Surface Profile Of Workpiece Research Articles

Evolution mechanism of the rough surface in vibratory finishing within restricted space

ABSTRACT Vibratory finishing within restricted space is an effective way to finish the surface of complex cavities in an aero-engine integral casting casing, where the contact mechanism between the processing media and the rough surface is the nature for producing the processing effect. In this work, vibratory finishing was performed by semi-shading the workpiece, comparing the workpiece surface profile change with time for different vibration directions and processing positions, and height parameters and feature parameters evaluated the rough surface. The results show that the process of profile change on the workpiece mainly subjected to normal action is mainly the raising of valleys after profile peaks extrusion deformation and accompanied by the formation of finer peak-to-valleys. The workpiece subjected to tangential action reduces the surface roughness by filling the valleys after cutting the peak tips. The finishing mechanisms for rough surfaces can be summarized as collision, roll-pressing, and scratching.

Material removal characteristics and particle motions in abrasive water jet quantitative machining.

Abrasive water jet quantitative machining (AWJQM) is a method that enables the production of lightweight mirrors. Investigating material removal characteristics and particle motions in AWJQM is necessary to understand its erosion mechanism and improve its processability. A material removal model based on computational fluid dynamics was established in this study by integrating the transition condition of the brittle-ductile mechanism and the single-particle erosion model. Quantitative machining experiments were carried out to verify the material removal model, and experimental data were consistent with the simulation results. Further simulation experiments were carried out to explore the particle distribution under different working pressures or slurry viscosities. Results showed that operating parameters affect the workpiece surface profile by changing the flow field distribution and particle behavior.

Tool wear prediction in turning using workpiece surface profile images and deep learning neural networks

Tool wear prediction in turning using workpiece surface profile images and deep learning neural networks

A new analytical model of ploughing friction for arbitrary grit shape on any unflat surface

To gain better knowledge of the grinding process especially the grinding force and efficiency, the friction characteristics between workpiece and abrasive grains has been investigated extensively. However, the calculation of coefficient of friction (COF) in previous studies is limited to indenters with optimized shapes, e.g. pyramidal shape from Berkovich and Vickers indenter or conical shape from conical indenter. In the kinematic simulation of grinding, the applicability of previous models in estimating the contact area and COF is limited due to the very irregular grit shapes of the grinding product. In this study, a new model for calculating the COF for scratches by arbitrary grit shape on any unflat surface is proposed, which only involves the elastic recovery ratio, the pile-up ratio, the grit profile and the workpiece surface profile prior to scratch. To verify the model, single scratch tests under two different shape indenter were conducted on a Nanoindenter. The model is then used to analyze the influence of indenter tip shape on the COF by applying it to a few typical shapes of grinding grits. Finally, good agreement was found on the variation trend of the COF in double scratch tests under different separation distances.

Experimental investigation and optimization on machined surface of Si3N4 ceramic using hot SiC abrasive in HAJM

Abstract Machining of advance engineering ceramic materials are extremely demanded in present day precision industries as it has a wide and potential application in various fields i.e. automobile, aerospace and biomedical engineering. Silicon nitride ceramics (Si3N4) are difficult to machine in conventional machining methods. Hot abrasive jet machining (HAJMing) is a state-of-art technology which practically facilitates the machining in all the engineering materials. This experimental research deals with the investigation on optimization of process parameters of HAJMing of Si3N4 with hot SiC abrasive particles. The surface roughness, overcut and flaring diameter were studied against the HAJMing process parameters, namely, compressed air pressure, nozzle-tip- distance and abrasive temperature. Contribution of these constraints on outcomes was determined by analysis of variance. Also, Regression models were obtained for all the outcomes. CFD simulation for material erosion mechanism of HAJMing was used to obtain the nature of produced workpiece surface profile. Moreover, microstructures of HAJMed surface s were analyzed using scanning electron microscopic images. The outcomes of RSM data validated through experimentation and RSM predictive model. In addition, a multi-objective optimization problem has been employed based on empirical model objective functions and solved by multii objective genetic algorithm (GA). The RSM with GA was used to find out the optimum machining constraint value for an exact HAJMing condition in Si3N4 ceramic materials.

