Ground Surface Roughness Research Articles

Vibration Response of Manual Wheelchairs According to Loads, Propulsion Methods, Speeds, and Ground Floor Types

Manual wheelchair (MWC) users are daily exposed to vibration during propulsion. The impact of such exposure on the MWC user’s health has yet to be proven. To date, no agreement has been reached, presumably on the account of the wide variety of experimental parameters that need to be controlled. A possible solution relies on the implementation of a User/MWC model to point out the effect of propelling conditions (MWC loads, propulsion methods, speeds, and ground floor types) on the vibration exposure and eventually on the MWC user’s health. To feed such a model, the evaluation of the MWC vibration response during propulsion is required. Following a necessary MWC experimental modal analysis under laboratory conditions, this study presents the vibration response of an MWC under various propelling conditions. For each investigated condition, the identified set of modal parameters was provided and the effect on the MWC response to vibration at the User/MWC interfaces was highlighted. Results mostly underline that the response to vibration is highly dependent on the propelling conditions. The speed and the ground floor type greatly affect the vibration response: doubling speed and increasing ground surface roughness imply threefold and eightfold vibration levels, respectively. Finally, the main outcome is that an empty MWC or an MWC loaded with a dummy generates vibration outside the range measured for an MWC loaded with a human body, resulting in a lower frequency content and an almost two-fold vibration level increase. The findings of this study will help enhance the understanding of the health risks that wheelchair users encounter as a result of vibrations.

Experimental Study on ELID Grinding of Silicon Nitride Ceramics for G5 Class Bearing Balls

This study has focused on analyzing the impact of material characteristics and grinding conditions on the surface roughness in ELID grinding of ceramic materials intended for bearing balls. The main research objective was to examine the feasibility of achieving the required surface roughness for G5 class bearing balls through a high-efficiency and high-precision ELID grinding process. Three types of silicon nitride specimens and two types of grinding wheels with cBN and diamond abrasives were prepared for the experiments. An HP (high-pressure) specimen was fabricated through high-temperature and high-pressure sintering at 1700 °C for 2 h, containing a composition of Y2O3 and MgO in Si3N4, while GPS 1hr and GPS 6hr specimens were prepared using gas-pressure sintering for 1 h and 6 h, respectively. From the experimental results, it has been confirmed through surface morphology and surface roughness analysis that material characteristics and grinding parameters affect the surface roughness of silicon nitride ceramics during the grinding process. The surface ground with a #2000 diamond wheel is at a level that can satisfy the required surface roughness, 0.014 um or less in G5 class bearing balls. Based on the analysis of surface morphology and roughness in grinding processes, the #325 cBN wheel exhibited excellent performance in rough grinding, while the #2000 diamond wheel demonstrated highly effective surface finishing performance, indicating that the combination of these two abrasives can be effectively utilized for high-efficiency and high-precision nanosurface machining of silicon nitride ceramics.

SiO2f/SiO2 composite grinding characteristics based on maximum undistorted chip thickness

ABSTRACT The thickness of the maximum undistorted chip (a gmax ) is a crucial component of the machining arguments and significantly affects the quality of the grind. For the purpose of investigating the impact of the a gmax on the grinding removal properties of SiO2f/SiO2 composites, grinding experiments of SiO2f/SiO2 composites were conducted. The findings indicate that crack propagation, fiber cracking, matrix breaking, and interface debonding are the primary removal modes of SiO2f/SiO2 composites. Grinding force, surface roughness, and chip size all have a positive correlation with the maximum undeformed chip thickness. Surface brittle fracture brought on by SiO2 fiber microcrack results in little surface roughness. The surface quality degrades when bending fracture and compression fracture take place. Controlling the a gmax can enhance the grinding surface quality of SiO2f/SiO2 composites.

Experimental Investigation on Microstructure Alteration and Surface Morphology While Grinding 20Cr2Ni4A Gears with Different Grinding Allowance Allocation.

