechanical components since the early 1980s. Mixed ceramics (aluminum oxide plus TiC or TiCN) is one of the two cutting tool materials (apart from PCBN) widely used for finish machining of hardened steel (HRC 50–65) parts, especially under dry machining conditions and moderate cutting speed ranging from 90 to 120 m/min. This paper reports an extensive characterization of the surface roughness generated during hard turning (HT) operations performed with conventional and wiper ceramic tools at variable feed rate and its changes originated from tool wear. Moreover, it compares some predominant tool wear patterns produced on the two types of ceramic inserts and their influence on the alteration of surface profiles. After the hard turning tests, the relevant changes of surface profiles and surface roughness parameters were successively registered and measured by a stylus profilometer. In this investigation, a set of 2D surface roughness parameters, as well as profile and surface characteristics, such as the amplitude distribution functions, bearing area curves and symmetrical curves of geometrical contact obtained for the machined surface, were determined and analyzed. A novel aspect of this research is that the notch wear progress at the secondary cutting (trailing) edges was found to produce the substantial modifications of the individual irregularities, and constitute the altered surface profiles. Moreover, this research contributes to practical aspects of HT technology due to exploring the relations between the tool state at different times within the tool life and the relevant surface roughness characterization.     

In-cycle detection of built-up edge (BUE) from 2-D images of cutting tools using machine vision

The built-up edge (BUE) phenomenon that appears under certain machining condition, such as low-to-moderate cutting speed, high depth of cut, dry cutting, cutting of ductile material, etc. is known to have a major effect on the surface quality of the finished workpiece. In the published literature, BUE has been measured using scanning electron microscope and optical microscopes to study its effect on tool life and surface quality. Such measurement methods are only applicable in off-the-machine inspection. Since the BUE extending beyond the tool nose alters the tool geometry and, thus, influences the workpiece roughness profile, detection of BUE outside the nose region is important. This research proposes a new method for detecting BUE from 2-D images of the nose region of the tool using a machine vision approach. Two methods of determining the BUE area are proposed—the subtraction method and polar-radius transformation method. Application of both methods is successfully demonstrated using simulated and real cutting tool images.     

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.     

Characterization of Tool Wear Measurement with Relation to the Surface Roughness in Turning

Abstract

In turning, an accurate gauging of tool wear condition is an essential part of process control due to adverse effects on dimensional tolerance and surface finish quality. When the surface roughness is the primary concern, the conventional measure of tool wear is found to be imprecise because it provides very little information on the wear patterns in tool nose and flank. A tool wear model, developed in this study, represents the wear condition more comprehensively and accurately with relation to the surface roughness. Experimental results validate the model, showing 92% accuracy between the predicted surface roughness and the actual measurements.     

Improvement of machinability of Waspaloy via laser-assisted machining

Waspaloy is a heat-resistant alloy primarily used in aircraft turbine engines, as forged turbine and compressor disk, which is difficult to machine at room temperature due to excessive tool wear and poor surface finish. Laser-assisted machining (LAM) offers the ability to machine such superalloys more efficiently by locally heating and softening the workpiece material prior to material removal and machining with a conventional single-point cutting tool. A transient, three-dimensional heat transfer model is used for modeling LAM of Waspaloy. The thermal model is validated by comparing the temperature predictions and the surface temperature measurements using an infrared camera. The machinability of Waspaloy under varying conditions is evaluated by examining tool wear, cutting forces, and surface finish. With increasing material removal temperature from room temperature to 300–400°C, the benefit of LAM is demonstrated by a 20% decrease in specific cutting energy, a two- to three-fold improvement in surface roughness, and a 50% increase in ceramic tool life over conventional machining.     

