Ground Surface Roughness Research Articles

Intelligent prediction of surface roughness of PSZ ceramic grinding based on correlation analysis and CNN-BiLSTM neural network

Partially stabilized zirconia (PSZ) ceramics are widely used in aerospace industry and other fields due to their superior performance. Surface roughness is a key indicator for evaluating the grinding level of PSZ ceramics. In order to reduce the prediction error of grinding surface roughness, an acoustic emission prediction model for PSZ ceramic grinding surface roughness based on correlation analysis and convolution-bidirectional long short term memory neural network (CNN-BiLSTM) was proposed. By analyzing the correlation between the eigenvalues of grinding acoustic emission signals and the grinding surface roughness values, the most relevant frequency bands and feature matrices between the grinding acoustic emission signals and the grinding surface roughness were selected as the input parameters of the CNN-BiLSTM neural network to reduce the error of acoustic emission prediction of grinding surface roughness. The research results show that the average prediction error of PSZ ceramic grinding surface roughness based on correlation analysis and CNN-BiLSTM neural network is less than 3.92%.

Piezoelectric Ultrasonic Local Resonant Ultra-Precision Grinding for Hard-Brittle Materials.

Hard-brittle materials are widely used in the optics, electronics, and aviation industries, but their high hardness and brittleness make it challenging for traditional processing methods to achieve high efficiency and superior surface quality. This study aims to investigate the application of ultrasonic local resonant grinding to sapphire to improve the efficiency and meet the requirements for the optical window in the surface roughness of the material. The resonant frequency of a piezoelectric ultrasonic vibration system and the vibration amplitude of a grinding head's working face were simulated and tested, respectively. The results of ultrasonic grinding experiments showed that the local resonant system reduced the surface roughness parameter (Ra) of sapphire to 14 nm and improved its surface flatness to 44.2 nm, thus meeting the requirements for the ultra-precision grinding of sapphire. Compared with a conventional resonant system, the surface roughness of the sapphire ground with the local resonant system was reduced by 90.79%, its surface flatness was improved by 81.58%, and the material removal rate was increased by 31.35%. These experimental results showed that ultrasonic local resonant grinding has better effects than those of conventional ultrasonic grinding in improving surface quality and increasing the material removal rate.

Simulation modeling of wafer grinding surface roughness considering grinding vibration

When using the workpiece rotation method to grind wafers, the grinding end vibration will deteriorate the surface roughness of the wafers. To study the impact law of vibration on the surface roughness of wafers during the grinding procedure, this paper presents a new approach to simulate and model the surface roughness of wafer grinding considering the grinding vibration. Firstly, the dynamics model under the consideration of grinding force was established for the grinding end of the grinding wheel and workpiece turntable. Secondly, using the iterative method to solve the dynamic equations that have been established, the vibration equation is obtained by fitting the displacement vibration curve of the end. Then, by reconstructing the surface grain of the gear teeth, a simulation model of wafer grinding surface roughness was established considering material removal, grain motion and grinding vibration. And then the grinding comparison test was conducted to compare the simulation and test surface roughness measurement results. The maximum deviation of the surface roughness Sz and Sa was 7.7 % and 5.4 %, respectively. The results indicate the accuracy of the modeling. Finally, based on the established wafer roughness model, explore the impact of vibration on wafer roughness during the grinding procedure. This model provides a reference for the research of wafer precision grinding technology.

A visual measurement method for grinding surface roughness combining filter and branch convolution network

ABSTRACT The visual measurement method of grinding surface roughness is mainly predicted by designing image feature indicators. In contrast, the self-extraction method of grinding surface features based on deep learning has problems such as noise, low resolution and poor perception of details. This paper proposes a visual measurement method for grinding surface roughness to address these problems by combining filters and branching convolutional networks. The method adopts a two-branch convolutional neural network, inputs the images using filter denoising and the original grinding workpiece surface images simultaneously, self-extracts roughness correlation features and fuses them to achieve feature enhancement, effectively improves the recognition accuracy and generalisation ability of the model, and verifies the robustness of the model in the case of rusting on the workpiece surface. The experimental results show that the method can effectively solve the problem of the grinding process’s weak and difficult recognition of surface roughness feature information and provide a basis for automated visual online measurement of grinding surface roughness.

