Optimization Of Grinding Process Research Articles

Grinding process optimization considering carbon emissions, cost and time based on an improved dung beetle algorithm

During the machining phase, carbon emissions produced by grinding machines account for a significant proportion of the total emissions. Optimizing grinding process parameters is an effective energy-saving measure, which can notably reduce carbon emissions. However, most of the research on parameter optimization related to carbon emissions and energy saving is focused on turning and milling processes, with limited studies on the grinding process. To address this gap, this paper introduces an optimization method for grinding process parameters that considers carbon emissions and seeks to balance emissions, time, and cost in the grinding process. Initially, we quantify the relationship between grinding parameters and optimization objectives and a corresponding multi-objective optimization model is established subsequently. Then an improved multi-objective dung beetle optimization algorithm (INSDBO) is proposed to solve this model. As a case study, we conduct experiments on the machining of a plunger. Simulation results indicate that after optimization, carbon emissions, grinding costs and time have decreased by 11.7%,7.7%, and 6.7% respectively, validating the effectiveness of the proposed optimization method. When compared with the Adaptive Weighted Evolutionary Algorithm (AdaW)、the traditional dung beetle algorithm (NSDBO), and Multi-Stage Multi-Objective Evolutionary Algorithm (MSEA), the improved dung beetle optimization algorithm(INSDBO) showed superior performance. This refined algorithm can suggest optimal parameters in the grinding process, thereby reducing carbon emissions, machining time, and costs.

Optimization of adaptive and sustainable gold ore grinding processes for better environmental and land conditions in the small-scale gold mining sector in Indonesia

The artisanal and small-scale gold mining (ASGM) sector largely relies on mercury in gold processing, posing potential environmental contamination, health issues, and land degradation. In the villages of Tatelu and Talawaan, ASGM operations, guided by local knowledge and resources, have transitioned to using cyanide leaching for gold processing sustainably. These operations utilize andesitic stones from river deposits as grinding media in the grinding process. However, the cyanide leaching results were not optimal, with a gold recovery below 60%. This leaves significant amounts of gold in the waste, necessitating further processing and the incomplete treatment of free cyanide waste. The suboptimal gold recovery in cyanide leaching is attributed to the inadequate grain size liberation during grinding. This study optimized grinding by comparing andesitic stone grinding media with steel balls and rods. The findings indicate that to achieve a grain size of 75% passing 74 um, grinding with andesitic stones takes 4 hours, while steel rods and balls take 3 hours. For a grain size of 75% passing 44 um, grinding with andesitic stones, steel balls, and rods requires 6 hours. With more precise process parameters, locally available andesitic stones can be an effective grinding medium to optimize gold recovery. In line with optimizing gold recovery, this will enhance ASGM's revenue, encouraging the adoption of waste management practices to alleviate environmental impact, health risks, and land degradation. This aligns with the promotion of sustainable practices within the ASGM sector.

Process Optimization of Robotic Grinding to Guarantee Material Removal Accuracy and Surface Quality Simultaneously

Abstract Simultaneously guaranteeing material removal accuracy and surface quality of robotic grinding is crucial. However, existing studies of robotic grinding process optimization have mainly focused on a single indicator that solely considers contour error or surface roughness, while studies that simultaneously investigate the impact of contact force, spindle speed, feed rate, inclination angle, and path space on the material removal profile (MRP) and the surface roughness are lacking. This paper proposes a hybrid optimization method that considers dimensional accuracy and surface quality constraints. First, an MRP model that considers the coupling influence of the contact force, spindle speed, feed rate, and inclination angle is presented. Then, a surface roughness model that considers the inclination angle is established. Finally, the contact force, feed rate, inclination angle, and path space are simultaneously optimized to satisfy the hybrid constraints of MRP accuracy and surface roughness. The proposed method ensures maximum grinding efficiency while satisfying dimensional accuracy and surface quality constraints. The proposed method is verified on an industrial robotics grinding system with a pneumatic force-controlled actuator. The results show that the proposed method has higher profile accuracy and lower surface roughness than traditional methods.

Selected Aspects of Precision Grinding Processes Optimization.