Modeling and performance evaluation of Al2O3, MoS2 and graphite nanoparticle-assisted MQL in turning titanium alloy: an intelligent approach

Recently, the urgency of improved machining performance and environmental sustainability has forced the manufacturer to seek for alternative cooling and lubricating agent/technique such as nano-fluid (NF)-assisted minimum quantity lubrication (MQL). In this context, the performances of aluminum oxide (Al2O3), molybdenum disulfide (MoS2) and graphite (C) NF-impinged MQL in turning of Ti alloy (grade II) using CBN tool were evaluated regarding the cutting force, cutting temperature and surface roughness. The cutting speed, feed rate, approaching angle and cutting conditions (i.e., NFs) were oriented following the Box–Behnken design-of-experiment. The experimental results showed that the graphite NF, compared to Al2O3 and MoS2, revealed the lowest cutting force, temperature and roughness. Moreover, it is evident from SEM images that graphite NF revealed a smoother machined surface and tool profile. This smooth tool and workpiece surface profile can be accredited to graphite’s role as a nano-lubricant and its breaking ability into smaller NFs under pressure. To make the study complete, the adaptive neuro-fuzzy inference system (ANFIS) was employed to predict, the response surface methodology (RSM) was used to mathematically model, and the composite desirability approach (CDA) was used to optimize the responses. A good agreement between the experimental and modeled observations was found; however, the ANFIS outperformed the RSM. Moreover, the analysis of variance exhibited that the cutting force and temperature were primarily influenced by the cutting speed and the surface roughness was afflicted mostly by the feed.

Study on surface material removal uniformity in double side grinding based on grain trajectories

Double side grinding is a process with high processing efficiency in which the wheel and the workpiece are in surface contact. But the phenomenon that the workpiece surface profile is out of tolerance exists because of the material removal non-uniformity in the grinding process. In order to improve the surface integrity of the ground workpiece, the mathematical models of grinding trajectory distribution and material removal were established on the basis of the double side grinding process. The simulation and experimental research were carried out under different grinding parameters. It was shown that when the grinding wheel rotates in the co-rotation to the workpiece, the surface profile of the workpiece was better than that of the opposite direction. The speed ratio of the grinding wheel to the workpiece had a significant influence on the surface trajectory distribution and material removal uniformity. When the speed ratio was irrational, the trajectory distribution was more uniform. At a lower speed ratio, the surface profile of the workpiece was better. Although the simulation results and the experimental results had a good consistency in the trend, the simulated values were obviously smaller than the experimental values, which indicated that the material removal uniformity based on grain trajectories was not the only influencing factor of the phenomenon of convex in the middle of the workpiece during double side grinding.

Chipping Detection in Ceramic Insert during Turning by Analysis of Workpiece Surface Profile Using Cross-Correlation

Chipping in ceramic cutting inserts during machining can cause detrimental effects on the surface finish quality of the workpiece. Current methods of detecting chipping in ceramic inserts do not compare the minute changes in the workpiece profile in subsequent machining passes. In this work, an effective method of detecting chipping in ceramic inserts by analysing the workpiece surface profile using cross-correlation is proposed. In this method, the sub-pixel edge location method was used to extract the surface profile captured using a digital camera. Surface profiles corresponding to subsequent machining passes were cross-correlated at three different sections of the workpiece. An abrupt drop in the correlation coefficient was observed in the middle and end sections of the workpiece after the fifth machining pass, thus providing a reliable measure of tool chipping.

