Surface Roughness Of Workpiece Research Articles

Improving and Predicting the Surface Roughness and the Machining Accuracy in Ultrasonic Vibration-Assisted Milling

AbstractThe present research work amis to develop a controlled vibratory system for improving the performance of slotting process based on the vibration-assisted milling technique. In addition, the establishment of a statistical model for predicting the process efficiency was a key aim target.In the current investigation, an ultrasonic vibratory device was designed to provide vibration oscillations in the cutting feed direction to the 7075 aluminum alloy workpiece side at 34.7 kHz frequency and 10 μm amplitude. A LabView code has been developed to control the input vibration parameters. A full factorial design of experiments with 4 factors and 2 levels was conducted. An in-depth statistical analysis is then implemented to study the effect of machining and vibration parameters on the process performance. Then, a statistical model for predicting the process response in terms of cutting force, surface roughness, and machining accuracy has been established.Some experiments showed a significant reduction in cutting force up to 50% in the feed direction. Additionally, an improvement in workpiece surface roughness was recorded. Regarding the machined surface accuracy, introducing ultrasonic vibrations significantly reduces the produced slot width error compared to Conventional Milling (CM).The developed statistical model shows a very well agreement with the experimental results. Therefore, the developed VAM technique is suitable for manufacturing parts that require high geometrical and dimensional accuracy.

Texture feature similarity-based roughness intelligent evaluation: a case study applied to milled surfaces

Surface roughness is of great significance in maintaining mechanical performance and improving the reliability of the equipment. However, fast surface roughness evaluations that are sufficiently stable and efficient for engineering in situ use have not yet been realized. To address this issue, an image-driven roughness intelligent method is proposed in this research. By evaluating the texture similarity intelligently between the testing image and the reference image, the surface roughness of the testing image can be acquired. Firstly, with a proposed adaptive texture extraction method, the texture feature of an image can be extracted even under a complex background. Secondly, by establishing the graph structure of the texture grayscale features, the similarity between different images is evaluated. Finally, by establishing a sparrow-optimized support vector machine regression method, the mapping relationship between the similarity and the surface roughness can be acquired. The experimental results indicate that the proposed method for intelligent evaluation of roughness has superior prediction performance (the average relative prediction error of Ra and Rz are 8.8156% and 8.0571%, respectively). Therefore, this work provides a useful tool for non-contact detection of workpiece surface roughness.

Optimizing surface roughness of workpieces made of stainless steel 316L with DMLS technology: a Taguchi method approach

This study employs Direct Metal Laser Sintering (DMLS) technology to investigate surface roughness in stainless steel 316L 3D printing processes. Utilizing the Taguchi method in experimental design, we examine the influence of independent variables—laser power, scan speed, and hatch spacing - on surface roughness quality. Results indicate that laser power has the greatest impact, followed by scan speed and hatch spacing. Notably, both laser power and hatch spacing positively affect surface roughness, while scan speed adversely affects the top surface quality of printed components. This research enhances comprehension of the intricate relationship between process parameters and surface quality in DMLS-based 3D printing, offering insights for optimizing surface roughness in stainless steel 316L applications. The study holds practical significance for enhancing the quality and performance of 3D-printed components across diverse engineering and manufacturing sectors.

Influence of Molybdenum Disulphide (MoS2) Nanoparticles Loading in Treated Used Palm Oil Bio-lubricant on Surface Roughness during Turning of AISI420

The environmental impact and non-biodegradability of petroleum-based lubricants has caused some researchers to shift the formulation of lubricant formulation using plant-based oil, However, food security has caused concerns in development of new bio-lubricants. In this study, treated used cooking palm oil added with Zinc Dialkyldithiophosphate and Molybdenum Disulphide nanoparticles were used as lubricant to assist the cutting of hardened martensitic stainless steel AISI420 using coated carbide tool. Surface roughness of the cut workpiece was investigated using a surface roughness tester. From the study, treated used cooking oil added with ZDDP and 0.8wt% MoS2 nanoparticles displayed the lowest average surface roughness of workpiece with Ra = 0.532µm and total roughness Rz = 4.006µm. This concluded that the new formulated bio-lubricant can successfully replace the readily available commercial cutting fluid as it produces a low surface roughness during cutting process.