Optimal design and performance evaluation of grinding wheels with triply periodic minimal surface lattice structure

High-performance grinding wheels are critical tools for precision surface generation. In precision grinding processes, the coolant supply at the wheel-workpiece interface is critical for reducing grinding temperature and detrimental friction. For better lubrication and chip removal performance, the cavities or pores need to be generated throughout the grinding wheel. The ideal morphology of pores in grinding wheels should be inter-connected and capable of providing sufficient coolant. Conventional grinding wheel design and fabrication methods can only passively generate closed circular pores by using pore-forming agents. Increasing the percentage of pores in this way generally leads to an uncontrollable reduction in mechanical strength, while the closed pores are insufficient in cooling performance. In this paper, the grinding wheels with triply periodic minimal surface lattice structure are designed and fabricated, which achieves the regulable and inter-connected internal structure of grinding wheels. Meanwhile, to balance the relationship between the structural properties and performance indicators of porous grinding wheels, an optimal design method for the porous structure of grinding wheels is proposed. Finally, the grinding performance of the novel grinding wheels in comparison to electroplated diamond grinding wheels is investigated in terms of grinding force, specific grinding energy and ground surface roughness.

Roughness constant selection for atmospheric boundary layer simulations using a k-ω SST turbulence model within a commercial CFD solver

Transit of an atmospheric boundary layer (ABL) profile within computational fluid dynamics (CFD) simulations is often hindered through unintended streamwise gradients in the flow resulting in ABL inhomogeneity. Within Reynolds-Averaged Navier–Stokes (RANS) turbulence modeling in Ansys Fluent, user-defined wall functions are not available for the k–ω class of RANS models as a remedy to this problem. Instead, the practitioner is required to use the sand–grain roughness property for ground surface roughness calibration, and specify a roughness height and roughness constant, Cs, accordingly. To-date, no clear guidance on their selection is available that can accommodate both high and low roughness terrains. To overcome this, a straightforward and practical calculation is presented for the roughness constant based on the standard wall function implementation. When this calculation is synthesized with other best-practice guidelines, it is possible to reliably model transiting ABL profiles based on wind-tunnel data whilst minimizing effects due to ABL inhomogeneity.

Surface micro-morphology model involved in grinding of GaN crystals driven by strain-rate and abrasive coupling effects

The complexity of the interaction between the workpiece and abrasives, the characterisation difficulty of the strain-rate effect, and the analytical difficulty of brittle-ductile coexistence removal pose significant challenges in surface micro-morphology modelling of brittle-solid grinding. To overcome these bottlenecks, a theoretical model of the normal scratching force driven by the strain-rate effect was developed to verify the strain-rate sensitivity coefficients of gallium nitride (GaN) crystals. Impact scratching tests with a single grit further emphasised that the brittle-to-ductile transition and subsurface damage behaviour of GaN crystals exhibited a distinct strain-rate dependence. Subsequently, a theoretical model of the surface micro-morphology involved in the grinding of GaN crystals was developed by comprehensively considering the strain rate, abrasive coupling effect, time evolution, abrasive randomness, and elastic-to-plastic and brittle-to-ductile transition depths. The simulated results of the model agreed well with the experimental results, with an average error of <10 %. The model indicated that the ground surface micro-morphology and roughness were insensitive to variations in the grinding depth. Under the allowable conditions of the grinder stiffness and dynamic balance, appropriately increasing the wheel speed and grinding depth, decreasing the feed speed, and refining the abrasive size could effectively improve the proportion of ductile removal during the grinding of brittle solids. The results not only enhance the understanding of the abrasive coupling effect on surface micro-morphological evolution, material removal, and damage accumulation, but also provide theoretical guidance for the parameter optimisation involved in the grinding of brittle solids.