Application of Wavelet Analysis in Tool Wear Evaluation Using Image Processing Method

Tool wear plays a significant role for proper planning and control of machining parameters to maintain the product quality. However, existing tool wear monitoring methods using sensor signals still have limitations. Since the cutting tool operates directly on the workpiece during machining process, the machined surface provides valuable information about the cutting tool condition. Therefore, the objective of present study is to evaluate the tool wear based on the workpiece profile signature by using wavelet analysis. The effect of wavelet families, scale of wavelet and statistical features of the continuous wavelet coefficient on the tool wear is studied. The surface profile of workpiece was captured using a DSLR camera. Invariant moment method was applied to extract the surface profile up to sub-pixel accuracy. The extracted surface profile was analyzed by using continuous wavelet transform (CWT) written in MATLAB. The results showed that average, RMS and peak to valley of CWT coefficients at all scale increased with tool wear. Peak to valley at higher scale is more sensitive to tool wear. Haar was found to be more effective and significant to correlate with tool wear with highest R2 which is 0.9301.

Kinematic simulation of the uncut chip thickness and surface finish using a reduced set of 3D grinding wheel measurements

This paper combined experimentally-measured grinding wheel topography data taken around the entire circumference of the grinding wheel with a kinematic simulation of the grinding process. Several new methods were developed in order to create the resulting high-fidelity and computationally-efficient simulation. First a novel peak-removal technique was developed and applied to effectively remove erroneous peaks in the raw wheel topography data. Next a method was found to determine only the active cutting points on the wheel model by considering the kinematics of the grinding process. This new approach was able to reduce the simulation time from over twelve hours to about four seconds without losing any information about the cutting edge–workpiece interaction. The resulting predicted workpiece surface was then experimentally validated by carrying out a grinding experiment using the same grinding wheel used to develop the grinding wheel computer model and then measuring the resulting workpiece surface profile. Good agreement between simulated and experimental workpiece profiles was observed. Finally, the validated simulator was used to develop a kinematically-exact method to calculate the maximum uncut chip thickness and the simulation results were investigated for different depths of cut, wheel speeds and workpiece feeds.

Experimental evaluation of the lubrication performance of mixtures of castor oil with other vegetable oils in MQL grinding of nickel-based alloy

Vegetable oil is employed as base fluid in precision grinding because of its biodegradability and non-pollutant properties. Castor oil exhibits superior lubrication performance to other vegetable oils, but its high viscosity and poor flow limit its application in industrial production. In this study, castor oil was used as base oil and individually mixed with six other kinds of vegetable oils (i.e., soybean, maize, peanut, sunflower, palm, and rapeseed oils) at a ratio of 1:1 to change the rheological properties of the former. Each mixture was obtained as base oil for minimum quantity lubrication grinding. The high-temperature nickel-based alloy GH4169 was used as workpiece to evaluate the lubrication performance at the grinding wheel/workpiece interface. The mechanism of lubrication was also studied based on the molecular structure of vegetable oil. Specific grinding force, specific grinding energy, surface roughness, surface microtopography, and grinding debris were compared among the experimental and comparison groups (castor oil). The workpiece surface profile was analyzed using the correlation function and cross-correlation coefficient. Results indicated that the comprehensive lubricating performance of mixed oil was superior to that of castor oil, and soybean/castor oil exhibited the optimal performance. The specific tangential grinding force and specific normal grinding force were 0.664 and 1.886 N/mm, respectively, with 27.03% and 23.15% reduction, respectively, with respect to those of castor oil. The surface profile curves of the workpiece obtained from four kinds of working conditions (castor oil, castor/soybean oil, castor/maize oil, and castor/palm oil) were also analyzed. The amplitude of the surface profile curve in castor/soybean oil is larger and the correlation coefficient is higher (0.51) than those under other mixed oils; hence, the workpiece showed the optimal surface quality.

