Optimization Of Grinding Parameters Research Articles

Optimization of grinding parameters in robotic-assisted preparation of cracked teeth based on fracture mechanics: FEA and experiment

Optimization of grinding parameters in robotic-assisted preparation of cracked teeth based on fracture mechanics: FEA and experiment

Experimental study on grinding process of Al-Mg2Si aluminum matrix composites

As an aluminum-based reinforcement, Mg2Si particles have excellent properties such as high hardness, high melting point, and high elastic modulus compared with other reinforcements and are widely used in aerospace and automobile fields. However, there is little research on the cutting mechanism of this material. In order to explore the surface grinding mechanism of Mg2Si/Al composites, Grinding simulation of low-volume fraction Mg2Si/Al composites was carried out by ABAQUS, and the influence of different positions of matrix and particle removal on the surface quality of Mg2Si/Al composites was expounded. The plane reverse grinding test was carried out by using CBN (boron nitride) grinding wheel. The L16(43) orthogonal test and three groups of single factor tests were designed. The orthogonal results show that the linear velocity of the grinding wheel has the greatest influence on the surface roughness, and the feed rate and grinding depth are significantly smaller. The range analysis shows that the optimal grinding parameters are: vs = 35 m/s, vw = 0.75 m/min, ap = 0.015 mm. The regression equation for surface roughness was established by using MATLAB. The single factor results show that the surface quality is obviously improved by increasing the linear velocity of the grinding wheel and reducing the feed rate and grinding depth through the observation of the morphology of the processed specimen. The analysis results show that defects such as pits, protrusions, burrs, and a small amount of fish-scale-like on the machined surface are mainly caused by the linear velocity of the grinding wheel, the pulling force of the grinding wheel, and the adhesion of the debris. The defects such as particle micro-cracks, particle pull-out, and matrix cracking on the subsurface, are mainly caused by the compressive stress of the grinding wheel and the stress concentration of the particles. The research results have guiding significance for future composite material cutting research and actual cutting processing.

Prediction model of grinding roughness for worm with complicated space tooth surface

Optimum grinding parameters are crucial for achieving high-quality grinding of helical tooth surfaces for worm drives. The complex coupling motion relationship and formation mechanism between the worm blank and the grinding head make it difficult to predict the effect of grinding parameters on tooth surface roughness. Therefore, we developed a prediction model for the worm tooth surface roughness under conjugate grinding and applied it to the Roller Enveloping Worm Reducer (REWR). The forming process of the theoretical worm tooth surface during the grinding process was modeled by using the conjugate meshing principle. Considering the plowing and cutting effects of grinding particles on the tooth surface at different design parameters, feed speeds, and rotational speeds of the grinding head, the roughness prediction equation was established based on the theory of the maximum depth of valley bottom. Through the coupling analysis of the theoretical tooth surface model and the roughness prediction model, the prediction of the worm tooth surface roughness is realized. Based on the validated model, the influences of grinding parameters on the worm tooth surface roughness of REWR were quantitatively discussed. This study is of great significance for optimizing process parameters in the grinding of complex spatial surface transmissions.

Multi-Objective Optimization of Surface Integrity in the Grind-Hardening Process

Grind-hardening machining is a new integrated manufacturing technology that integrates the theory of material surface quenching and grinding machining. The surface integrity of grind-hardening directly affects the performance and reliability of the parts. Improving the grind-hardening quality has always been the focus and difficulty in this field. Based on the surface integrity theory and the characteristics of the grind-hardening process, this paper proposed four optimization criteria for grinding parameters according to the engineering application requirements of materials. Using the expectation function, the burr cross-sectional area, depth of the effective hardened layer, and surface roughness were comprehensively analyzed under each optimization criterion to obtain an optimal combination of grinding parameters. The results revealed a significant inconsistency in the optimized grinding parameters under each optimization criterion. When considering the depth of the effective hardened layer as the primary optimization parameter and ignoring the surface roughness and burr cross-sectional area, the highest overall desirability was 0.926395. In practical application, the optimization criteria should be reasonably selected according to the actual engineering requirements.