Experimental study of picosecond laser-assisted grinding of GH4169 nickel-based superalloy

The nickel-based superalloy GH4169 exhibits excellent thermal stability and fatigue resistance, making it widely utilized in the aerospace and marine manufacturing industries. However, the high mechanical strength of the nickel-based superalloy GH4169 leads to issues such as high grinding forces during grinding and poor surface morphology of the material after grinding. To improve the processing quality of GH4169, this study employs ultrafast pulse laser texturing to create two distinct biomimetic structures on its surface. The study explores the impact of laser parameters on both the recast layer and surface hardness. Subsequently, grinding experiments are conducted on both structured and non-structured workpieces. The grinding forces and surface roughness during grinding are analyzed, and the surface morphology of the workpiece after grinding and the corrosion resistance of the material are also tested. According to the test results, the grinding forces of the fishbone-like and nacre-like structure workpieces are reduced compared with those of the non-structured workpieces, and the maximum tangential grinding forces are reduced by 38.54 % and 35.98 %, and the maximum normal grinding forces are reduced by 30.24 % and 29.6 %, respectively. In addition, the corrosion resistance of the surface of the structured workpieces after grinding is better compared to the non-structured workpieces, and the best corrosion resistance is obtained for the fishbone-like structure.

An oil‐on‐water minimum quantity lubrication technology for high‐quality grinding of carbon fiber reinforced thermoplastic composites

AbstractCarbon fiber reinforced thermoplastic (CFRTP) composites, known for their excellent mechanical properties and environmental sustainability, are highly sought‐after in aerospace, energy, and many other fields. Grinding could be used to realize the high‐accuracy and low‐damage machining of CFRTP components. However, due to the temperature sensitivity and water absorption of CFRTP, dry and flood lubrication grinding could cause thermal damage and hygrothermal aging, respectively. In addition, these methods could lead to dust and excessive cutting fluids harmful to the environment. Addressing these bottlenecks, this study is the first to propose using oil‐on‐water minimum quantity lubrication (OoW‐MQL) to grind CFRTP. Experiments including various lubricating conditions and grinding parameters were performed to investigate the grinding performance. Experimental results showed that OoW‐MQL with the oil–water proportion of 1:1 outperformed the other lubricating conditions, achieving the best surface quality with the lowest grinding forces and temperatures. The optimal oil–water proportion remained almost unchanged under different grinding parameters. Furthermore, importance analysis using the extreme gradient boosting (XGBoost) algorithm demonstrated that oil–water proportion was the most critical factor affecting surface roughness. This study proves the feasibility of OoW‐MQL and provides technical guidance for high‐quality grinding of CFRTP.Highlights Oil‐on‐water minimum quantity lubrication (OoW‐MQL) was proposed to grind carbon fiber reinforced thermoplastic (CFRTP) composites. The experimental results of grinding forces, grinding temperatures, surface roughness and surface morphologies were analyzed, which proved that OoW‐MQL with oil–water proportion (1,1) had outstanding lubricating and cooling capability. The effect of grinding parameters on the optimal oil–water proportion was proved to be negligible through comparative tests under grinding parameters of high force and high temperature. Quantitative analysis using the extreme gradient boosting (XGBoost) algorithm identified the oil–water proportion as the most critical factor influencing surface roughness.

Investigation of Single Grain Grinding of Titanium Alloy Using Diamond Abrasive Grain with Positive Rake Angle

Traditional grinding, which is predominantly performed with a negative rake angle (NRA), can be transformed into grinding with a positive rake angle (PRA) by employing femtosecond pulsed laser technology to modify the apex angle of the grains to be less than 90°. This innovative approach aims to reduce grinding forces and grinding temperatures while improving the surface quality of typical hard-to-machine materials. To assess the performance of PRA single grain grinding and to investigate the underlying mechanisms, the finite element simulation software ABAQUS 6.14 was employed to model the grinding of Ti6Al4V titanium alloy with a single diamond abrasive grain. The dependence of grinding force and temperature in single grain grinding with a PRA or an NRA under different grinding parameters was studied and compared. PRA and NRA single diamond grain grinding experiments on Ti6Al4V alloy were carried out, with grinding forces measured using a dynamometer and compared with the simulation results. The grinding surface morphology and surface roughness were observed and measured, and a comparison was made between PRA and NRA grinding. The results indicated that in single diamond grain grinding, transforming to a PRA significantly enhances grinding performance, as evidenced by reduced grinding forces, lower temperatures, improved surface morphology, and decreased surface roughness. These findings suggest that PRA single diamond grain grinding offers substantial benefits for the precision machining of hard-to-machine materials, marking a step forward in optimizing surface finishes.