Transmission gear is a key component of vehicles and its surface integrity affects the safety of the transmission system as well as the entire mechanical system. The design and optimization of allowances in form grinding are important for improving dimensional accuracy and machining efficiency during the manufacturing of heavy-duty gears. This work aims to investigate the effects of grinding allowance allocation on surface morphology, grinding temperature, microstructure, surface roughness, and microhardness fluctuation during the form grinding of 20Cr2Ni4A gears. Results indicated that grinding temperature was primarily influenced by rough grinding involving significant grinding depths exceeding 0.02 mm. The ground surface exhibited slight work hardening, while thermal softening led to a reduction in microhardness of around 40 HV. Ground surface roughness Ra varied from 0.930 μm to 1.636 μm, with an allowance allocation of the last two passes exerting the most significant influence. Analysis of surface and subsurface microstructures indicated that a removal thickness of 0.02 mm during fine grinding was insufficient to eliminate the roughness obtained from rough grinding. Evident ridges, gullies, and surface defects such as material extraction, adhesion, and plastic deformation were also observed. The proposed grinding strategy was validated in practical manufacturing with good surface quality and geometrical accuracy.

Multi-Objective Optimization of the Process Parameters of a Grinding Robot Using LSTM-MLP-NSGAII

Grinding robots are widely used in the automotive, mechanical processing, aerospace industries, among others, due to their strong adaptability, high safety and intelligence. The grinding process parameters are the main factors that affect the quality and efficiency of grinding robots. However, it is difficult to obtain the optimal combination of the grinding process parameters only by manual experience. This study proposes an artificial intelligence-based method for optimizing the process parameters of a grinding robot using neural networks and a genetic algorithm, with the aim to reduce the workpiece surface roughness and shorten the grinding time. Specifically, this is the first study utilizing a multi-objective optimization approach to optimize the process parameters of a grinding robot. Based on the experimental data of the grinding robot ROKAE XB7, the long short-term memory (LSTM) and multilayer perceptron (MLP) neural networks were trained to fit the quantitative relationships between the process parameters of the grinding robot, such as feed rate, spindle pressure and pneumatic motor pressure, and the result of grinding surface roughness and grinding time. After that, the non-dominated sorting genetic algorithm II (NSGA-II) was used to calculate the Pareto optimal process parameter combinations using the trained LSTM and MPL model as the objective function. Compared with the method based on manual experience, the process parameters optimized with this method achieved a reduction in surface roughness of at least 13.62% and a reduction in the whole grinding process time of 28%. The excellent grinding results obtained for grinding time and surface roughness validated the feasibility and efficiency of the proposed multi-objective method for the optimization of grinding robots’ process parameters in practical manufacturing applications.

Grinding surface roughness prediction for silicon nitride ceramics: A dynamic grinding force and frequency domain approach

The existing surface roughness theoretical predictive models exhibit high parameter sensitivity and complexity, resulting in low reliability. Moreover, machine learning-based predictive models are constrained by the training parameter range, limiting their precision and generalization. In response to these limitations and from the frequency domain perspective, this paper proposes an innovative surface roughness (Ra) predictive model for silicon nitride ceramics grinding based on the dynamic grinding force model. First, a dynamic grinding force model was established using single grain scratch test results from both ductile and brittle removal stages, with model parameter estimation assisted by Genetic Algorithm. The results demonstrated a Mean Relative Error (MRE) of 19.51% between experimental and theoretical dynamic grinding force values, and a Relative Error (RE) of 9.37% for average grinding force. Subsequently, the frequency domain characteristics of both experimental grinding force signals and surface topography texture curves were extracted, which exhibited a significant correlation based on cosine similarity and Kendall's tau. Finally, leveraging the frequency domain characteristics of grinding force, the surface roughness predictive model was developed through the Convolutional Neural Network. The predictive model achieved an MRE of 12.79%, with random grinding experiments further confirming the model's accuracy with an MRE of 13.65%. These findings indicate that the proposed surface roughness predictive model offers high generalization capability and accurate prediction across a broader parameter range.

