Workpiece Roughness Research Articles

Tool wear of DLC coating as top-layered to CrN-, TiAlSiN-, TiAlN coatings in machining of steel and aluminum alloys

This study investigates the tribological performance of DLC top-layered coatings applied to surfaces of tools coated with CrN, TiAlSiN, and TiAlN. Double-layered coated tools (DLC/CrN, DLC/TiAlSiN, and DLC/TiAlN) were evaluated against single-layered (CrN, TiAlSiN, and AlTiN) and uncoated carbide cutting tools. The coatings were applied using the HiPIMS technique. Machining through orthogonal cutting under dry friction conditions with constant cutting parameters (240 rev/min speed, 2 mm cutting depth, 0.15 rev/mm feed rate) was performed on materials with soft ductile (aluminum) and hard, brittle (steel) characteristics. The coated tools exhibited approximately 20-30% reductions in interface temperature, workpiece roughness, and chip thickness. The DLC top-layered coatings improved tool durability for machining both ductile and brittle materials, as evidenced in Raman analysis. Among the DLC coatings, DLC/AlTiN demonstrated the highest wear resistance and enhanced tool life, as confirmed by SEM-EDS analysis.

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.

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.

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.

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.

Finish turning of AISI 5140 tempered steel to improve machinability for engineering applications: An experimental approach with dry cutting

This study explores the best finish turning conditions for AISI 5140 tempered steel which is commonly used in automotive, agriculture and construction sectors thanks to its high strength and toughness properties. The steel is proper for surface hardening which makes it a perfect option to employ in various engineering applications. However, there are a few studies about this material and no paper is seen on finish turning operation which is highly critical to prepare the parts for workspace. Therefore, this work focuses on the impacts of feed rate, cutting speed and depth of cut parameters on roughness of workpiece along with cutting force and cutting temperatures while dry cutting of AISI 5140 steel. The results of the experiments evaluated with statistical analysis and Taguchi’s optimization approach. Accordingly, cutting speed is the dominant parameter on all response parameters namely surface roughness, cutting force and temperature with 92.6%, 61.6% and 91.6% contribution rate respectively. For maximum surface quality, highest cutting depth (0.3 mm) and cutting speed (200m/min) along with lowest feed rate (0.15 mm/rev) should be employed. Such an approach is expected to be a guide for the practical applications where tempered steels are used in different engineering disciplines.

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.

Prediction of milling surface roughness based on DGLCM and neural network

With the manufacturing industry’s increasingly automated and intelligent development, traditional workpiece roughness measurement methods can no longer meet the requirements for short time, high efficiency, and online measurement. Therefore, this article proposes a roughness evaluation model for vertical milling workpiece roughness measurement and improves upon the traditional Gray Level Co-occurrence Matrix (GLCM) method to achieve high accuracy and robustness in measuring milling surface roughness. First, the milled surface is illuminated by a coaxial light source, and an image is captured using an industrial camera and a telecentric lens. Then, four features of the image are extracted using the Direction Measure-based Gray Level Co-occurrence Matrix (DGLCM) to construct a feature descriptor. Finally, Finally, create an RBF network to predict surface roughness. Through experimental comparison with traditional GLCM feature extraction methods, our proposed feature extraction method has a prediction error of only 2.01%, which is superior to traditional methods.

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.

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.

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.

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 %.

An Experimental Investigation of the Effects of Dressing and Grinding Parameters on Sustainable Grinding of Inconel 738 Used for Automated Manufacturing

The significant effect of the dressing process on the surface of the grinding wheel (GW) and the need to provide an optimal dressing condition are the requirements of reduction machining time and energy consumption in the sustainable grinding process. In this study, for the first time, the results of changes in the parameters of the dressing process and changes in the topography of the grinding surface on the surface roughness of the Inconel 738 have been presented using single-edge and four-edge diamond dressers. The use of minimum quantity lubrication (MQL) and wet condition are other variables in this study to reduce the consumption of cutting fluid and prevent its destructive effects on the environment. The results indicate that the MQL technique increases the grinding performance of Inconel 738 by reducing ground workpiece surface roughness and decreasing the coolant–lubricant consumption comparing to the conventional wet grinding process. Additionally, it has been found from the experimental results that applying a single-edge dresser generates finer topography on the grinding wheel and, consequently, has a better surface finish in the grinding process compared to the multipoint diamond dressing tool with the same dressing and grinding parameters. In other words, increasing the dressing feed rate during dressing of the grinding wheel using a multipoint dresser makes a finer wheel surface topography and as a result decreases the surface roughness of the ground workpiece compared to a single-edge dresser. With multipoint diamond tools, the grinding performance during the life of the dressing tool also tends to remain more consistent, which is a definite advantage in automated production. Therefore, application of a multipoint dresser leads to a reduction in dressing time and increased production capability.

Influence of wire electrical discharge conditioning on the grinding performance of metal-bonded diamond grinding tools

Wire Electrical Discharge Conditioning (WEDC) is an efficient non-conventional method for conditioning metal, resin, and hybrid bonded super-abrasive grinding tools. Since the diamond and CBN grains are not affected directly by the WEDC and only the electrically conductive bond material is eroded by the process, high grain protrusions are achievable. Therefore, unlike the common mechanical conditioning methods using WEDC, the microtopography of the grinding tool can be influenced by the process parameters, directly affecting the grinding efficiency. However, the selection of WEDC process parameters is influenced by the specifications of the grinding tool, particularly the grain size and concentration. This study aims to investigate the effects of WEDC parameters and strategies on the microtopography and material removal rate of metal-bonded grinding tools, considering different grain sizes and concentrations. Additionally, the correlation between the microtopography of grinding tools conditioned by the WEDC process and their grinding efficiency was investigated by analyzing tool wear, grinding forces, and surface roughness of tungsten carbide workpieces. The results indicate that the material removal rate of the WEDC process decreases as the grain size and concentration of metal-bonded diamond grinding tools increase. Furthermore, it was observed that employing high spark energy parameters reduces the grain protrusion of grinding wheels with small grain sizes. However, the utilization of high-energy sparks is necessary to create larger spark gaps and achieve a higher material removal rate.

Investigation of the Impact of High-Speed Machining in the Milling Process of Titanium Alloy on Tool Wear, Surface Layer Properties, and Fatigue Life of the Machined Object.

This article presents the results of experimental research on the effect of high-speed machining (HSM) in the milling process on the tool wear, surface layer properties, and fatigue life of objects made of Ti-6Al-4V titanium alloy. Titanium alloys are widely used in many industries due to their high strength-to-density ratio, corrosion resistance, and resistance to dynamic loads. The experiment was conducted on a vertical three-axis machining centre, Avia VMC800HS. The influence of increased cutting speeds on the average values and amplitudes of the total cutting force components and the surface roughness of the machined workpiece was determined. Variable cutting speeds vc = 70; 130; 190; 250; 310 m/min were applied. The impact of HSM on machinability indicators, such as the microhardness of the surface layer, the distribution of residual stresses, and the fatigue life of the samples after milling, was analysed. The thickness of the hardened layer varied from 20 to 28 micrometres. The maximum compressive residual stress Ϭm = 190 MPa was achieved at the speed of vc = 190 m/min. A significant influence of increased cutting speeds on tool wear was demonstrated. The longest tool life (t = 70 min) was obtained for low cutting speeds (conventional) vc = 70 m/min.