Study on the grinding surface quality of CNTs/2009AL composite material based on minimum quantity lubrication

Carbon nanotube aluminum matrix (CNTs/AL) composites are ideal lightweight and high-strength materials due to their excellent properties. However, the research on CNTs/AL composites only stays in the simulation and preparation stages, and no grinding research has been carried out. Revealing the grinding mechanism of CNTs/AL composites has far-reaching significance for the development of high-end equipment industries such as aerospace. Taking CNTs/2009AL composites as the research object, a minimum quantity lubrication (MQL) flat grinding platform was built for comparing the surface quality and defects of dry grinding and MQL grinding. The experimental results show that grinding wheel velocity was the grinding parameter that had the greatest influence on surface roughness in dry grinding and MQL grinding. The subsurface defects of dry grinding mainly included microcracks, burrs, pits, coatings, cavities, etc., and the rebound phenomenon of agglomerated carbon nanotubes. The subsurface defects of MQL grinding were only burrs and pits. The surface coating phenomenon of dry grinding increased with the increase in grinding wheel velocity, and the surface quality was improved. MQL grinding could significantly reduce grinding heat, reduce friction, and make the machined surface smoother. In dry grinding and MQL grinding, the subsurface damage depth decreased obviously with the increase in grinding wheel velocity. When the grinding wheel velocity was 30 m/s, the minimum subsurface damage depth of dry grinding and MQL grinding was 40.091 and 26.906 μm, respectively. Mastering the grinding mechanism of grinding CNTs/AL composites and the influence of grinding parameters on surface quality provides a basis for the selection of parameters and methods in grinding in industry. MQL grinding of CNTs/AL composites reduces the formation of many surface defects and improves the surface quality of the workpiece. It is an efficient, clean, and environmentally friendly processing method.

Influence of grit friability and grit distribution pattern on dry grindability of WC-6Co by single layered brazed diamond wheels

Single-layered brazed diamond wheels, composed of uni-layered diamond grits, metal binder, and metal core, have potentially good thermal conductivity to dissipate heat from the grinding zone. Considering this characteristic feature, the current study aims to explore the performance and suitability of various indigenously developed single-layered brazed diamond tools in plunge-grinding of fine-grained cemented carbide in a challenging dry atmospheric condition. Single crystalline synthetic diamond particles with different strengths and friability were utilized to develop indigenous diamond wheels with random and patterned grit distribution. The current study explores the role of diamond-grit friability and grit distribution patterns to identify the most suitable wheel for dry grinding of WC-6Co composite. Detailed analysis of the wheel morphology, signifying the fracture behavior of the diamond grit used, justified the variation in the grinding forces, tool life, and ground surface roughness. Irrespective of the grit and distribution type, all wheels showed saturation of dynamic changes in work-surface topography with the number of passes and more sensitively to the degree of increase of uncut chip thickness. Grinding wheels containing friable diamond grits with irregular granular structures performed better than the high-strength, cubo-octahedral grains during the dry grinding experiments.

Surface Roughness Model of Ground 4H-SiC Considering Ductile and Brittle Removal

Abstract Surface roughness is a critical indicator to evaluate the quality of 4H-SiC grinding surfaces. Determining surface roughness experimentally is a time-consuming and laborious process, and developing a reliable model for predicting surface roughness is a key challenge in 4H-SiC grinding. However, the existing models for surface roughness in wafer rotational grinding fail to yield reasonable results because they do not adequately consider the processing parameters and material characteristics. In this study, we proposed a new analytical model for predicting surface roughness in 4H-SiC wafer rotational grinding, which comprehensively incorporates the grinding conditions and material characteristics of brittle substrate. This model derives and calculates the material's elastic recovery coefficient based on contact mechanics and elastic contact theory. Subsequently, we modified the grain depth-of-cut model by incorporating elastic recovery coefficient. Additionally, we analyze the distribution of the failure mode (ductile or brittle) on the surface of a material when the depth at which the material is cut instead follows a random distribution known as the Rayleigh distribution. To validate the accuracy of the established model, a series of grinding experiments are conducted using various grain depth-of-cut to produce 4H-SiC wafers with different surface roughness values. These results are then compared with those predicted by both this model and the traditional model. The findings demonstrate that the calculated data obtained from the proposed model exhibit better agreement with the measured data. This research addresses the need for an improved surface roughness model in 4H-SiC wafer rotational grinding.