Prediction of cutting force, tool wear and surface roughness of Al6061/SiC composite for end milling operations using RSM

The results of mathematical modeling and the experimental investigation on the machinability of aluminium (Al6061) silicon carbide particulate (SiCp) metal matrix composite (MMC) during end milling process is analyzed. The machining was difficult to cut the material because of its hardness and wear resistance due to its abrasive nature of reinforcement element. The influence of machining parameters such as spindle speed, feed rate, depth of cut and nose radius on the cutting force has been investigated. The influence of the length of machining on the tool wear and the machining parameters on the surface finish criteria have been determined through the response surface methodology (RSM) prediction model. The prediction model is also used to determine the combined effect of machining parameters on the cutting force, tool wear and surface roughness. The results of the model were compared with the experimental results and found to be good agreement with them. The results of prediction model help in the selection of process parameters to reduce the cutting force, tool wear and surface roughness, which ensures quality of milling processes.     

Effect of wedge angle on surface roughness in finish turning: analytical and experimental study

Most of the theoretical models for surface roughness in finish turning assume that the work piece surface profile is formed by the rounded tip of the tool nose. The effect of the straight flank section in the tool nose region on the surface roughness is usually neglected. In this work, the straight flank section is taken into account in order to predict the arithmetic average roughness R _a and root-mean-square roughness R _q more accurately. The analytical models for R _a and R _q are developed as a function of three parameters, namely feed rate, nose radius, and wedge angle. These models were verified using digital simulation method. The surface roughness determined using the new three-parameter models were compared with the existing two-parameter models that consider only the feed rate and nose radius. Decreasing wedge angle was found to lower the surface roughness significantly. An experiment was conducted to test the validity of the three-parameter model at different feed rates in real machining operation. The experimental results agreed more closely with the proposed three-parameter models compared to the two-parameter models.     

Performance studies of multilayer hard surface coatings (TiN/TiCN/Al2O3/TiN) of indexable carbide inserts in hard machining: Part-I (An experimental approach)

In recent years, hard machining using CBN and ceramic inserts became an emerging technology than traditional grinding and widely used manufacturing processes. However the relatively high cost factors associated with such tools has left a space to look for relatively low cost cutting tool materials to perform in an acceptable range. Multilayer coated carbide insert is the proposed alternative in the present study due to its low cost. Thus, an attempt has been made to have an extensive study on the machinability aspects such as flank wear, chip morphology, surface roughness in finish hard turning of AISI 4340 steel (HRC 47 ± 1) using multilayer coated carbide (TiN/TiCN/Al₂O₃/TiN) insert under dry environment. Parametric influences on turning forces are also analyzed. From the machinability study, abrasion and chipping are found to be the dominant wear mechanism in hard turning. Multilayer TiN coated carbide inserts produced better surface quality and within recommendable range of 1.6 μm i.e. comparable with cylindrical grinding. At extreme parametric conditions, the growth of tool wear was observed to be rapid thus surface quality affected adversely. The chip morphology study reveals a more favorable machining environment in dry machining using TiN coated carbide inserts. The cutting speed and feed are found to have the significant effect on the tool wear and surface roughness from ANOVA study. It is evident that, thrust force (Fy) is the largest component followed by tangential force (Fz) and the feed force (Fx) in finish hard turning. The observations yield the machining ability of multilayer TiN coated carbide inserts in hard turning of AISI 4340 steel even at higher cutting speeds.     

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.     

Kinematics prediction and experimental validation of machined surface roughness

Online monitoring of surface roughness is a desirable capability for machining processes; however, 100 % inspection of all parts is not feasible unless it can be integrated into the machining process itself through real-time monitoring of cutting conditions. One strategy is to feed these conditions into a predictive modeling kernel which would in turn give the properties of the finished part. In the case of roughness, the surface resulting from turning can be largely represented as the trace of the passing tool geometry. The question addressed herein is whether computationally intensive modeling of the surface accounting for tool nose radius is necessary for online monitoring of surface roughness. This paper presents a predictive modeling methodology wherein the tool-workpiece contact position varies under a simple cutting model, and the resulting surface roughness is estimated. It presents the concept of calculating a “pseudo-roughness” value based only on tool tip locations and to compare this value to that determined by full predictive modeling of the tool geometry. Cutting experimental data has been presented and compared to predictions for model validation. It is found that the root mean square roughness calculation is dominated by tool geometry, rather than tool position deviations and surface roughness estimation could be implemented without a computationally intensive modeling component, thereby enabling online monitoring and potentially real-time control of the part finish.