Modeling and control of electrical discharge wire sawing of single-crystal silicon

Electrical discharge wire sawing (EDWS) is a new composite processing method that combines wire electrical discharge machining with fixed abrasive wire sawing. During processing, relying on the grinding effect of the abrasive grains, craters and recast layers generated by discharge are removed. The surface roughness of the workpiece is reduced while the cutting efficiency is improved. In this study, the relationship between discharge voltage and workpiece surface roughness in composite machining is investigated. A single-input single-output control system model with applied resistance as the input and discharge voltage as the output is established. Based on the theory of system identification and parameter estimation, the order of the model is identified by calculating the variance of the model residuals and using the F-test method. The model parameters of the control system are estimated online using a recursive least squares algorithm with a forgetting factor. A generalized predictive controller is designed on the basis of the predictive control theory and the identified discharge voltage control system model. Using LabVIEW software, a discharge voltage prediction control system is constructed for the processing of single-crystal silicon by EDWS, and its prediction control performance is experimentally verified. The results show that the discharge voltage control system designed in this study can successfully control the composite machining of single-crystal silicon in real time. Under the same machining conditions, the surface roughness of the workpiece machined with predictive control was 23.5 % lower than that without predictive control.

Investigation on the optimal ultra-precision machining of nitriding mold steel by in-situ laser assisted diamond cutting

S136 (STAVAX ESR) mold steel is an advanced material for the optical mold industry. The precisely machined surface quality is essential for guaranteeing the excellent performance of molds. Diamond cutting is a high-efficiency machining technique in optical mold manufacturing. However, the chemical wear of diamond tool is a huge challenge in precisely machining steel mold due to the reaction between atoms C and Fe. In this study, the hybrid machining technique which combined the in-situ laser assisted diamond cutting (LADC) and plasma nitriding was used to machine S136 mold steel. Furthermore, the impact of the various machining conditions on the machined workpiece surface roughness Sa was comprehensively investigated by the Taguchi method (TM) and Response surface methodology (RSM). The experimental results of material characterization indicated that a 90 μm nitride layer was generated on the workpiece surface by plasma nitriding. Analysis of variance (ANOVA) results demonstrated that the feed rate and laser power had an important impact on the machined surface roughness Sa. According to the RSM, a precise mathematical model was developed which is explored by experimental investigations. The results showed that the optimal machining parameter combination by RSM generated the better surface quality of 6.393 nm in in-situ LADC of nitriding steel. This study provides a reference for mold steel ultra-precision machining technique and parameter optimization method for surface integrity.

Based on CBB-yolo rusted workpiece surface roughness detection

The surface roughness of the workpiece is one of the important indicators to measure the quality of the workpiece. Vision-based detection methods are mainly based on human-designed image feature indicators for detection, while the self-extraction method of milling surface features based on deep learning has problems such as poor perception of details, and will be affected by surface rust. In order to solve these problems, this paper proposes a visual inspection method for surface roughness of milling rusted workpieces combined with local equilibrium histogram and CBB-yolo network. Experimental results show that local equilibrium histogram can enhance the milling texture and improve the accuracy of model detection when different degrees of rust appear on the surface of the milled workpiece. The detection accuracy of the model can reach 97.9%, and the Map can reach 99.3. The inference speed can reach 29.04 frames per second. And the inspection of workpieces without rust, this method also has high detection accuracy, can provide automatic visual online measurement of milling surface roughness Theoretical basis.

Optimizing Abrasive Water Jet Machining for Enhanced Machining of 316 Stainless Steel