Visual measurement of grinding surface roughness based on feature fusion

Due to the random surface texture and weak features of the grinding processing, most current visual measurement of grinding surface roughness is predicted by designing feature indicators, but its imaging environment is more demanding and the indicator design is more artificial. While deep learning can achieve feature self-extraction, the existing deep learning-based feature extraction uses a single convolutional model to extract features, which tends to make the extracted features noise, with low resolution and poor perception of details. To address the above problems, this paper proposes a feature fusion-based method for measuring grinding surface roughness. The method adopts different feature extraction ways and fuses the extracted features to obtain more features while also improving the generalization ability of the model, and verifies the adaptability of the model in different lighting environments. The experimental results show that deep learning self-extracted features based on feature fusion can effectively solve the problem of weak feature information on grinding surface roughness that is difficult to identify, and the model has high detection accuracy across different lighting environments, thus laying the foundation for the automated visual online measurement of grinding surface roughness.

Real-time prediction of grinding surface roughness based on multi-sensor signal fusion

Real-time prediction of grinding surface roughness based on multi-sensor signal fusion

Numerical prediction of surface morphology and roughness in rotary ultrasonic face grinding SiO2f/SiO2 composite

The SiO2f/SiO2 ceramic matrix composite has been applied to fabricate high-performance radar radome because of its good wave transmission and mechanical property. The machined surface morphology and roughness of SiO2f/SiO2 ceramic matrix composite have an important impact on the service performance of radome products. This paper presented a numerical model to predict surface morphology for grinding of SiO2f/SiO2 composite. The prediction model was based on finite element method (FEM). Both conventional face grinding (CFG) and rotary ultrasonic face grinding (RUFG) of SiO2f/SiO2 composite were FEM simulated by integrating the microstructure of SiO2f/SiO2, the constitutive as well as failure mechanism of fiber/matrix, and the coupling grinding effect of multiple abrasive grits. The machined surface morphology was predicted by extracting node coordinates on simulated ground surface. The numerical prediction results were quantitatively validated using experimental results of ground surface roughness Sa. The application of the proposed prediction model not only elucidated the influence law of fiber orientations and machining parameters on ground surface quality but also revealed the different shear failure modes of SiO2 fiber during grinding process. The ground surface of SiO2f/SiO2 was deteriorated by uncut-off debonding fibers and fiber peeling pits. Ultrasonic-assisted vibration was beneficial to improve ground surface quality by force reduction and hammering effect caused by intermittent machining. However, the key prerequisite for the ultrasonic hammering effect to exert its process advantage was that the ultrasonic hammering energy could effectively fracture fibers on the ground surface rather than being absorbed by crack propagation at the fiber–matrix interface.

Surface roughness prediction of SiCp/Al composites in ultrasonic vibration-assisted grinding

SiCp/Al composites are important materials in the automotive and aerospace fields. In order to improve surface quality of SiCp/Al composites, ultrasonic vibration assisted grinding was used to grind SiCp/Al composites. This paper observed and analyzed the microscopic morphology of the ground surface. Combined with the influence coefficient of axial ultrasonic vibration, a prediction model for the surface roughness of ultrasonic vibration-assisted grinding of SiCp/Al composites was established. Compared with the experimental results, its accuracy was verified. The results show that compared with conventional grinding, ultrasonic vibration-assisted grinding can improve surface roughness to a certain extent, and the maximum error of the surface roughness prediction model for ultrasonic-assisted grinding of SiCp/Al composites is 3.64 %. Finally, the grinding process was optimized using NSGA-II multi-objective optimization algorithm, the optimized grinding process parameters were vs = 35 m/s, vw = 0.7 m/min, ap = 9 μm and A = 5 μm.

Novel designed mechanical-mechanochemical synergistic micro-grinding technology and compounded abrasive micro-grinding tools

The mechanical and mechanochemical synergistic micro-grinding technology was proposed for grinding hard and brittle materials, especially for fused silica micro parts in this work. Compounded abrasive micro-grinding tools with diamond abrasives on the outer layer and cerium oxide abrasives on the inner layer were designed and fabricated. Efficient grinding was initially achieved by mechanical processing of super hard abrasives, and then ultra-precision processing was obtained by mechanochemical process to remove surface or subsurface damage. This study firstly analyzed the contact model of single diamond abrasive particle and theoretically evaluated ground surface roughness. The mechanism of abrasive layer wear was investigated for accurate error compensation. The formulation of cerium oxide abrasive layer and its processing efficiency were further analyzed. The material removal mechanisms of fused silica during mechanochemical grinding were also studied. Results showed that the material removal rate was 1.924 μm/min and the surface roughness Ra was 0.248 μm after mechanical processing. Subsequently, the surface roughness Ra was improved to 38 nm after mechanochemical processing. The study indicates that this technique is a promising approach for high-efficiency and high-quality grinding of fused silica.

