Multiparameter Optimization and Controlling for Cylindrical Grinding Process

This paper bursts the bondage of conventional no-burn thought, presents an optimum strategy permitting burn appear in grinding roughing stage, but the burning layer can be summed on the following finishing stage. On the base of the basic grinding models, the objective function and constrained functions for the multiparameter optimum grinding models had been built in this paper. By the computer simulation, the nonlinear optimum grinding control parameters had been obtained, and the truth grinding process had been controlled by these parameters. The results of simulation and the experiments proved the exactitude of the optimum models and the feasibility of the optimum strategy. This paper had also created the precondition for the grinding automation, virtual grinding and intelligent grinding system for cylindrical grinding process.

Effectuate Application of Optimum Methods in Grinding Process

Random error of grinding process is central factor th at give an effect on grinding quality all through. Optimum methods are usually a pplied on grinding process for higher productivity and preferable grinding quali ty. But the grinding quality can’t be reliably controlled now and then while opt imal solution of grinding processing parameters have been applied in production, because of two involved aspects which are availability of established empirical formulas and reliability of setting up optimum mathematical model. That is to s ay, there is particular application of optimum methods in grinding process. This paper discussed that how to confirm conditions of grinding test which be po int to grinding peculiarity when test design and regression analysis are used to setting up some empirical formulas. In order to reduce effect of random errors on precision of the empirical formulas and enable them to be applied widely, a m ethod that a lot of random error can be sufficiently contained in grinding test was suggested. And then, A means to ameliorate restriction formulas of grinding quality is expounded based on marked level of the empirical formulas and improve d reliability of optimum mathematical model is given, which offer an effectual w ay for solving grinding quality out of control as a result of random error and w orkable optimal solution of grinding processing parameters can be applied in the production really. Finally, an example is presented.

Multi-Objective Optimization for an Industrial Grinding and Classification Process Based on PBM and RSM

The grinding and classification process is one of the key sub-processes in mineral processing, which influences the final process indexes significantly and determines energy and ball consumption of the whole plant. Therefore, optimal control of the process has been very important in practice. In order to stabilize the grinding index and improve grinding capacity in the process,a process model based on population balance model(PBM) is calibrated in this study. The correlation between the mill power and the operating variables in the grinding process is modelled by using the response surface method(RSM), which solves the problem where the traditional power modeling method relies on some unobservable mechanism-related parameters. On this basis, a multi-objective optimization model is established to maximize the useful power of the grinding circuit to improve the throughput of the grinding operation and improve the fraction of –0.074 mm particles in the hydrocyclone overflow to smooth the subsequent flotation operation. The elite non-dominated sorting genetic algorithm-II(NSGA-II) is then employed to solve the multi-objective optimization problem. Finally, subjective and objective weighting methods and integrated multi-attribute decision-making methods are used to select the optimal solution on the Pareto optimal solution set. The results demonstrate that the throughput of the mill and the fraction of –0.074 mm particles in the overflow of the cyclone are increased by 3.83 t/h and 2.53%, respectively.

Intelligent optimal control system for ball mill grinding process

Operation aim of ball mill grinding process is to control grinding particle size and circulation load to ball mill into their objective limits respectively, while guaranteeing producing safely and stably. The grinding process is essentially a multi-input multi-output system （MIMO） with large inertia, strong coupling and uncertainty characteristics. Furthermore, being unable to monitor the particle size online in most of concentrator plants, it is difficult to realize the optimal control by adopting traditional control methods based on mathematical models. In this paper, an intelligent optimal control method with two-layer hierarchical construction is presented. Based on fuzzy and rule-based reasoning （RBR） algorithms, the intelligent optimal setting layer generates the loops setpoints of the basic control layer, and the latter can track their setpoints with decentralized PID algorithms. With the distributed control system （DCS） platform, the proposed control method has been built and implemented in a concentration plant in Gansu province, China. The industrial application indicates the validation and effectiveness of the proposed method.