Abrasive Water Jet Machining (AWJM) is a non-traditional machining process renowned for its versatility and ability to cut a wide range of materials precisely. This research article presents an in-depth analysis of the optimization of AWJM parameters for machining 316 stainless steel, aiming to enhance surface quality and machining efficiency. Through a comprehensive experimental setup, the study explores the effects of varying the speed, standoff distance (SOD), and flow rate on the surface roughness (Ra) of the machined workpiece. The Taguchi method's L9 orthogonal array is employed to design the experiments, and a signal-to-noise (S/N) ratio analysis, alongside an analysis of variance (ANOVA), is utilized to discern the most significant machining parameters. Response tables for S/N ratios and means are created to summarize the effects, and main effects plots are generated to visualize trends in the data. Furthermore, a regression model is developed to correlate the machining parameters with the surface roughness, which is validated by a high coefficient of determination. Residual plots and diagnostics for unusual observations are utilized to ensure the robustness of the model. The study concludes that SOD is the most influential parameter, followed by speed and flow rate. The optimization results provide a quantitative understanding that can significantly contribute to the industrial application of AWJM for 316 stainless steel, ensuring optimal surface integrity and operational cost-effectiveness. The findings of this research offer pivotal insights for manufacturing industries that seek to integrate AWJM into their production processes.

Numerical simulation and tool parameters optimization of aluminum alloy transmission intermediate shell

Due to its challenging manufacturing and intricate morphology, the aluminum alloy transmission intermediate shell used in vehicle transmission has been the focus of many academic studies. In this study, the three-dimensional cutting model is condensed to a two-dimensional cutting model and utilized to simulate the finishing process of an aluminum alloy workpiece using the finite element modeling program DEFORM-3D. Through orthogonal testing and range analysis, the impact of integral end mill side edge parameters on cutting performance was investigated. It is determined that tool chamfering has a greater impact on cutting performance than tool rake and relief angles, that chamfering width has the most impact on cutting force, and that chamfering angle has the greatest impact on cutting temperature. The workpiece's surface roughness is tested during a cutting experiment, and an analysis of the data reveals that the finite element simulation model is accurate and the orthogonal test method is reasonable. The tool chamfer has a greater impact on roughness than the tool rake angle and relief angle. The tool settings are further optimized using the firefly method. By examining the data, it is determined that the prediction model is correct and the optimization model is reasonable. The cutting efficiency is higher and the surface quality is better when the chamfer width is 0.17 mm and the chamfer angle is 7.3° or 18.3°. Therefore, optimizing the side edge parameters of the integral end mill during the finishing process of a thin-walled aluminum alloy shell has practical technical value.

Effect of laser texturing process on properties of composite coated cemented carbide ball-end milling cutter milling titanium alloy

To explore the strengthening mechanism of the laser texturing process on the surface of composite coating (AlCrN/AlTiSiN) cemented carbide and improve the application rate of laser texturing process in the field of tool cutting. Firstly, the influence of laser texturing process on the surface properties of composite coated cemented carbide was compared and analyzed. Secondly, a test platform for milling titanium alloy with composite coated cemented carbide ball-end milling cutters with different meso-geometry features parameters (blunt round edge radius and micro-texture distance from cutting edge) was built. Finally, the influence analysis and the optimization of geometric characteristic parameters were carried out with milling force, tool wear and workpiece surface roughness as dependent variables. It is concluded that the laser texturing process can significantly improve the phase structure of the composite coating and improve the quality of the coating. The influence of meso-geometry features on milling force, tool wear, and workpiece surface roughness and the parameter optimization of a single dependent variable are obtained by the milling test. Based on the genetic algorithm, the optimal meso-geometry features parameters of the carbide ball-end milling cutter are obtained, that is, the blunt round edge radius is 20.15 μm, and the micro-texture distance from cutting edge is 119.463 μm. It provides a practical basis for the application of laser-textured coating technology in the field of cutting tools.

Research on tool wear and surface quality in laser-assisted ultrasonic elliptical vibration cutting of cemented carbide

The Laser-ultrasonic elliptical vibration cutting (LA-UEVC) method was introduced for machining challenging cemented carbide. The machining parameters were estimated through numerical simulation, and the cutting forces, the tool wear and the workpiece surface quality were analyzed and investigated. Compared to precision cutting, during LA-UEVC of cemented carbide, the main cutting force and the cutting radial thrust force were reduced by 44.3% and 31.2%, respectively. Moreover, the tool wear decreased, which led to an increase in tool life by 118.8%, and a maximum reduction of 89.7% of Workpiece surface roughness was received. The impact of laser power and cutting parameters on surface roughness was analyzed through orthogonal experiments, and a surface roughness prediction model was established using a quadratic regression model.