Mechanism and enhanced grindability of cryogenic air combined with biolubricant grinding titanium alloy

Grinding is an essential method for ensuring the machining accuracy and surface quality of difficult materials, such as aerospace titanium alloys. However, the deterioration of workpiece surface integrity due to thermal damage is the technical bottleneck of titanium alloy grinding. Therefore, a new enabling grinding technology for cryogenic air combined with a biolubricant is proposed. The introduction of cryogenic air leads to fundamental changes in the physical properties and lubrication mechanism in the grinding zone. On this basis, the formulas between the cryogenic air temperature and biolubricant temperature, kinematic viscosity, and surface tension coefficient are established. Under the same grinding parameters, compared with dry grinding, the normal force, tangential force, specific grinding energy, friction coefficient, temperature, and surface roughness value of cryogenic air minimum quantity lubrication decrease by 47.6%, 44.3%, 45.1%, 19.8%, 43.9%, and 46.3%, respectively. Then, the influence law of single and multiple factors on the grinding effect is discussed. When the peripheral speed, feed speed, and grinding depth are 30 m/s, 6 mm/s, and 30 µm, the optimal combination of factor parameters is a cryogenic air temperature of − 50 °C, an air pressure of 0.4 MPa, and nozzle inclination angle of 16°. Furthermore, on the basis of the multifactor orthogonal experiment, the quantitative mapping relationship between different grinding and influence factors is investigated to achieve optimal workpiece surface quality. This research provides a new effective method for improving the high surface integrity of grinding titanium alloys by using the cryogenic air minimum quantity lubrication method.

Analysing the Impact of Cutting Force and Vibration on Surface Roughness in External Cylindrical Grinding of SKH2 Stell

Analysing the Impact of Cutting Force and Vibration on Surface Roughness in External Cylindrical Grinding of SKH2 Stell

Surface Integrity Analysis in Grinding of Dual-Phase High-Entropy Alloy

Abstract High-entropy alloys (HEAs) are highly anticipated because of their superb properties in strength, hardness, wear resistance, etc. However, compared with numerous studies on the design and properties of HEAs, the machinability research of HEAs is extremely rare, which limits the application of HEAs. In this work, grinding experiments of (FeCoNi)86Al7Ti7 dual-phase HEA workpieces were carried out, and the results are analyzed from a general machinability perspective (the machining parameters’ effect on grinding force and surface roughness) to a more in-depth perspective, including grinding-induced changes in morphology and microstructure on the ground surface and subsurface. With scanning electron microscope (SEM) and electron backscatter diffraction (EBSD) information of subsurface, the deformation mechanisms have been studied, including the role of the second-phase (Ni2AlTi) in the grinding process, the material removal modes of the different phases, and the morphology of the nanoprecipitates in the matrix, based on the completely opposite properties of different phases in HEA. It is noticed that the hard and brittle property of the second phase brings support to the material, reduces the plastic deformation, and also makes its own removal brittle, while the plastic matrix experiences shear deformation in grinding, which makes the nanoprecipitates in it assume different morphologies. These detailed findings could be of help to understand the effect of grinding on material properties so as to improve the machining quality of this material.