Numerical simulation of particle motion at cucumber straw grinding process based on EDEM

Simulation of straw grinding process based on discrete element method(DEM)was proposed.According to the force analysis and kinematics analysis,the differential equation of straw particle motion on hammers was deduced,and the formation mechanism of the material circulation layer was obtained.Geometric model of grinder,particle model and contact model were established by EDEM software.The influence of hammer number,hammer thickness and gap of the hammer-sieve on particle grinding number and power consumption were obtained by single factor simulation test.The grinding process is divided into three stages.The hammer smashing plays a dominant role in 0-0.25 s.While the hammer smashing particle number increases slowly and then decreases to the lowest level in 0.25-0.60 s,the tooth plate smashing particle number increases rapidly and dominates,and then forming a material circulation layer.The hammer and tooth plate smashing particle number is basically stable in 0.60-2.00 s,and the tooth plate smashing occupies the dominant position.With the increase of the number and thickness of hammers,the power consumption of crusher tends to increase,and with the increase of the gap between hammers and sieves,the power consumption of crusher decreases first and then increases.The results can provide guidance for the development of high-efficiency and energy-saving grinding equipment for cucumber straw.

Subsurface damage pattern and formation mechanism of monocrystalline β-Ga_(2)O_(3) in grinding process

Monocrystalline beta-phase gallium oxide (β-Ga_(2)O_(3)) is a promising ultrawide bandgap semiconductor material. However, the deformation mechanism in ultraprecision machining has not yet been revealed. The aim of this study is to investigate the damage pattern and formation mechanism of monocrystalline β-Ga_(2)O_(3)in different grinding processes. Transmission electron microscopy was used to observe the subsurface damage in rough, fine, and ultrafine grinding processes. Nanocrystals and stacking faults existed in all three processes, dislocations and twins were observed in the rough and fine grinding processes, cracks were also observed in the rough grinding process, and amorphous phase were only present in the ultrafine grinding process. The subsurface damage thickness of the samples decreased with the reduction in the grit radius and the grit depth of cut. Subsurface damage models for grinding process were established on the basis of the grinding principle, revealing the mechanism of the mechanical effect of grits on the damage pattern. The formation of nanocrystals and amorphous phase was related to the grinding conditions and material characteristics. It is important to investigate the ultraprecision grinding process of monocrystalline β-Ga_(2)O_(3). The results in this work are supposed to provide guidance for the damage control of monocrystalline β-Ga_(2)O_(3)grinding process.

Dynamic modeling and analysis of the closed-circuit grinding-classification process

Mathematical models of the grinding process are the basis of analysis, simulation and control. Most existent models in- cluding theoretical models and identification models are, however, inconvenient for direct analysis. In addition, many models pay much attention to the local details in the closed-circuit grinding process while overlooking the systematic behavior of the process as a whole. From the systematic perspective, the dynamic behavior of the whole closed-circuit grinding-classification process is consid- ered and a first-order transfer function model describing the dynamic relation between the raw material and the product is established. The model proves that the time constant of the closed-circuit process is lager than that of the open-circuit process and reveals how physical parameters affect the process dynamic behavior. These are very helpful to understand, design and control the closed-circuit grinding-classification process.

Clinical Data-Driven Finite Element Analysis of the Kinetics of Chewing Cycles in Order to Optimize Occlusal Reconstructions Dedicated to Professor Karl Stark Pister for his 95th birthday

The occlusal design plays a decisive role in the fabrication of dental restorations.Dentists and dental technicians depend on mechanical simulations of mandibular movement that are as accurate as possible,in particular,to produce interference-free yet chewing-efficient dental restorations.For this,kinetic data must be available,i.e.,movements and deformations under the influence of forces and stresses.In the present study,so-called functional data were collected from healthy volunteers to provide consistent information for proper kinetics.For the latter purpose,biting and chewing forces,electrical muscle activity and jaw movements were registered synchronously,and individual magnetic resonance tomograms(MRI)were prepared.The acquired data were then added to a large complex finite element model of the complete masticatory system using the functional information obtained and individual anatomical geometries so that the kinetics of the chewing process and teeth grinding could be realistically simulated.This allows developing algorithms that optimize computer-aided manufacturing of dental prostheses close to occlusion.In this way,a failure-free function of the dental prosthesis can be guaranteed and its damage during usage can be reduced or prevented even including endosseous implants.