Model Of Grinding Process Research Articles

Optimization of Typical Grinding Model and the Simulation of Grinding Process Numerical

Based on the actual conditions during industrial production, this paper proposes an improvement method and solution to the typical grinding process model and derives a mathematical model of grinding process that’s suitable for industrial production. Sampling analysis and laboratory experiments of the production process were conducted, the derived model was tested, and the results show that particle size distribution curve obtained from the improved model is similar to the actual product, which means that the improved model can be applied to simulate industrial grinding process.

The Study of Wear Flat Effect on the Single Grain Micro-Cutting Performance in Superabrasive Grinding from a Simulation Perspective

Grain-workpiece interface, which resembles a micro-cutting process, directly modifies the workpiece surface and dominates all the output measures of a grinding process. The abrasive grains always become worn or dulled during grinding, which alters the grain-workpiece interface output and turns to be the primary factor that causes the transient or time dependent behavior in monolayer superabrasive grinding. Therefore, the study on how the grain wear influences the grain-workpiece interaction through micro-cutting analysis becomes necessary. As the emergence of the packaged FEM software for micro-cutting simulation, apart from single grit cutting test, it enables another qualitative and quantitative investigation method on grain-workpiece interface mechanism in an efficiency and effective manner. Based on previous efficacy verification of Third Wave AdvantEdgeTM, the FEM simulation is carried out to investigate the effect of grain wear on its micro-cutting performance. The simulation results provide an illustrative manner to interpret the phenomenon and mechanism, and the results can be used in the grinding process modeling in the future as well.

Design of an adaptive controller in constant pressure grinding of natural diamond cutters

Natural diamond cutters are critical cutting tools applied in nano science and technology fields. The properties of diamond crystal, which are advantages for making high precision cutters, also bring difficulties to the cutter grinding process. In this paper, an adaptive control method is described based on principles of predictive functional control (PFC) method. With this method, an adaptive controller was designed in order to implement constant pressure grinding of natural diamond cutters. Firstly, a real-time grinding process model was constructed, taking account of time-variant characteristic of the wheel-cutter contact and the low stiffness of flexible stage in the practical grinding system. Then, parameters of PFC controller was calculated according to this model. Controller performances were simulated under three conditions of non-disturbance, model parameter mismatch, and time-variant disturbance. Simulation results show that performance of the designed controller is as good as desired, and the controller has enough capability to fit practical diamond cutter grinding process which has characteristics of non-linear and time variance.

Framework of grinding process modeling and simulation based on microscopic interaction analysis

This paper describes a framework of grinding process modeling to understand the grinding fundamentals and design grinding processes with predictive performance. The model regards grinding process as a time dependent process and an integration of microscopic interactions in the wheel-workpiece contact zone, including cutting, plowing, and sliding as well as other frictional interactions. The grinding process control and design are in fact to manage and balance all these interactions. The principles of microscopic interactions are analyzed and used to correlate the grinding process input parameters and performance output.

Research on Microscopic Grain-Workpiece Interaction in Grinding through Micro-Cutting Simulation, Part 1: Mechanism Study

Grinding is regarded as a special multiple edge cutting process, which can be decomposed into grain-workpiece interface, chip-workpiece/bond interface, and bond-workpiece interface at microscopic level. The grain-workpiece interface, which resembles a micro-cutting process, directly modifies the workpiece surface and dominates all the output measures of a grinding process. Therefore, the study of the grain-workpiece interaction through micro-cutting analysis becomes necessary. As the emergence of the packaged FEM software for micro-cutting simulation, apart from single grit cutting test, it enables another qualitative and quantitative investigation method on grain-workpiece interface mechanism in an efficiency and effective manner. In this paper, the efficacy of the commercialized cutting simulation software Third Wave AdvantEdgeTM is evaluated, and the possibility of using AdvantEdgeTM for in-depth understanding of grain-workpiece interface as well as grinding process modeling is studied, too.

A case-based and practical approach for multivariate modeling of grinding process

Grinding process is claimed to be the only possible cost-effective means of shaping engineering part(s) or component(s) into finished products with required surface topology. However, because of the complicated relationship(s) between several phenomena and entities involved in a grinding process, determining functional relationship between relevant input(s), in-process variable(s), and output quality characteristic [or response] remains a critical and difficult task for researchers and practitioners. The complexity of the problem further increases as number of responses considered for modeling is more than one, and the situation is so-called multivariate or multiresponse surface modeling. Although various mechanistic and empirical modeling approaches have been proposed in literature, they are found to be of limited validity, with applied functional approximation and simplistic assumption(s). In addition, empirical models are sometimes based on off-line designed experimental data, which are expensive or uneconomical in many line layout type mass-manufacturing situations. This paper attempts to provide a case-based practical approach for practitioners, with detailed steps involved in multivariate modeling, based on conventional regression technique, and unconventional artificial neural network-based technique, using real-time production data. The two case examples selected to verify the approaches are from an automobile engine manufacturing unit located in eastern India.