Changeable environment visual detection of grinding surface roughness based on lightweight network

ABSTRACT The texture features of the grinding surface are not obvious, when the light source environment and shooting angle change, the prediction model of grinding surface roughness based on the deep learning method is greatly affected. Moreover, when applied to the mobile end or the embedded end, the model requires smaller size and faster inference time. This paper proposes a surface roughness detection model for grinding based on lightweight network MoCoViT, which can be applied to changeable imaging environments. This model uses the advantages of Transformer and mobile convolutional networks to improve performance and efficiency, so as to complete the recognition of grinding surface roughness in a transformable image environment. For different angles and different light sources, three experiments are designed to test the recognition effect of the model under unknown angles and unknown lighting conditions. The experimental results show that the grinding surface roughness detection model based on MoCoViT network has the highest accuracy in the test set. It proves that MoCoViT model has good robustness to the grinding surface roughness detection in the image changeable environment, which lays a foundation for the industrial application of online visual detection of grinding surface roughness.

Investigation of the Machined Surface Integrity of WC-High-Entropy Alloy Cemented Carbide

A fine-grained WC-15wt%Al0.5CoCrFeNi cemented carbide was prepared through a vacuum and gas pressure sintering. For achieving high surface integrity, diamond wheel grinding serves as the primary molding process for the machining of WC cemented carbide. To reveal the influence of grinding on the surface integrity of fine-grained WC-HEA cemented carbide, studies were conducted on grinding force, surface microstructure, surface roughness, residual stress, microhardness, and bending strength. The morphological analysis of the ground surface indicated a transition in the material removal mechanism of WC-HEA cemented carbide from ductile removal to brittle removal, with brittle removal becoming predominant as the depth of grinding increases. With the increasing depth of grinding, the grinding force increases, and the grinding force increases while the surface roughness decreases. Correspondingly, there is an improvement in both hardness and bending strength. Additionally, grinding induces high residual compressive stress on the surface, with a maximum compressive stress of 1795 MPa. The bending strength of the material is found to be dependent on the residual stress.

Implementation of hybrid CryoMQL sustainable lubri-cooling to enhance the grindability and surface integrity of tool steel

The current demand for surface finishing processes that are both technically proficient and environmentally friendly is increasing, necessitating the advancement of more efficient sustainable methods. Minimum quantity lubrication (MQL) provides excellent lubrication in the tool-work interaction but insufficient cooling impact. Besides, cryogenic enhances the chilling effect but has inadequate lubrication. This research revealed the synergistic impact of hybrid CryoMQL with eco-friendly Al2O3 NFs while grinding AISI D2 tool steel. CryoMQL facilitates the application of MQL-based soybean oil and deionized water (SO+DW) emulsion with 0.5 wt% and 1 wt% concentration of Al2O3 NPs and liquid nitrogen (LN2) through two individual nozzles in the cutting region. The results indicated that CryoMQL reduced grinding force (tangential force, Ft, and normal force, Fn), specific grinding energy (Ug), surface roughness (Ra), and grinding temperature (Tg) by 3.64–25.66 % in Ft, 3.52–12.87 % in Fn, 3.64–25.66 % in Ug, 8.81–25.02 % in Ra and 57.59–58.30 % in Tg, respectively, compared to MQL environment at 0.04 mm downfeed and 150 mm/s table feed rate. Eventually, it was observed that 1 wt% Al2O3 NFs improve Ft, Fn, Ug, Ra and Tg by up to 32.71 %, 20.33 %, 32.71 %, 23.55 %, and 6.81 % in comparison to 0.5 wt% Al2O3 NFs in the CryoMQL mode. The effectiveness of Al2O3 nanofluids with CryoMQL has resulted in good tribological conditions in the grinding zone with enhanced cooling/lubricating action of vegetable oil. Furthermore, negligible chip adhesion over finished surfaces was seen with 0.5 wt% and 1 wt% Al2O3 NFs under CryoMQL grinding. This study provides numerous scientific assistance in the automotive and tooling sectors, such as a better selection of environmentally friendly coolants and sustainable parameters for excellent surface properties, to provide the solution to highly complex and close tolerances of tool steel parts using CryoMQL finishing.