Lubricating property of MQL grinding of Al2O3/SiC mixed nanofluid with different particle sizes and microtopography analysis by cross-correlation

With the increased requirements for environmental protection, energy conservation, and low consumption, nanofluid minimal quantity lubrication (MQL) grinding, which is an environment-friendly machining method, has been paid increasing attention. Improving the lubricating property of nanofluids effectively is currently a main research trend. Meanwhile, optimizing mixed nanoparticle (NP) size ratio is an effective way for enhancing the lubricating property of MQL grinding. In the experiment, different sizes (30, 50, and 70nm) of Al2O3 and SiC NPs were mixed, and nanofluids were prepared at 2% (volume fraction) mixed NPs and base oil. The prepared nanofluids were then used in MQL grinding on a hard Ni-based alloy (inconel 718). The experiment was then evaluated by specific grinding force, removal rate of workpiece, surface roughness, morphology of grinding debris, and contact angle. The effect of the sizes of the Al2O3/SiC mixed NPs on MQL grinding performance was discussed in accordance with the period and amplitude, as well as cross-correlation coefficient, of the workpiece surface cross-correlation function curve profile. Experimental results suggest that different Al2O3/SiC mixed NP sizes affect the nanofluid MQL grinding performance variably. The highest removal rate of the workpiece [189.05mm3/(sN)] and the lowest RSm (0.0381mm) were achieved when the Al2O3/SiC mixed NP size ratio was 70:30. The lowest Ra (0.298μm) was obtained at 50:30. Meanwhile, the highest length ratio of the profile support (90%), the best morphology of abrasive dusts, and the largest wetting area of liquid drops were acquired at 30:70. Furthermore, a cross-correlation analysis of the workpiece surface profile curve under three size ratios (30:70, 50:30, and 70:30) was carried out. The cross-correlation function curve of the workpiece surface profile under 30:70 attained the shortest period, the largest amplitude, and the largest cross-correlation coefficient (0.67), thereby indicating good workpiece surface quality. Therefore, 30:70 was the best size ratio of the Al2O3/SiC mixed nanofluid.

Multi-beam angle sensor for flatness measurement of mirror using circumferential scan technology

Flatness tolerance of mirror is usually determined for a particular manufactured product based on the user’s requirement. To help meet this requirement, we here propose a high-accuracy microscale flatness-measuring machine (micro-FMM) that consists of a multi-beam angle sensor (MBAS). We review the techniques and the sensors predominantly used in the industry to quantify flatness. Compared with other methods, the MBAS can eliminate zero-difference error by circumferential scan and automatically eliminates the tilt error caused by the rotation of a workpiece. Our optical probe uses the principle of an autocollimator, and the flatness measurement of the mirror comprises two steps. First, the MBAS is designed to rotate around a circle with a given radius. The workpiece surface profile along this trajectory is then measured by the micro-FMM. Experimental results, confirming the suitability of the MBAS for measuring flatness are also presented.

Effects of the Profile of Workpiece Surface on Material Removal Rate Distribution in Polishing Process

Accurate prediction of the material removal rate (MRR) distribution is very important for the control of the polishing process. However, the widely used prediction method of MRR based on the Preston equation is still incapable of predicting the roll-off phenomenon in polishing process. One of the reasons is that many of the researchers’ neglected the effect of the surface profile of the workpiece on the MRR. In this paper, the evolutionary process of MRR distribution with the change of surface profile using two different polishing pad is studied, it is found that MRR varies gradually with the change of surface profile and tends to be uniform finally. Based on the analysis of contact pressure considering the actual surface profile of workpiece and modified Preston equation, the distribution of MRR is analyzed. It is found that the Preston coefficient distribution on workpiece surface is stable when the surface profile variation is small and shows obvious differences from the center to the edge of the workpiece. Through the comparison it is found that correlation between the regularities of Preston coefficient distribution and the type of polishing pad is significant. The research results in this paper will play an important guiding role in the quantitative prediction method research of polishing process.

Analysis of the polishing ability of electrogenerated chemical polishing

Electrogenerated chemical polishing (EGCP) had been proved effective in improving both the smoothness and the flatness of copper surface in our previous work. In this paper the polishing ability, defined as the ratio between the material removal rate at the peak and at the valley of a rough surface, is studied theoretically and experimentally. In a mathematical model, the effects of the special wavelength L, the peak-to-valley height h of the workpiece surface profile and the working distance d between working electrode and workpiece surface on the polishing ability are studied. The results show that the polishing ability decreases with increasing the working distance and finally approaches the value (d+h)/d, if L is much larger than d. However, the effect of the working distance on polishing ability is negligible, if L is close to or less than d. The polishing ability also decreases with h decreasing. Based on the above analysis, an analytical expression of the polishing ability of EGCP is given. For validating the theoretical analysis, a copper surface is polished by EGCP and the change of the surface profile is measured and analyzed using the analytical expression. The measured polishing ability agrees well with the simulation results.