Synergistic Lubrication Mechanism of Nano-Fluid and Grinding Wheel Prepared by CNTs@T304 Nano-Capsules

Grinding fluid often struggles to enter the grinding area and overcoming this challenge has been a major focus of research in recent years. Therefore, CNTs@T304 nano-capsules are prepared by filling the cavities of CNTs with a lubricant of T304. CNTs@T304 nano-capsules were used as an additive in this paper to prepare resin grinding wheels and nanofluids, respectively. The resin wheels filled with nano-capsules were used for grinding under the lubrication of nanofluids, and T304 could then be released to the grinding area to play a self-lubricating role during grinding. First, CNTs@T304 nano-capsules were characterized, and the properties of the prepared grinding wheels and nanofluids were tested. Second, the effects of the filling of nano-capsules and grinding speed on the grinding force, grinding temperature, surface roughness, and grinding ratio were studied. Finally, the lubrication mechanism of the nano-capsules was revealed through surface analysis of the workpiece. The results suggested that nano-capsules had good thermal stability and the nanofluid prepared from them exhibited good dispersion stability and thermal conductivity. The grinding wheel was found to satisfy the service conditions when the filling content was less than 15%. Compared with a common wheel, the grinding force and grinding temperature were reduced by 24% and 28%, respectively, and the surface roughness of the workpiece and the grinding ratio were increased by 18% and by 21%, respectively, when grinding GCr15 steel with the nano-capsule wheel. Lubrication with nanofluids could further reduce the grinding force, grinding temperature, and surface roughness values. During grinding, the self-lubrication film formed by the T304 released from the nano-capsules in the wheel served first and foremost as a lubricant. The intervention of the nanofluid enhanced the heat-exchange effect and lubrication efficiency in the grinding zone.

Influence of Radiative Components and Meteorological Conditions on Simulation of Slope-Specific Heat Balance

Fields have uneven surfaces, such as ridges, and the shortwave radiation of fields differs depending on the orientation of the slope of the unevenness, resulting in variations in the distribution of moisture on the ground surface. Therefore, it is necessary to estimate the heat and water balances spatially, taking into account the variation of the moisture distribution on the ground surface. Previously, one-dimensional simulations have been used to estimate the heat and water balance of non-sloping surfaces. To estimate the heat and water balance spatially while taking into account ground surface roughness, it is necessary to first estimate the heat and water balance of the ground surface on the basis of slope orientation. The purpose of this study was to clarify those factors, in addition to shortwave radiation, that affect the heat balance of a bare sloping surface at different orientations. To achieve this, the heat balance calculated using observational data of bare ground, including unevenness, was compared with the heat balance estimated by HYDRUS-1D simulation for each slope orientation. Additionally, the brightness index of RGB images was calculated and compared with the relative ground surface brightness and the estimated heat balance for each slope orientation. The estimated results at night and at sunrise/sunset were extremely small in comparison with the calculated results, and the heat balance simulation in the absence of shortwave radiation remained an issue. The relationship between ground surface brightness and ground conduction heat was completely different depending on slope direction, suggesting that ground surface heat transfer is affected substantially by factors other than shortwave radiation related to slope orientation. The findings indicate that it is necessary to examine the effects of heat transfer in detail to estimate the heat balance related to slope orientation.

The grindability performance and measurement of surface functional parameter capabilities of difficult-to-machine tool steel under tangential ultrasonic-vibration-assisted dry grinding

The grinding performance of the finished component is significantly affected by the consistency and durability of the grinding mode used in its formation. The current research attempted to evaluate the influence of worktable feed rate and ultrasonic vibration amplitude on grinding outcomes responses such as ground forces, ground surface morphology, surface roughness and topography, surface bearing index, core fluid retention index, grinding temperature, and chip morphology. Experimental works were performed on a setup that was indigenously developed and manufactured. Experiments were conducted on AISI D2 tool steel workpiece material under the tangential ultrasonic-vibration-assisted dry grinding (TUDG), common dry grinding (CDG), and common flood grinding (CFG) modes to compare the effectiveness of each mode in terms of the responses of the grinding outcomes. A comprehensive comparative analysis of each grinding mode is demonstrated, along with observations of changes in the output responses under the effect of the investigated grinding parameters. Findings showed that under identical conditions, the TUDG mode’s surface bearing index and core fluid retention index was higher than that of the CDG and CFG modes. To elucidate these findings. Besides, small, thin chips generated in TUDG mode indicate the ease of grinding of AISI D2 tool steel.