A novel comprehensive framework for surface roughness prediction of integrated robotic belt grinding and burnishing of Inconel 718

Surface roughness serves as a critical metric for assessing surface integrity, directly influencing the service performance of components. Hardening burnishing is a regularly used post-grinding procedure for Inconel 718 that can simultaneously reduce surface roughness and improve surface mechanical characteristics. A novel comprehensive framework is proposed to predict the surface roughness of Inconel 718 processed by the integrated robotic belt grinding and burnishing system developed in this study. Accounting for abrasive grain conditions and material removal rate during grinding, the L-BFGS-B optimization algorithm and image processing are utilized to predict the ground surface roughness first. Building upon this foundation, theoretical models are applied to ascertain the critical burnishing force needed to enter the hardening burnishing stage. Following this, the Expansion Cavity Model and Hertz theory are used to predict the roughness of the hardening burnished surface, which is only applicable when the step-over value exceeds a critical value. The proposed framework not only enhances our comprehension of the mechanisms underlying material surface deformation but also offers a novel avenue for predicting surface roughness during integrated robotic belt grinding and burnishing with high accuracy.

Trajectory Control of Designed Experimental Mobile Robot

Due to advancing technology; nowadays mobile robot applications in hospitals have been increased. For that reason, it is very important and necessary to analyze the trajectory of such helping robotic system. However; there are many types of mobile robots have been utilized in hospital applications such as helping nurses. In this simulation study; a designed and controlled mobile robot was controlled by using standard feedback controllers. On the other hand, the robot was also tested with disturbances of ground surface roughness. The simulation results were improved that standard PID controller has superior performance to overcome surface roughness of the robot trajectory.

Preparation and performance of high entropy alloy reinforced Cu-based bond diamond tool

The research on the influence of hot-press sintering on the properties of AlFeCrCoNi high-entropy alloy powders reinforced copper (HEA/Cu) alloys and the application of HEA/Cu alloys in diamond tools is still limited. Therefore, a new metal bond based on hot-press sintered HEA/Cu alloy for the diamond tool is proposed in this paper. The effect of adding HEA powder on the microstructure, mechanical, and tribological properties of the HEA/Cu alloy is investigated. A metal bond diamond tool based on the HEA/Cu alloy is prepared by hot-press sintering, and its grinding performance on alumina ceramic is examined. The results show that mutual element diffusion occurs between the HEA and Cu matrix during sintering, densifying the HEA/Cu alloys. The Cu alloy presents a higher hardness, yield strength, and wear resistance with a higher content of HEA powder. The hardness, yield strength, and wear resistance of Cu alloy are increased by 53.4%, 332%, and 200%, respectively, with the addition of 20 wt% HEA powders. The HEA/Cu bond diamond tool presents grinding forces and surface roughness equal to those of a commercial Cu-based alloy bond diamond tool.

Grinding Surface Roughness Measurement Combined with Simulation Data and Transfer Learning

AbstractThe surface‐roughness machine vision measurement method requires training numerous samples with uniform roughness values to construct a prediction model. Insufficient actual sample size will make the model lack generalization ability. So, a grinding surface‐roughness measurement method combined with simulation data and transfer learning is proposed. First, through surface simulation, inverse fitting, and three‐dimensional (3D) modeling, more realistic simulation samples are obtained, real samples are prepared, and a simulation imaging system and an actual imaging experiment to acquire surface images are designed. Second, the data distribution difference between the simulation domain and the actual domain through transfer component analysis and the use of the adjusted simulation‐domain data and its label to train a support vector regression model is adjusted. Finally, the model predicts the surface roughness of the uncalibrated actual‐domain sample. For shared features of transferable simulation images and actual images, using the high sensitivity of color information, image features are enhanced and a proportion of pure color region index is designed, strongly correlated within the domain but less differentiated between domains. The results show that the transfer learning prediction accuracy based on this index exceeds 90%, verifying the feasibility of the proposed method and providing a new improvement strategy for sample expansion in visual roughness measurement.