Preparation and performance of 10 wt% of chlorantraniliprole Nano-suspension concentrate (SC) using polyether amine-bridged sulfonated alkali lignin (PSAL) as a dispersant

Polyether amine-bridged sulfonated alkali lignin (PSAL) with different sulfonic group contents and molecular weights was firstly synthesized and characterized. Then, 10 wt% of chlorantraniliprole Nano-suspension concentrate (SC) was prepared by the water-based grinding method, and the optimum grinding parameters were explored and obtained. Based on this optimum grinding formula, chlorantraniliprole Nano-SC was prepared and three optimum dispersants were finally obtained as follows: the compounds of polycarboxylate D-2 separately with P1SAL0.68, P1SAL0.84 and TERSPERSE 2500 at the mass ratio of 1:1. All Nano-SC prepared by these three dispersants had a D90 particle size below 200 nm, suspension percentage above 99%, water separation proportion below 1%, excellent thermal storage stability, and also better wettability on banana leaf surface. Additionally, adding glycerin could effectively inhibit the agglomeration and austenogenization of pesticide nanoparticles, and thus improve the stability of Nano-SC. Finally, the farmland experiments indicated chlorantraniliprole Nano-SC prepared in this work had a significantly improved pesticide effect on first-instar rice leaf rollers than traditional micron-sized SC. This work provided a new dispersant with excellent performance for chlorantraniliprole Nano-SC as well as broadened the high value-added utilization of industrial lignin into pesticide Nano-SC field, which had great economic and environmental benefits.

A Novel Approach to Optimizing Grinding Parameters in the Parallel Grinding Process

Hard materials have found extensive applications in the fields of electronics, optics, and semiconductors. Parallel grinding is a common method for fabricating high-quality surfaces on hard materials with high efficiency. However, the surface generation mechanism has not been fully understood, resulting in a lack of an optimization approach for parallel grinding. In this study, the surface profile formation processes were analyzed under different grinding conditions. Then, a novel method was proposed to improve surface finish in parallel grinding, and grinding experiments were carried out to validate the proposed approach. It was found that the denominator (b) of the simplest form of the rotational speed ratio of the grinding wheel to the workpiece has a great influence on surface generation. The surface finish can be optimized without sacrificing the machining efficiency by slightly adjusting the rotational speeds of the wheel or the workpiece to make the value of b close to the ratio (p) of the wheel contact width to the cross-feed distance per workpiece revolution. Overall, this study provides a novel approach for optimizing the parallel grinding process, which can be applied to industrial applications.

Grinding optimization using nondestructive testing (NDT) and empirical models

Proper choice of grinding parameters is important because productivity is limited by the possibility of grinding burns. Nondestructive testing (NDT), as Barkhausen noise (BN), can provide a tool for determining parameter limits. However, BN requires a threshold value for approval and requires multiple experiments to provide grinding parameters corresponding to high productivity. Other limiting process outputs, such as surface roughness, need to be fulfilled. In this study, process modeling, experiments and NDT are combined to produce optimized grinding parameters that fulfill the process output requirements and reduces the number of experiments for a new component. Design of experiments (DOE) grinding tests were performed with cutting power measurements. The outcome was verified with BN and surface roughness outputs. Then, using polynomial fitting, regression models were fitted into the BN, surface roughness and cutting power data. Malkin’s temperature model was utilized to analyze the temperature rise in the grinding zone. Combining all models, a grinding productivity optimization problem was defined from a specific material removal rate perspective. The polynomial fits of BN results and cutting power showed good prediction capability, as expressed by their R2 values. The study provides a novel method to choose grinding parameters to produce quality parts with minimal loss of productivity.

Determination of the Optimal Jar Mill Setting for Crushed Eggshells Used in LDPE Tile Creation

In this study eggshells and low density polyethylene plastic (LDPE) wastes were reused to produce tiles. The eggshells were ground in a laboratory scale jar mill into the particle size of fine sand. The optimum grinding parameters were determined to be the setting that produced the highest mass of eggshell powder that was within the desired particle diameter size range of 0.425 mm (No. 40) and 0.075 mm (No. 200). A two to the three factorial design of experiment was used with variables of Time, Speed, and Ball Filling Ratio. The high and low values used in this study were 20 minutes and 10 minutes for the time, 250 rpm and 100 rpm for the speed, and 35% and 20% for the ball filling ratio. There were eight settings made with 3 trials per setting and one validation for the optimal setting produced. The eggshell powder produced by the optimal setting of the jar mill was used to create the LDPE-eggshell tiles. Four tiles were made for the three trials and for the validation part. The tiles were tested for its relative strength using the Compression Testing Machine. The researchers determined that eggshells and LDPE plastics may be formed into tiles and that the strength of the tiles that were produced by these materials exceeded the standard value set for this type of product.