Numerical Analysis for Thermal Deformation of Workpiece in Cylindrical Plunge Grinding

During the last decades, heat generation in grinding is one of the top concerns because high temperature under fabrication leads to less dimensional accuracy of a workpiece. Several studies with regard to grinding heat have been carried out, focused on micro phenomena of abrasive grains or macro phenomena of thermal deformation in grinding machines. However, these researches have been extensive, schematized information such as thermal deformation, and grinding temperature is indispensable for practical applications. In this study, we combined the simulation model of the plunge grinding process and the numerical analysis method with the differencing technique for the non-steady heat conduction problem, and have constructed the simulation technique for analyzing the heat problem in the workpiece. The simulation results provided information of the heat conduction, and the thermal deformation of the workpiece.

Evolutionary grinding model for nanometric control of surface roughness for aspheric optical surfaces

A new evolutionary grinding process model has been developed for nanometric control of material removal from an aspheric surface of Zerodur substrate. The model incorporates novel control features such as i) a growing database; ii) an evolving, multi-variable regression equation; and iii) an adaptive correction factor for target surface roughness (Ra) for the next machine run. This process model demonstrated a unique evolutionary controllability of machining performance resulting in the final grinding accuracy (i.e. averaged difference between target and measured surface roughness) of -0.2+/-2.3(sigma) nm Ra over seven trial machine runs for the target surface roughness ranging from 115 nm to 64 nm Ra.

A process model of wafer thinning by diamond grinding

Abstract This paper is to develop and investigate a wafer thinning process model (WTPM) to integrate the wafer thickness into set-up parameters and predict total thickness variation (TTV) of ground wafers with modification of the wafer grinding process model (WGPM) developed previously. Due to the variation of wafer thickness during diamond grinding, the WTPM can be used to estimate the TTV after wafer thinning process. Experiments have been performed on a G&N nano grinder MPS-940 to demonstrate its feasibility of silicon wafer thinning. Results have been obtained that the TTV is less than 3 μm for two sets of tests of wafer thickness of 600–700 μm and 700–800 μm as compared with the desired TTV of 0 μm from simulation of the developed WTPM with certain set-up of tilting angles. Therefore, the WTPM has been verified in this study and further used for forecasting the limitation of wafer thinning with different wafer thickness.

Generalized practical models of cylindrical plunge grinding processes

This paper presents generalized grinding process models developed for cylindrical grinding processes based on the systematic analysis and experiments. The generalized model forms are established to maintain the same model structures with a minimal number of parameters so that the model coefficients can be determined through a small number of experiments when applied to different grinding workpiece materials and wheels. The relationships for power, surface roughness, G-ratio and surface burning are established for various steel alloys and alumina grinding wheels. It is shown that the established models provide good predictive capabilities while maintaining simple structures.

Simulation based optimization of the NC-shape grinding process with toroid grinding wheels

For achieving high material removal rates while grinding free formed surfaces, shape grinding with toroid grinding wheels is favored. The material removal is carried out line by line. The contact area between grinding wheel and workpiece is therefore complex and varying. Without detailed knowledge about the contact area, which is influenced by many factors, the shape grinding process can only be performed sub-optimally. To improve this flexible production process and in order to ensure a suitable process strategy a simulation-tool is being developed. The simulation comprises a geometric-kinematic process simulation and a finite elements simulation. This paper presents basic parts of the investigation, modelling and simulation of the NC-shape grinding process with toroid grinding wheels.

A systematic solution methodology for inferential multivariate modelling of industrial grinding process

Abstract The need for precision components and parts in manufacturing industries has bought an increase in the need for finishing operations that can satisfy this demand. In addition, there is a continuous demand for hard and tough materials that can withstand varying stress conditions to ensure prolonged service life of components and parts. The need to process these materials economically so as to meet stringent product quality requirements (generally expressed as composite of a family of properties, so-called multiple response characteristics) has become a real challenge for researchers and practitioners in manufacturing industries. Grinding has the potential to meet these critical needs for accurate and economic means of finishing parts, and generate the required surface topography. Despite this importance and popularity, grinding still remains one of the most difficult and least-understood processes due to lack of adequate inferential mechanistic and analytical multivariate models, for varied industrial situations. In this context, data-driven inferential linear or nonlinear multiple statistical regression, and artificial neural network modelling have become increasingly popular techniques for complex industrial grinding processes. Unfortunately, these techniques are either proposed and implemented in isolation or presented as a comparative evaluation grinding case study. A systematic solution methodology for inferential multivariate modelling, which addresses the different phases, starting from preliminary linear random x -case multivariate regression model, hypothesis testing of influence of addition of higher-order nonlinear terms to the adequate linear model (or presence of nonlinearity), and subsequent selection of a suitable nonlinear artificial neural network-based multivariate model, is lacking. In view of the above-mentioned conditional requirements, this paper attempts to provide a systematic methodology to develop a multivariate linear regression model, hypothesis testing for the influence of nonlinear terms to linear model, and accordingly selection of a suitable artificial neural network-based inferential model with improved prediction accuracy and control of grinding behaviour. The methodology suggests the use of various statistical techniques, such as Q–Q (quantile–quantile) plotting, data transformation, data standardization, outlier detection test, model adequacy test, model cross-validation and generalization. The suitability of the recommended methodology is illustrated with the help of an engine cylinder liner grinding (honing) case example, in a leading automotive manufacturing unit in India.