The paper describes selected aspects of the optimization of grinding processes, taking into account the characteristic probabilistic features of this process. Characteristic features of the grinding process that influence the significant dispersion of the quantities used in the optimization process to define goals and limitations are indicated. Attention was paid to the reasons for uncertainty in the use of research results, imperfections in information extraction procedures and the limited amount of data in the use of simulation and regression models in optimization procedures. The issue of determining the durability of abrasive tools in grinding process optimization procedures was analyzed. Methodologies for defining tool life are specified, taking into account the dispersion of the values of controlled process parameters. The effects of interference were taken into account in the relationships describing grinding efficiency and costs. The benefits of optimization taking into account the probabilistic nature of the process were determined.

Optimization of grinding process for hard and brittle materials based on damage evolution mechanism

Grinding damage of hard and brittle materials has a direct impact on part strength and subsequent polishing efficiency. Damage suppression is one of the key research issues in hard and brittle material processing. Due to the uncertain damage evolution law, there is still a lack of optimization methods for the multi-step grinding process under given damage constraints. In this paper, the extended finite element method is used to establish a model between the prefabricated crack depth and final crack depth, which reveals the influence of various existing subsurface damages on crack propagation. It is found that only when the prefabricated crack depth is close to the indentation crack depth on the perfect surface, the final crack depth will be affected to some extent. To optimize the grinding process to achieve low damage, a semi-empirical model for predicting grinding damage is proposed which is suitable for process optimization and does not rely on some difficult-to-obtain parameters. Finally, based on the damage evolution law and prediction model, the core content of this paper is proposed, that is, a multi-step grinding process optimization method with variable grinding depth. This method can minimize the damage while ensuring the same processing efficiency, the damage depth is reduced by 25 %. The effectiveness of the optimization method and the correctness of the damage evolution law are proved by experiments. This method is suitable for engineering applications and has practical significance for understanding the damage evolution mechanism, which is helpful for the suppression of grinding damage and the optimization of the grinding process.

Experimental modelling and multi-objective optimisation of electrochemical discharge peripheral surface grinding process during machining of alumina epoxy nanocomposites

Experimental modelling and multi-objective optimisation of electrochemical discharge peripheral surface grinding process during machining of alumina epoxy nanocomposites

Effect of cutting set variations on structural and functional properties of hamburgers

AbstractMeat grinders are composed of a combination of individual functional elements (e.g., screw conveyor, perforated plates, knives). This setup, and in particular the chosen cutting set, influences the characteristics of ground meat and hamburgers produced. In this study, we took a closer look at the effect of cutting set variations and process parameters on structural, functional, and physicochemical properties of beef hamburgers produced. It was found that the specific mechanical energy input during grinding increased when cutting levels, i.e., a set of one hole plate and one knife, were increased, causing more cell disintegration (r = 0.387, p = 0.02). Surprisingly though, an influence on the functional and quality parameters of the hamburgers could not be found for most parameters tested. The findings indicate that variations in the cutting set affect the process parameters and the stress applied to the meat, but residence times in this zone are too small to cause noticeable effects on the analytical and qualitative properties of hamburgers. As such, there are options for energy and cost optimization of industrial grinding processes without sacrificing quality.

Assessment and Optimization of Grinding Process on Zirconia Ceramic Using Response Surface Method

Assessment and Optimization of Grinding Process on Zirconia Ceramic Using Response Surface Method

Correction to: Geometrical optimization of centerless grinding process by profiled workrest

Correction to: Geometrical optimization of centerless grinding process by profiled workrest

Optimization and Design of Grinding Process by Computer Simulation

Grinding is a unit operation to reduce particle size by milling, and recently the demands for such operations have been increasing more and more. Computer simulation is effective in optimizing the grinding process and designing equipment, and the number of studies is increasing. To predict grinding results quickly with a low calculation load, it is useful to output the impact energy obtained from ball-only calculations as an indicator of grinding. We introduce how simulation can be used in grinding process analysis, including examples of reproducing ball behavior, predicting grinding results such as particle size distribution, and analyzing mechanochemical effects.