Detection of fracture in ceramic cutting tools from workpiece profile signature using image processing and fast Fourier transform

A non-contact vision-based method to detect the occurrence of fracture in ceramic cutting tool inserts using the workpiece profile signature is proposed. Machining experiments were carried out to turn stainless steel using ceramic cutting tool inserts. The images of the workpiece profile were captured after each turning pass using a high-resolution DSLR camera. The edge profiles of the workpiece were extracted to sub-pixel accuracy using the invariant moment method. The extracted profiles were transformed from the spatial domain to the frequency domain using Fast Fourier Transform (FFT). From the Fourier spectrum the amplitude of the fundamental feed frequency was observed to increase steadily with the cutting duration during gradual wear of the tool edge. However, significant fluctuations in the amplitude of the fundamental feed frequency were observed after the onset of chipping and fracture due to the continuous deterioration of the cutting edge. This was caused by the irregular peak-to-valley heights in the workpiece surface profile resulting from the fractured tool at the end of the cutting time of 84.8s.

In-process measurement of surface roughness using machine vision with sub-pixel edge detection in finish turning

Common methods of in-process surface roughness measurement are capable of providing a limited number of amplitude parameters for roughness assessment. This is mainly due to the averaging effect of the sensors used. In this work, we measured the amplitude, spacing, hybrid as well as functional surface roughness parameters during dry turning of AISI 1035 carbon steel using machine vision. A commercial DSLR camera with high shutter speed was used to capture a blur-free image of the workpiece surface profile diametrically opposite the cutting tool. The edge of the surface profile was detected to sub-pixel accuracy using the grey level invariant moment and the roughness parameters were determined from the profile. The tool nose wear and machining time were correlated with amplitude, hybrid, and spacing surface roughness parameters, as well as the bearing area curve parameters. Three new roughness parameters, namely average slope of profile peaks (Φp), average slope of profile valleys (Φv), relative length of peaks (Rrl), were introduced to study the effect of changes in tool nose micro geometry due to wear on the surface roughness. Among these new parameters Φp and Rrl showed better correlation with machining time and nose wear compared to all other parameters.

Effect of tool nose profile tolerance on surface roughness in finish turning

The effect of the tool nose profile deviations in cutting tool inserts on the surface roughness of the work piece produced based on the actual tool nose profile geometry is studied. The nose profile was detected from the tool nose image captured using the 3-D metrology system. A edge detection approach combining moment invariance operator with Sobel 2-D filter operator is proposed. A work piece surface profile is then generated by considering tool nose profile deviation, feed rate, nose radius and wedge angle to study the effect of the work piece geometry deviation on the roughness values. Based on the experimental results, the maximum differences from ideal and experimental results are 19.8% for R t, 19.9% for R a and 16.1% for R q respectively.

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.

Ultrasonic Cavitation Peening of Stainless Steel and Nickel Alloy

Ultrasonic cavitation peening is a peening process utilizing the high pressure induced by ultrasonic cavitation in liquids (typically water). In this paper, ultrasonic cavitation peening on stainless steel and nickel alloy has been studied. The workpiece surface microhardness, the microhardness variation at different depths, the workpiece surface profile, roughness, and morphology have been measured or observed. It has been found that for the studied situations, ultrasonic cavitation peening (at a sufficiently high horn vibration amplitude) can obviously enhance the workpiece surface hardness without significantly increasing the surface roughness. Under the investigated conditions, a surface layer of more than around 50 μm has been hardened under a horn vibration amplitude of ∼20 μm.