Surface Roughness Of Workpiece Research Articles

Coupling Taguchi experimental designs with deep adaptive learning enhanced AI process models for experimental cost savings in manufacturing process development

The Aluminum alloy AA7075 workpiece material is observed under dry finishing turning operation. This work is an investigation reporting promising potential of deep adaptive learning enhanced artificial intelligence process models for L18 (6133) Taguchi orthogonal array experiments and major cost saving potential in machining process optimization. Six different tool inserts are used as categorical parameter along with three continuous operational parameters i.e., depth of cut, feed rate and cutting speed to study the effect of these parameters on workpiece surface roughness and tool life. The data obtained from special L18 (6133) orthogonal array experimental design in dry finishing turning process is used to train AI models. Multi-layer perceptron based artificial neural networks (MLP-ANNs), support vector machines (SVMs) and decision trees are compared for better understanding ability of low resolution experimental design. The AI models can be used with low resolution experimental design to obtain causal relationships between input and output variables. The best performing operational input ranges are identified for output parameters. AI-response surfaces indicate different tool life behavior for alloy based coated tool inserts and non-alloy based coated tool inserts. The AI-Taguchi hybrid modelling and optimization technique helped in achieving 26% of experimental savings (obtaining causal relation with 26% less number of experiments) compared to conventional Taguchi design combined with two screened factors three levels full factorial experimentation.

Study on the Properties of Sinusoidal Micro-Textured Ball End Milling Cutter for Milling Titanium Alloy

To improve the cutting performance when cutting hard alloys and achieve reasonable optimization of micro-texture parameters, this paper proposes a sinusoidal micro-textured tool, with four parameters set as micro-textured spacing, period, width, and amplitude. Establish three-dimensional models of non-micro-textured and micro-textured milling tools and simulate the milling process using finite element simulation. Study the effects of milling force and milling temperature on tool performance. Prepare micro-textured milling tools for orthogonal experiments, analyse the effects of milling force and the surface roughness of a titanium alloy workpiece, and study the impact of different micro-textured parameters on milling tool performance. Obtain the parameters that have the greatest impact on milling tool performance, and then use genetic algorithm to optimize three sets of parameter combinations. Use the optimized parameters to prepare milling tools for comparative experiments, and then determine the optimal parameter combination. The research results indicate that micro-textured tools can effectively improve the milling performance of the tool, and the spacing between sinusoidal micro-textures has the greatest effect on improving the milling performance of the tool. When the period of sinusoidal micro-texture is 2.87 and the amplitude is 25.13 μm, width is 79.89 μm, and spacing is 134.54 μm, the milling performance of the tool is optimal.

Pulsed Unipolar-Polarisation Plasma Electrolytic Polishing of Ni-Based Superalloys: A Proof of Conception

The enhanced performance of aerospace equipment drives parts development towards integration, complexity, and structural optimization. This advancement promotes metal near-net fabrication technologies like wire electrical discharge machining (WEDM) and 3D printing. However, the high initial surface roughness from WEDM or 3D printing poses significant challenges for the high-performance surface finishing required. To effectively reduce the surface roughness of the workpieces with high initial surface roughness, this paper proposes pulsed unipolar-polarisation plasma electrolytic polishing (PUP-PEP). The study examined the material removal mechanisms and surface polishing quality of PUP-PEP. This technique combines the high current density and material removal rate of the electrolytic polishing mode with the superior surface polishing quality of PEP through voltage waveform modulation. For an Inconel-718 superalloy part fabricated by WEDM, PUP-PEP reduced surface roughness from Ra 7.39 μm to Ra 0.27 μm in 6 min under optimal conditions. The roughness decreased from Ra 7.39 μm to Ra 0.78 μm in the first 3 min under pulsed unipolar-polarisation voltage, resulting in a remarkable 233% increase in efficiency compared to that with conventional PEP. Subsequently, the voltage output voltage is transformed into a constant voltage mode, and PEP is continued based on PUP-PEP to finally reduce the workpiece surface roughness value to Ra 0.27 μm. The proposed PUP-PEP technology marks the implementation of ‘polishing’ instead of conventional rough-finish machining processes, presenting a new approach to the surface post-processing of metal near-net fabrication technologies.