Few-shot detection of surface roughness of workpieces processed by different machining techniques

The traditional deep learning method for detecting workpiece surface roughness relies heavily on a large number of training samples. Also, when detecting the surface roughness of workpieces processed by different machining techniques, it requires a large number of samples of that workpiece to rebuild the model. To address these problems, this paper proposes a few-sample visual detection method for the surface roughness of workpieces processed by different techniques. This method first trains a base model using a relatively large amount of samples from one machining technique, then fine-tunes the model using small amounts of samples from workpieces of different techniques. By introducing contrastive proposal encoding into Faster R-CNN, the model’s ability to learn surface features from small amounts of workpiece samples is enhanced, thus improving the detection accuracy of surface roughness of workpieces processed by different techniques. Experiments show that this method reduces the model’s dependence on training samples and the cost of data preparation. It also demonstrates higher accuracy in surface roughness detection tasks of workpieces processed by different techniques, providing a new approach and insights for few-sample surface roughness detection.

Self-lubricating performance of a grinding wheel filled with CNTs@T304 nano-capsules

This paper introduces a new grinding wheel which lubricates the grinding area directly. This is achieved by filling a resin grinding wheel with nano-capsules comprised of carbon nanotubes (CNTs) and di-n-butyl phosphite (T304) (CNTs@T304). The CNTs@T304 nano-capsules filled grinding wheel reduces the grinding force, lowers the grinding temperature, and improves the workpiece surface quality. The T304 lubricant is first filled into the CNT cavity to prepare nano-capsules, which exhibit better friction and wear quality than CNTs. Then the paper analyzes the configuration of the nano-capsules in the grinding wheel, together with the investigation of how the filling content impacts the mechanical properties. Additionally, the effects of nano-capsule filling content and grinding parameters on grinding force, grinding temperature, grinding ratio, as well as workpiece surface roughness are studied and the self-lubricating mechanism of the nano-capsules is assessed. According to the experimental results, nano-capsules maintain stable under thermal condition and resist the curing temperature. Moreover, the tensile strength and hardness exhibited by the grinding wheel still meet the application requirements at a filling content <16 wt%. Compared with the grinding wheel without fillers, the nano-capsule filled wheel exhibits superior grinding performance, with a 33 % reduction in the grinding force. Also, the grinding temperature is 28 % lower, there is a 30 % improvement in the grinding ratio, and the workpiece surface quality increases by 35 % under the same conditions. The T304 in the nano-capsules is continually released onto the grinding interface with the wear of the wheel in the grinding process, which forms a self-lubricating layer along with the friction products of the CNTs. This exerts an outstanding anti-friction and anti-wear effect, thereby improving the grinding performance.

Elastoplastic contact model of pitch-based rough surface and its polishing characteristics

Fused silica glass is widely used in optical systems, including astronomical telescopes, laser systems, optical communications, and the semiconductor industry. At the same time, the surface quality of the fused silica directly determines the performance and precision of the system. In order to analyze the microscopic surface interaction based on the basis of tribology, a roughness contact model of pitch and fused silica glass surfaces was established. Analyze the performance parameters of contact materials, surface roughness, and the relationship between load and contact area. Pitch materials with a higher plasticity index have a larger elastoplastic contact area with the fused silica surface during the polishing process. The experimental results demonstrate that the surface quality of the polished fused silica improves as the plasticity index of the pitch material increases. At the same time, judging from the PSD curve results, the polished surface of the No. 55 pitch on the spatial-frequency band curve (100-101/mm) is significantly lower than the other two brands of pitch. Additionally, the Ra value of the workpiece surface roughness reaches 0.091 nm. The results of this study provide important theoretical guidance for achieving full-diameter, full-frequency ultra-smooth polishing of large-diameter complex curved surfaces.