A comprehensive investigation of surface morphology during grinding of Inconel 625 using conventional grinding wheels

Grinding difficult-to-grind (DTG) materials such as superalloys have shown an upward trend for manufacturing high-precision and durable components. Superalloys, such as Inconel, have superior thermo-mechanical properties that allow them to withstand caustic working conditions and simultaneously make their grindability difficult. In this context, the present work investigates the grinding performance of Inconel 625 (IN 625) using conventional grinding wheels in dry condition. The current investigation uses the alumina wheel and the silicon carbide wheel to keep the cost factor minimum. The outcome of the experiments has been evaluated in terms of grinding forces and surface roughness. Advanced surface characterization techniques such as Scanning electron microscopy, X-ray diffraction, Raman spectroscopy and X-ray photoelectron spectroscopy have been employed to analyze the surface morphology of the ground surfaces produced by both wheels. It has been observed that the alumina wheel has performed much better than the silicon carbide wheel for a higher depth of cut. These observations have also been supported by wheel loading, in which loading for the silicon carbide wheel was approximately four times that of the alumina wheel. Moreover, long and flow types of chips produced by the alumina wheel indicate the better suitability of the alumina wheel while grinding IN 625. The findings of the present work are expected to provide an economical way of grinding IN 625, preferably using an alumina grinding wheel.

Improved Geometric Optics with Topography (IGOT) Model for GNSS-R Delay-Doppler Maps Using Three-Scale Surface Roughness

Although multiple efforts have been made to model global navigation satellite system (GNSS)-reflectometry (GNSS-R) delay-Doppler maps (DDMs) over land, there is still a need for models that better represent the signals over land and can enable reliable retrievals of the geophysical variables. Our paper presents improvements to an existing GNSS-R DDM model by accounting for short-wave diffraction due to small-scale ground surface roughness and signal attenuation due to vegetation. This is a step forward in increasing the model fidelity. Our model, called the improved geometric optics with topography (IGOT), predicts GNSS-R DDM over land for the purpose of retrieving geophysical parameters, including soil moisture. Validation of the model is carried out using DDMs from the Cyclone GNSS (CYGNSS) mission over two validation sites with in situ soil moisture sensors: Walnut Gulch, AZ, USA, and the Jornada Experimental Range, NM, USA. Both the peak reflectivity and the DDM shape are studied. The results of the study show that the IGOT model is able to accurately predict CYGNSS DDMs at these two validation sites.

Estimating Manning’s coefficient n for sheet flow during rainstorms

Many hydrological and soil erosion models employ Manning’s coefficient (n) to calculate the velocity of sheet flow (SF). However, Manning’s coefficient of SF cannot be estimated accurately due to the strong influences of rainfall, slope gradient, ground surface roughness, sediment size, etc. The effects of rainfall, especially rainstorms, on Manning’s n are usually ignored. In this study, flume tests were conducted with rainfall intensities ranging from 40 to 120 mm h−1, unit width inflow discharges ranging from 0.02 to 0.06 m2 min−1, bed surface roughness values ranging from 0.009 to 0.25 mm, and slope gradients ranging from 3° to 7°. Manning’s n and the correction (Δnr) induced by raindrops were calculated. The results indicated that Manning’s n without rainfall increased with increasing surface roughness and decreasing unit width inflow discharge. In rainstorms, Manning’s n increased with increasing rainfall intensity and surface roughness and decreasing unit width inflow discharge. Δnr increased with increasing rainfall intensity and surface roughness and decreased slightly with increasing unit width inflow discharge. Manning’s n significantly decreased with increasing Froude number (Fr) according to a power function, while Δnr significantly decreased logarithmically with increasing Fr. The Δnr values of this study were greater than 0 when Fr was less than approximately 2.15. For slope gradients of 3°, 5° and 7°, the rainfall increased n by 1.47–49.33 %, 0.44–21.81 % and 0.43–23.94 % for the cases of Fr < 2.15, respectively. This result indicates that the effects of rainfall on Manning’s n of SF cannot be ignored for laminar flows and low Reynolds numbers (<900). Additionally, two empirical equations for predicting Manning’s n and Δnr under rainfall conditions were devised. The correction Δnr due to raindrop impact was affected by the inflow or base flow regimes. This study experimentally provides basic insights into the effects of rainfall on Manning’s coefficient.