A compact compliant robot for the grinding of spherical workpieces with high force control accuracy

A robotic grinding system requires a force-controlled grinding module to provide a consistent surface roughness and a robot arm to position the grinding module to reach a wide range of surface area on a workpiece. Existing pneumatic grinding modules are heavy and bulky and cannot provide very accurate force control. Articulated 6-axis robot arms are often used for positioning the grinding module, but they require a large accommodation space and have limited access to the surface of a spherical workpiece. This paper proposes a compact 3-axis grinding robot with no grinding surface limitations on spherical workpieces. The robot employs torque-controlled actuators so that a human operator can easily teach grinding paths to the robot. The proposed grinding module uses series elasticity to generate very low reflected inertia and friction. Hence, accurate grinding force control can be achieved. The grinding module also has a small size and low noise. Experimental results verify the high accuracy of grinding force control when compared with existing counterparts. Through an illustration of removing the parting line of a helmet hardshell, the grinding robot can effectively reduce the surface roughness of workpieces that are sensitive to the grinding force. It is expected that the proposed robot can be easily reconfigured to grind workpieces of different geometries.

Topography Modeling of Surface Grinding Based on Random Abrasives and Performance Evaluation

The surface morphology and roughness of a workpiece are crucial parameters in grinding processes. Accurate prediction of these parameters is essential for maintaining the workpiece’s surface integrity. However, the randomness of abrasive grain shapes and workpiece surface formation behaviors poses significant challenges, and accuracy in current physical mechanism-based predictive models is needed. To address this problem, by using the random plane method and accounting for the random morphology and distribution of abrasive grains, this paper proposes a novel method to model CBN grinding wheels and predict workpiece surface roughness. First, a kinematic model of a single abrasive grain is developed to accurately capture the three-dimensional morphology of the grinding wheel. Next, by formulating an elastic deformation and formation model of the workpiece surface based on Hertz theory, the variation in grinding arc length at different grinding depths is revealed. Subsequently, a predictive model for the surface morphology of the workpiece ground by a single abrasive grain is devised. This model integrates the normal distribution model of abrasive grain size and the spatial distribution model of abrasive grain positions, to elucidate how the circumferential and axial distribution of abrasive grains influences workpiece surface formation. Lastly, by integrating the dynamic effective abrasive grain model, a predictive model for the surface morphology and roughness of the grinding wheel is established. To examine the impact of changing the grit size of the grinding wheel and grinding depth on workpiece surface roughness, and to validate the accuracy of the model, experiments are conducted. Results indicate that the predicted three-dimensional morphology of the grinding wheel and workpiece surfaces closely matches the actual grinding wheel and ground workpiece surfaces, with surface roughness prediction deviations as small as 2.3%.

Study on electrochemical polishing mechanism of TC4 titanium alloy

To achieve a high-quality and precise surface on TC4 titanium alloy for demanding engineering applications, electrochemical polishing (ECP) technology was employed to investigate the polishing process of TC4 titanium alloy in an ethylene glycol-sodium chloride green electrolyte. The macro and micro morphology, surface roughness, material removal rate, surface elements, surface corrosion resistance, and surface hardness of TC4 titanium alloy were measured before and after polishing. Single-factor experiments were conducted to explore the effects of processing gap, electrolyte temperature, polishing voltage, and polishing time on TC4 titanium alloy surface quality. Optimal polishing parameters were determined to be an electrode gap of 12 mm, a voltage of 20 V, a temperature of 40 °C, and a polishing time of 10 min. The experimental results indicated that after electrochemical polishing, the workpiece surface roughness Ra decreased from 256 nm to 25 nm, achieving a roughness reduction rate of 90.23%, the material removal rate was 3.348 μm min−1. The self-corrosion potential of the polished surface increased from −0.416 V to −0.375 V and the self-corrosion current density decreased from 4.447 × 10−7 A·cm−2 to 1.873 × 10−7 A·cm−2, the surface indentation hardness of the workpiece decreased from 5.8669 GPa to 4.6726 GPa. Based on the characterization of polished surface, the mechanism of electrochemical polishing of TC4 titanium alloy was proposed. The results demonstrate that electrochemical polishing of TC4 titanium alloy is highly efficient, involves no mechanical action, and causes no subsurface damage, making it a promising technology to meet medical requirements.