Energy conservation and consumption reduction in grinding operations through ceramic media stirring mill: An industrial validation test

This paper demonstrates the feasibility of using a ceramic medium stirring mill to reduce the energy consumption of grinding operations through an industrial verification experiment. Compared with the original two-stage ball milling process, the cost of grinding power consumption is significantly reduced by about 50.16%, which provides a reference for the subsequent research on energy saving and consumption reduction in ball milling operations. The optimal grinding parameters were determined to be grinding concentration of 50%, material ball ratio of 0.6, 3:2:5 media ratio (10:15:20 mm) of 3:2:5, medium filling rate of 90%, as well as stirring speed of 110 rpm. Under the optimum grinding parameters, an iron concentrate with an iron grade of 68.18% and an iron recovery rate of 94.17% were obtained by two-stage magnetic separation. The successful case of ceramic medium stirring mill replacing two-stage ball grinding provides a feasible solution for energy conservation and consumption reduction in mines in China.

Energy Prediction Models and Distributed Analysis of the Grinding Process of Sustainable Manufacturing.

Grinding is a critical surface-finishing process in the manufacturing industry. One of the challenging problems is that the specific grinding energy is greater than in ordinary procedures, while energy efficiency is lower. However, an integrated energy model and analysis of energy distribution during grinding is still lacking. To bridge this gap, the grinding time history is first built to describe the cyclic movement during air-cuttings, feedings, and cuttings. Steady and transient power features during high-speed rotations along the spindle and repeated intermittent feeding movements along the x-, y-, and z-axes are also analysed. Energy prediction models, which include specific movement stages such as cutting-in, stable cutting, and cutting-out along the spindle, as well as infeed and turning along the three infeed axes, are then established. To investigate model parameters, 10 experimental groups were analysed using the Gauss-Newton gradient method. Four testing trials demonstrate that the accuracy of the suggested model is acceptable, with errors of 5%. Energy efficiency and energy distributions for various components and motion stages are also analysed. Low-power chip design, lightweight worktable utilization, and minimal lubricant quantities are advised. Furthermore, it is an excellent choice for optimizing grinding parameters in current equipment.

Optimization of Grinding Parameters for the Mechanochemical Activation of Kaolin with the Addition of Trass

At present, to achieve further reductions in CO2 emissions in the cement industry, it is essential to improve the efficiency of grinding processes and reduce the energy demand. This study examined the effects of various grinding parameters (addition of trass, ball to powder mass ratio (BPR), material of grinding bodies and jars, rotational speed, and mill type) to minimize the energy consumption of the mechanochemical activation of kaolinite. X-ray diffraction, Fourier transform infrared spectroscopy, inductively coupled plasma optical emission spectroscopy, scanning electron microscopy, and specific surface area measurements were used to examine the influence of grinding parameters. It was found that the addition of as little as 25% (mass percent) trass reduced the specific energy demand for the complete amorphization of kaolinite by 56%. The application of steel grinding bodies (instead of ZrO2 ones) had a slight influence on the amorphization kinetics of kaolinite, but it could mechanochemically activate 30% more samples at the same BPR and specific energy demand. The use of the four-pot milling instead of the one-pot could considerably decrease the specific energy demand of the complete and incomplete (α = 0.9) amorphization of kaolinite. Overall, a 94% reduction was achieved in specific energy demand with steel grinding material, 14:1 BPR, four-pot milling, and the incomplete amorphization of kaolinite.

Comparative Cutting Fluid Study on Optimum Grinding Parameters of Ti-6Al-4V Alloy Using Flood, Minimum Quantity Lubrication (MQL), and Nanofluid MQL (NMQL)