Experimental modeling and multiobjective optimization of electrochemical discharge peripheral surface grinding process during machining of alumina epoxy nanocomposites

Experimental modeling and multiobjective optimization of electrochemical discharge peripheral surface grinding process during machining of alumina epoxy nanocomposites

Grinding simulation and process optimization method of tungsten carbide alloy

Grinding simulation and process optimization method of tungsten carbide alloy

Optimization of Grinding Process of Sunflower Meal for Obtaining Protein-Enriched Fractions

In this study, dry fractionation process was proposed in order to obtain protein-enriched sunflower meal fractions. The process includes two-stage grinding using a hammer mill and a roll mill, and fractionation of sunflower meal by sieving. Central composite design (CCD) with four variables on three levels within response surface methodology was applied in order to estimate the influence of grinding parameters (sieve openings diameter of the hammer mill: 2, 4, and 6 mm, roll gap: 0.15, 0.2, and 0.25 mm, feed rate: 0.1, 0.175, and 0.25 kg/cm min, and roll speed: 400, 500, and 600 rpm) on responses (protein content, fraction yield and grinding energy consumption). Sieve openings diameter expressed the highest impact on fraction yield while roll gap expressed the most dominant influence on protein content in the fraction and grinding energy consumption. The highest protein content obtained was 48.06%(dm) with fraction yield of 77.22%. A multi-response optimization procedure was performed and optimal values were: sieve openings diameter of 2 mm, roll gap of 0.25 mm, feed rate of 0.2 kg/cm min, and roll speed of 400 rpm, while predicted values for a desired range of responses were: protein content 45.5%(dm), fraction yield 77.89%, and grinding energy consumption 8.31 Wh/kg.

High-speed grinding fracture mechanism of Cf/SiC composite considering interfacial strength and anisotropy

Cf/SiC composite is connected through the bonding interface to achieve the toughening effect, which makes the fiber direction and interface strength in the material directly affect the composite properties and grinding quality. Aiming at the high-quality processing of Cf/SiC composite, a single abrasive particle model is built to reveal the variation law of grinding force in the process and the surface damage phenomena such as interface debonding, fiber fracture and matrix fracture. Based on the constitutive relationship among matrix, reinforcement and interface phase, the constitutive model of composite was established. However, Cf/SiC composite is a typical hard brittle material. Low grinding speed cannot yield to good machining quality. Thus, to improve the output quality, high speed grinding is applied in this paper. Meanwhile, by considering the heterogeneous structure characteristics of composite materials, the two-dimensional simulation of grinding of single abrasive particle grinding, based on FEM (Finite Element Model) method, is realized. Finally, composite fracture behavior is analyzed by observing microstructure in the grinding process. The effects of fiber orientation, surface strength, grinding speed, and grinding depth on the surface properties and the quality of Cf/SiC composite are studied by grinding force and surface analysis. The mechanism of high-speed grinding of ceramic matrix composites, as well as the practical approach and theoretical underpinnings of grinding process optimization, are examined.

Characterization and formation mechanisms of rail chips from facing grinding by abrasive wheel

Rail grinding is a widely-used method to eliminate surface damages on railway tracks. The grinding chips are closely related to the grinding parameters and the grinding quality. Therefore, the investigation on the characteristics and the formation mechanism of chips from rail grinding is beneficial for the optimization of rail grinding process. In the present study, the grinding chips from the real rail grinding in the field and from the rail grinding experiments in the laboratory were analyzed in terms of their morphologies, microstructural evolution and chemical characteristics. The results showed that flowing, knife, slice and spherical chips were generated during rail grinding. The formation mechanism of flowing, knife and slice chips was plastic deformation by forces from abrasive grains with oxidation in air by grinding heat. Primary and secondary shear zones and central zones were generated. In the primary shear zone, cracks were generated in knife and slice chips. In the secondary shear zone, obvious plastic deformation was formed in knife and slice chips and a significant white etching layer (WEL) was generated in knife chips. The formation mechanism of spherical chips was melting of small powdery grinding debris by the high temperature and the subsequent solidification, accompanied with severe oxidization in air. The oxides produced in chips consisted of FeO, Fe3O4, Fe2O3 and AlFe2O4. The grinding quality could be predicted through observing the grinding debris. The larger percentage of flowing chips represented the better grinding quality (smaller surface roughness, slighter surface burn and thinner WEL on ground rail), which could be the goal for the grinding parameters choice.