An Experiment Study on Surface Topography of GH4169 Assisted by Ultrasonic Elliptical Vibration Ultra-Precision Turning

Nickel-based superalloys (GH4169) are a typical difficult-to-machine material with poor thermal conductivity and severe work hardening. They are also prone to poor surface quality, severe tool wear, and poor machinability, which affect their performance. In this paper, an experimental study of GH4169 ultrasonic elliptical vibratory ultra-precision cutting was carried out. The experimental results show that ultrasonic elliptical vibratory cutting (UEVC) significantly reduces surface roughness and improves surface quality compared to conventional cutting (CC). The effects of cutting parameters such as cutting speed, feed rate, cutting depth, ultrasonic amplitude, and tool nose radius on the surface roughness of GH4169 workpieces were further investigated in UEVC. Based on the analysis of the experimental data, the optimal combination of parameters for GH4169 ultrasonic elliptical vibration ultra-precision cutting was determined: cutting speed of 3 m/min, feed rate of 16 μm/rev, cutting depth of 2 μm, ultrasonic amplitude of Ay = 3.0 μm, Az = 0.8 μm, and a tool nose radius of 0.8 mm. This parameter combination improves the machining quality of GH4169 and provides a valuable reference for the subsequent development of ultrasonic elliptical vibratory cutting for other difficult-to-machine materials.

Numerical simulation and experimental researches on different abrasive grain arrangements of monolayer diamond grinding tools fabricated by HFCVD method

The hot filament chemical vapor deposition (HFCVD) method has been used to fabricate monolayer diamond grinding tools, whose abrasive grain arrangements can be achieved by patterned masks. The orderly arrangement pattern of abrasive grains can improve the grinding trajectory density of abrasive grains and reduce the workpiece surface roughness. In this study, the numerical simulation method is used to investigate the effect of abrasive grain pattern arrangements on the grinding trajectories and ground surface topographies, including Archimedes spiral, concentric circular, phyllotactic, disordered, and staggered patterns. The simulation results indicate that the grinding trajectory is most uniform and dense when applying the staggered pattern arrangement, and the workpiece surface roughness is lower than that of the other pattern modes. Then monolayer diamond grinding tools with five types of abrasive grain patterns are fabricated on the SiC substrate by HFCVD method. The grinding tool surface has a complete grain pattern and as-grown diamond film on the substrate is homogeneous in thickness, which is regarded as the bonding layer of the abrasive tools. EDS carbon element mapping and Raman spectra analysis reveal that the as-grown diamonds, both on substrates and seeds, exhibit high quality. Grinding experiments are performed utilizing CVD diamond grinding tools. The ground surface with a staggered pattern exhibits the lowest surface roughness without periodic grinding bands, as is consistent with the simulation results. Besides, the graphitization evaluation and worn feature statistics show that abrasive grains are of low levels of graphitization and high quantity of dynamic active grains as well as have slight abrasive grain wear and large chip space applying staggered pattern, which is the ideal abrasive grain arrangement pattern.

PRELIMINARY STUDY OF THE AREAL ROUGHNESS PARAMETERS IN 3D PRINTING OF WORKPIECES OF PLA MATERIAL

The field of production engineering is constantly expanding, as novel manufacturing procedures are being introduced and spread in the industrial environment. The creation of complex parts by the application of some kind of printing process is a relatively new method compared to the other traditional chip-removal processes. It can be applied in various fields, where the greatest advantage can be utilized, which is the lower restrictions on the geometry of finished parts. In this paper, the surfaces on 3D printed parts are being studied. The effect of the printing speed, layer height and nozzle temperature are analysed on the surface roughness of the experimental workpieces. The experimental setup plan is designed according to the full factorial design method. The results of this preliminary study are the general determination of the affecting factors on the surface roughness.

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.

Analysis of the Influence of Changes In Workpiece Rotation and High Speed Steel Lathe Tool Angles on the Surface Roughness of ST37 Workpieces

Analysis of the Influence of Changes In Workpiece Rotation and High Speed Steel Lathe Tool Angles on the Surface Roughness of ST37 Workpieces

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.