Titanium alloys have been of paramount interest to the aerospace industry due to their attractive characteristics. However, these alloys are difficult to machine and require grinding post-processes for quality assurance of the products. Conventional grinding takes a long time and uses a flood coolant-lubrication technique, which is not cost effective nor environmentally friendly. Several studies have been performed to prove the viability and benefit of using Minimum Quantity Lubrication (MQL) with vegetable or synthetic-ester fluids. This work aims to find the optimum grinding parameters of creep feed grinding Ti-6Al-4V with a green silicon carbide wheel, using a flood lubrication system with water-soluble synthetic oil, MQL with ester oil, and nano-MQL (NMQL) using alumina-nanopowder homogeneously dispersed within an ester oil. It is concluded that at 0.635 mm and 1.27 mm infeeds, the three lubrication methods performed similarly. At an infeed of 1.905 mm, MQL did not provide desirable quality, though NMQL and flood lubrication performed practically identically. At a cross feed of 0.254 mm, an infeed of 1.27 mm, and a table feed rate of 6.7 m/min, these grinding parameters provide a material removal rate of 2163 mm3/min with a surface roughness across (Ra) of 0.515 µm. These parameters provide the quickest material removal rate while still maintaining industrial quality. This conclusion is based on environmental, economic, and qualitative results.

Investigation on Surface Integrity of Nodular Cast Iron QT700-2 in Shape Adaptive Grinding

Nodular cast iron QT700-2 is extensively used in automobile engine crankshaft parts due to its prime mechanical properties. The journal of a crankshaft is a curved surface, and traditional wheel grinding easily causes grinding burn and surface and subsurface damage. Shape adaptive grinding (SAG) is a flexible grinding technology, which has the advantages of low grinding force and temperature, and good grinding quality. It is suitable for machining curved surface parts with complex shapes. Therefore, the SAG surface integrity of nodular cast iron QT700-2 was experimentally investigated. The influence of grinding parameters on grinding force, material removal rate, grinding temperature, and surface integrity was studied, and the machining performance of SAG tools was evaluated. It was concluded that the grain size in SAG is the most important factor affecting the grinding force, material removal rate, and surface roughness; the influence of SAG grinding is very weak, mainly removing the workpiece material. Then, the influence law of SAG technology on the surface integrity of nodular cast iron QT700-2 was summarized, and the optimal grinding parameters were obtained, providing a reference for the curved surface grinding of nodular cast iron QT700-2 in the future.

Surface grinding responses optimization with a promising eco-friendly cutting fluid

Lubrication and cooling are very important to safeguard grinding quality, in terms of surface integrity, surface texture, and accuracy of dimensions of the workpiece, because of the friction and the heat generation associated with the surface grinding process. So, cutting fluids are used to achieve this. Due to the ecological and health hazards of cutting fluids formulated from mineral oil and chemicals, the focus is on the use of bio-oil-based cutting fluids. Owing to the desirable properties of cashew nut shell liquid, it is attempted as a cutting fluid to optimize the surface roughness and cooling in surface grinding of EN8 material. The study deals with the analysis of the grinding parameters such as the type of cutting fluid, grinding wheel speed, grinding wheel grade, depth of cut, and workpiece speed as influential factors on the responses i. e. surface finish and grinding temperature. The performance of cashew nut shell liquid is compared to traditional synthetic cutting fluid using Taguchi’s 25 experimental design and L16 Orthogonal Array. A prediction model is proposed for both surface roughness and grinding temperature to get the optimum grinding parameters. The accuracy of the models is tested by conducting validation and confirmation experiments. The experimental results confirm well with those predicted and show that there is an improvement in the surface grinding responses when cashew nut shell liquid is used as a cutting fluid.

Optimization of Grinding Parameters of Tool Steel by the Soft Computing Technique.

Grinding is one of the most complex and accurate machining processes, and the efficiency of the grinding wheel depends significantly on its surface properties. This work aims to propose an algorithmic manner that reduces the cost and time to conduct grinding of an optimized DIN 1.2080 tool steel (SPK) using a soft computing technique to obtain the best combination of input parameters including depth of cut (20, 40, 60 μm), wheel speed (15, 20, 25 m/s), feed rate (100, 300, 500 mm/s), and incidence angle (0, 30, 45 de grees) with respect to output parameters consisting of average surface roughness and specific grinding energy. According to the input parameters and their levels, an experiment using fractional factorial design of experiment (RFDOE) was designed. Later on, two parallel feed-forward backpropagation (FFBPNN) networks with similar topology made up of 4, 11, and 1 units in their input, hidden, and output layers are trained, respectively. After sensitivity analyses of networks for determination of the relative importance of input variables, a genetic algorithm (GA) adopting linear programming (LP) based on Euclidean distance is coupled to networks to seek out the best combinations of input parameters that result in minimum average surface roughness and minimum specific grinding energy. The findings revealed that RFDOE provides valid data for training FFBP networks with a total goodness value of more than 1.99 in both cases. The sensitivity analyses showed that feed rate (38.97%) and incidence angle (33.94%) contribute the most in the case of average surface roughness and specific grinding energy networks, respectively. Despite the similar surface quality based on scanning electron microscopy (SEM), the optimization resulted in an optimized condition of the depth of cut of 25.23 μm, wheel speed of 15.02 mm/s, feed rate of 369.45 mm/s, and incidence angle of 44.98 de grees, which had a lower cost value (0.0146) than the optimum one (0.0953). Thus, this study highlights that RFDOE with a hybrid optimization using FFBP networks-GA/LP can effectively minimize both average surface roughness and specific grinding energy of SPK.