Study on tool wear mechanism and chip morphology during turning of Inconel 713C by textured inserts

The present study aims to analyze the tool wear mechanism and chip morphology during dry (D) and graphene solid-lubricant (S), turning of Inconel 713C with a new novel textured (honeycomb (T2) and rectangular(T3)) and further compare with the textured dimple tool (T1). In addition, the performances were compared with the untextured tool (T0). The machining tests for all conditions were carried out at different levels of cutting speeds (75 m/min and 150 m/min), feed rate (0.1 mm/rev and 0.2 mm/rev), and a constant level of depth of cut (0.5 mm). Honeycomb-textured cutting tools performed better. Improvements of 33.3 % (T1D), 50 % (T3D), and 75 % (T2D) without graphene and 66.6 %(T1S), 75 % (T3S), and 100 %(T2S) with graphene are observed compared to T0. The primary mechanism for the better performance of T2S was the reduced tool chip contact length (8–50 %), chip width (30.4 %), friction coefficient (57.4 %), chip thickness (17.8 %), and higher shear angle (14.2 %) compared to dry textured and untextured tools at the end of tool life. Additionally, the T2S (solid lubricant-impregnated honeycomb) demonstrates a decrease in equivalent chip thickness (19.8 %) and an increase in shrinkage factor (17.2 %) at worn-out tool life conditions at high cutting speed and feed rate. Under the same conditions, T2S has 27.3 % lower cutting force and 36.2 % lower surface roughness of machined workpieces compared to the untextured tool at the end of tool life. The chip micrograph reveals the chips are shorter, and the chip radius is lower for T2S tools with smooth friction tracks and fine deformed lamella on the free surface of the chip. Under SEM, flank wear, adhesion materials, abrasion, micro-chipping coating peeling, and BUE were dominant wear mechanisms. However, T2S tools effectively reduce Ni elements' diffusion and suppress the built-up edge's adhesion by improving friction characteristics at the tool-chip interface region. Lastly, the outcomes of experimental results suggest that the honeycomb texture for dry machining of Inconel 713C.

Evaluation of machining characteristics of SiO2 doped vegetable based nanofluids with Taguchi approach in turning of AISI 304 steel

In this study, nanofluids prepared by incorporating silicon dioxide (SiO2) nanoparticles into sunflower oil were used as cutting fluids for turning AISI 304 stainless steel. Dynamic viscosities and thermal conductivities of nanofluids prepared at two different concentrations (1 % and 0.5 % by volume) were measured at four different temperature conditions. The experiments were carried out in two stages: main experiments and additional experiments. In the main experiments, four different cooling conditions (dry, PureMQL, 0.5 % NanoMQL, 1 % NanoMQL), four different cutting speeds (80, 120, 160, 200 m/min), and four different feed rates (0.10; 0.15, 0.20, 0.25 mm/rev) were used. In additional experiments, the effect of 4 different cooling methods on machining performance was investigated by keeping the maximum cutting speed (200 m/min) and feed rate (0.25 mm/rev) constant. In all experiments, workpiece surface roughness (Ra), temperature in the cutting zone (T), and flank wear on cutting tools (Vb) were determined as performance criteria. Also, additional experiments were repeated 45 times, and wear was measured every ten experiments to get a clearer picture of the tool wear process. At the end of the study, it was determined that the most effective parameter on surface roughness was feed rate (76 %), while the temperature in the cutting zone and tool wear were mainly affected by the cooling method (80 % and 50.5 %, respectively). The best machining performance was observed in the 0.5 % nanoMQL method.

Suppressing Milling Chatter of Thin-Walled Parts by Eddy Current Dampers

Machining vibrations often occur when working with thin-walled workpieces. One effective method to mitigate these vibrations is by using a damper, which can enhance machining accuracy, surface finish, and tool life. However, traditional contact dampers have a drawback in that they require direct contact with the workpiece, leading to friction, wear, increased cutting forces, and reduced machining accuracy. In contrast, electromagnetic eddy current dampers are noncontact dampers that can effectively suppress machining vibrations without the need for physical contact. In this study, a method to suppress machining vibrations in thin-walled workpieces using electromagnetic eddy current dampers is proposed. By establishing a theoretical model for the electromagnetic damper, the damping force and equivalent damping of the damper are determined. Subsequently, the impact of electromagnetic dampers on frequency response functions and machining vibrations are investigated through hammer impact tests. The results indicate that increasing the surface damper voltage and reducing the air gap both enhance the equivalent damping of the electromagnetic eddy current damper. Moreover, cutting experiments are conducted to analyze the surface roughness of thin-walled workpieces with and without dampers. The results demonstrate that the eddy current damper can effectively increase the equivalent damping and provide the necessary damping force to suppress machining chatter. Overall, the proposed method utilizing electromagnetic eddy current dampers presents a promising solution for suppressing machining vibrations in thin-walled workpieces.