Multicriteria compromise optimization of feed grain grinding process

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Assessing load in ball mill using instrumented grinding media

Monitoring mill load is vital for the optimization and control of grinding process. This study proposed the use of an instrumented grinding media to assess solid loading inside a ball mill, with size and density of the instrumented ball comparable to that of the ordinary grinding media. The acceleration signal was captured by an embedded triaxial accelerometer. The signal was first detrended by a complete ensemble empirical mode decomposition and then reconstructed using a correlation coefficient method. The filling ratio, particle to ball ratio, time domain features and sample entropy are features extracted from the signal, providing input to a support vector machine (SVM) learning model. Grinding experiments with different loads were conducted. The typical loading level was classified according to grinding efficiency index and associated power consumption. Different methods were adopted to determine the optimal values of parameters in the SVM model, including particle swarm optimizer (PSO), genetic algorithm (GA), and grid search (GS). The results showed that the accuracy of particle swarm optimizer can reach 96.67%. This study demonstrates the potential of using instrumented grinding media for real-time characterization of mill feed and operation monitoring.

Geometrical optimization of centerless grinding process by profiled workrest

This work presents a method to optimize centerless grinding geometrical configuration. It is based on the regulation of the horizontal axes of operating and regulating wheels of the machine, coupled with an opportune blade profile, allowing a continuous selection of workrest angle and workpiece height, without requiring blade substitution and/or manual interventions. The regulation of workrest angle and height cannot be done independently: their relationship is defined during blade profile design. In machines with two independent axes for regulating wheel and blade horizontal displacement, this regulation can be performed in process or in the setup phase. This results in optimal processing parameter choice leading to improved quality and shorter processing time as well as reduced setup time.

Molecular dynamics simulations of scratching characteristics in vibration-assisted nano-scratch of single-crystal silicon

Vibration-assisted grinding improves machining quality and efficiency over conventional grinding, whereas its atomistic mechanism remains unclear. In this study, we investigated vibration-assisted machining using molecular dynamics simulations of the nano-scratching process by considering single-crystal silicon as the paradigm material. Vibration dynamically redistributes and rebalances the existing anisotropy among the applied forces, thereby leading to unique scratch characteristics including homogeneous deformation. Vibration reduces the tangential and normal force components and effectively suppresses the anisotropic stress state, resulting in a reduction of the amorphous-layer thickness and enlargement of the scratched surface area. The magnitudes of the tangential and normal components vary cyclically with a frequency that is twice that of the applied vibration. Furthermore, when the frequency increases, the tangential and normal components and amorphous-layer thickness decrease gradually, opposite to the scratched surface area. In addition, as the vibration amplitude increases, the tangential and normal components decrease, in contrast with the behaviour of the amorphous layer, which thins gradually and then slightly increased to a constant thickness. Vibration-assisted scratch effectively turns the brittle material at the working spot into a ductile material. Thus, our atomistic insights suggest a new route for optimization of vibration-assisted grinding processes.

Combining Taguchi method and DEAR method for multi-objective optimization of grinding process

In this paper, a study on multi-objective optimization of the cylindrical grinding process is presented. The experimental material used in this study is X12M steel. The two output parameters of the grinding process considered in this study are surface roughness and material removal rate (MRR). The cutting mode parameters including cutting speed, feed rate, and cutting depth have been selected as input parameters of the experimental process. Experimental matrix by Taguchi method has been used to design a matrix with 27 experiments. Analysis of experimental results by Pareto chart has determined the effect of input parameters on output parameters. The Data Envelopment Analysis-based Ranking (DEAR) method has been applied to determine the values of input parameters to simultaneously ensure the two criteria of minimum surface roughness and maximum MRR. Finally, the development direction for further studies has also been recommended in this study.