Experimental and finite element investigation of precision multi-grain grinding of pure titanium

Pure titanium composed of α single-phase has important applications for its excellent mechanical, physical, and chemical properties, which also impose high requirements on its surface finish. However, pure titanium is a difficult-to-machine material due to its low mass transportation caused by the low stiffness and the low thermal conductivity. Thus, it is greatly needed to investigate the machinability of pure titanium by precision grinding. In the present work, we carry out experimental and finite element simulation studies on the precision multi-grain grinding of pure titanium. Specifically, finite element model of pure titanium grinding using grinding wheel with multiple grains is established, which enables the prediction of successive material removal behavior by neighboring grains in line with experiments. The machining behavior of pure titanium and its correlation with machined surface morphology and grinding force are studied by experiment, and are further revealed by finite element simulation. Subsequent grinding experiments are performed to study the effects of workpiece speed and grinding depth on the ground surface quality of pure titanium, with which a surface roughness of 0.27 μm is obtained under the optimized grinding parameters.

Modelling of grinding mechanics: A review

Grinding is one of the most widely used material removal methods at the end of many process chains. Grinding force is related to almost all grinding parameters, which has a great influence on material removal rate, dimensional and shape accuracy, surface and subsurface integrity, thermodynamics, dynamics, wheel durability, and machining system deformation. Considering that grinding force is related to almost all grinding parameters, grinding force can be used to detect grinding wheel wear, energy calculation, chatter suppression, force control and grinding process simulation. Accurate prediction of grinding forces is important for optimizing grinding parameters and the structure of grinding machines and fixtures. Although there are substantial research papers on grinding mechanics, a comprehensive review on the modeling of grinding mechanics is still absent from the literature. To fill this gap, this work reviews and introduces theoretical methods and applications of mechanics in grinding from the aspects of modeling principles, limitations and possible future trendencies.

Coupled thermo-mechanical analysis and optimization of the grinding process for Inconel 718 superalloy using single grit approach

The current research aims to enhance the grinding efficiency of Inconel 718 by thermo-mechanical assessment of the effects of main grinding parameters. Three optimization criteria were proposed to minimize cutting forces and temperature and maximize removal rate. The overall optimization solution predicted the optimal grinding parameters as: maximum uncut chip thickness of 0.81 µm, cutting speed of 43.5 m/s, grain-edge radius of 0.5 µm, and grain rake angle of − 30°. Combined analysis of those major parameters and correlating them with grinding forces and temperature without doing experiments was a research gap covered by this study. Also, it was found that the incorporation of 0.3 wt% graphene nanoplatelets within a vegetable-based MQL resulted in the decrease of grinding forces and energy.

OPTIMIZATION OF GRINDING PARAMETERS AND PRODUCT PARTICLE SIZE PREDICTION ON GRINDING OF AMMONIUM PERCHLORATE IN AIR CLASSIFIER MILL

OPTIMIZATION OF GRINDING PARAMETERS AND PRODUCT PARTICLE SIZE PREDICTION ON GRINDING OF AMMONIUM PERCHLORATE IN AIR CLASSIFIER MILL

Study on scatter of surface integrity of bearing raceway grinding

Taking the discrete degree of bearing raceway grinding surface integrity as the research object, the orthogonal test based on the wheel speed, workpiece speed and grinding depth is designed. The residual stress, retained austenite, hardness, grinding modified layer thickness and roughness of the raceway after grinding test were measured, and the mean value and standard deviation of these indexes were calculated. The results show that the standard deviation of residual stress and roughness decreases with the increase of wheel speed, and the standard deviation of retained austenite, hardness and grinding modified layer thickness keeps stable. Finally, the optimal grinding parameters considering the scatter of surface integrity are selected.