Effect of applied electric and magnetic fields on surface roughness in minimum quantity lubrication turning using Fe3O4 nano-ferrofluid

PurposeFirst, the effect of magnetic field intensity and nano-ferrofluid concentrations on surface roughness was evaluated in magnetic minimum quantity lubrication (MMQL). Then, the effect of lubricant flow rate and nozzle position on surface roughness was investigated in MQL, MMQL, electrostatic MQL (EMQL) and electromagnetic MQL (EMMQL).Design/methodology/approachThis study examined the performance of MQL under magnetic and electric fields in turning AISI 304 stainless steel in terms of surface roughness and compared the results with those obtained from wet cutting and MQL turning operations. To prepare the nano-ferrofluid used in different states of MQL, Fe3O4 nanoparticles were added to the base fluid.FindingsThe results showed that the surface roughness under the EMMQL technique decreased by 36% and 49.4% on average compared with wet and MQL techniques, respectively. The lubrication technique affected the surface roughness by 90.2%, whereas it was 8.3% for the lubricant flow rate. EMQL and EMMQL techniques had no significant difference in their effects on surface roughness. In the innovative MMQL technique, the nano-ferrofluid concentration of 6% and magnetic field intensity of 93 G resulted in lower surface roughness of the workpiece relative to other counterparts.Originality/valueExamining previously published studies showed that using nano-ferrofluids under a magnetic field for cooling purposes in machining processes have less considered by researchers. This study applies an innovative method of lubrication under the concurrent effect of magnetic and electric fields, called EMMQL, to improve the efficiency of MQL in machining hard-to-cut materials. For comprehensively inspecting the newly presented method, the effects of several parameters, including the nano-ferrofluid concentration, magnetic field intensity, lubricant flow rate and position of lubricant spray nozzle, on the surface roughness of workpiece in turning of AISI 304 stainless steel are investigated.

Electrolysis combined shear thickening polishing method

Electrolysis compounded shear thickening polishing (E-STP) was proposed to increase the polishing efficiency of the difficult-to-machine metal. A 316 L stainless steel workpiece was employed in the experiments. The effects of current density, duty ratio, supply frequency, polishing speed, and abrasive concentration on the polishing material removal rate (MRR) and surface roughness of workpiece were examined through single factor experiment. The surface roughness Ra drops from 315 nm to 5 nm with MRR 57 μm/h in 15 mins' processing under the conditions with current density of 0.3 A/cm2, the duty ratio of 0.625, power supply frequency of 100 kHz, the polishing speed of 2.09 m/s, and the abrasive concentration of 12 wt%. A mathematic model of MRR was established, and the difference of MRR between the theoretical and experimental results is <12 %. As revealed by the results, E-STP can serve as a high efficiency polishing method for the difficult-to-machine metal.

Study on the dynamic influence of the distribution of cutting depth on the ground surface quality in a precision grinding process

Focusing on the excitation mechanisms of self-excited vibration and forced vibration, a time-delay differential model of the radial cutting depth of a single active abrasive grit is presented. Based on the principle of the layered superposition characteristics of each component of radial cutting depth, a full discrete simulation technology is used to analyze the dynamic mechanisms of different vibration excitations on the grinding characteristics. The reliability of the dynamic mechanisms is verified by the corresponding grinding experiments. The verification results demonstrate that static and dynamic components in the grinding dynamics, including radial cutting depth, grinding force, surface roughness and surface morphology of workpiece are greatly influenced by total material removal rate, followed by speed ratio and grinding directions. With the increase of cutting depth, the variation amplitude of grinding process influenced by forced-vibration mechanism are nearly twice larger than those influenced by self-excited vibration mechanism. When speed ratio reduces to a half, surface roughness of workpiece improves by nearly 33 %. The stability of machining process and finish surface of workpiece during up-grinding are better than those during down-grinding, where force reduction by up to 10 % and surface finish improvement by 37 %.