Grinding forces in regular surface texture generation

Certain grinding operations need a specially shaped wheel for regular surface texture (RST) generation. In such cases, wheel nominal active surface is reproduced on the ground surface in a special way. The simple version of the method consists in grinding with the wheel having helical grooves which are deeper than the grinding depth. Pattern regularity depends in longer time on wheel wear. The grinding force is thus one of the most important process indicators. A simulation model of grinding process, assuming random arrangement of abrasive grains was developed and is presented in this paper. The model was verified by grinding force measurements. These measurements showed specific features that were different from those characteristic of conventional grinding. Explanations of the untypical effects observed at force–time series signals for the three basic types of surface patterns are provided.

Particle size distributions a new approach

The consideration that the mass discontinuities of brittle materials statistically present fractal behavior makes available a new approach to the elucidation of crushing and grinding process. Through crushing and grinding experiments of characteristics brittle materials, the breakage distribution function (particle size distribution of products of a single breakage event) is defined as the sum of two distinct distribution (fragments and dust). The breakage distribution function follows a geometric dimensional scale and is depending on two new statistical characteristics constants of the material. Entering the number of breakage events, as the independent variable of the aforementioned function, a theoretical model of grinding process is formulated. The application of the theoretical model in any given size reduction process allows the definition of the process breakage probability distributions function and thus the theoretical model can be transformed to an actual simulation model of the process. The reliability of the new approach in describing the evolution of particle size distribution during the crushing or grinding was evaluated by its application in characteristics experimental and industrial installations.

Research on Genetic Optimization of Special Spiral Rod Grinding Process

In the grinding process of special spiral rods, the actual feed rate is far below the allowable limit, resulting in great efficiency waste. To tackle the problem, this paper presents a controlling strategy which significantly improves rough grinding efficiency through changing grinding feed rate, enhances work-piece surface quality and shortens the processing time. Based on the establishment of the work cycle model of the special spiral rod grinding process, the strategy sets the grinding efficiency of each individual rod section as the objective function, thus via genetic algorithm realizes an optimal calculation on grinding feed rate variable and resolves the power and roundness constraint problem. Through computer simulation, an optimal grinding control plan is obtained which can help the grinding process to achieve overall optimization.

Selected problems in evaluating topography of coated abrasives

The paper presents some problems connected with the topography of coated abrasives. Various classes of coating are discussed, ranging from conventional single-layer ones to modern engineered coatings of the TRIZACT type. Actual parameters of the active face for various makes are given. Principal in-service properties of abrasive belts are discussed: instantaneous grinding efficiency and variation in surface roughness of the machined workpiece. A grinding process model is outlined which, after completion, will be capable of controlling precisely automated grinding operations using coated abrasives.

Advances in Modeling and Simulation of Grinding Processes

In the last decade the relevance of modeling and simulation of grinding processes has significantly risen which is caused by industrial needs and is indicated by the number of publications and research activities in this area. This keynote paper results from a collaborative work within the STC G and gives an overview of the current state of the art in modeling and simulation of grinding processes: Physical process models (analytical and numerical models) and empirical process models (regression analysis, artificial neural net models) as well as heuristic process models (rule based models) are taken into account, and outlined with respect to their achievements in this paper. The models are characterized by the process parameters such as grinding force, grinding temperature, etc. as well as work results including surface topography and surface integrity. Furthermore, the capabilities and the limitations of the presented model types and simulation approaches will be exemplified.

Discrete model for analysis and design of grinding mill-classifier systems

Discrete mathematical and computer models are developed for the simulation of grinding mill-classifier systems on the basis of distributed parameter models of continuous grinding processes. The discrete model is derived from the partial integro-differential equation describing axial mixing and breakage of particles in grinding mills. The convective flow and axial dispersion in the mill are modeled as particle size-dependent processes, while the recirculation line is described as plug flow of the classifier rejected material involving also some time delay. The resulted set of recursive linear algebraic equations was the basis of computer model developed in MATLAB environment. The capabilities of the computer model are demonstrated by presenting and analyzing results obtained by numerical experiences. The influence of both the kinetic and process parameters of the mill-classifier system on the size distribution of the ground material as well the dynamic behavior of the system are presented and analyzed. The computer model is suitable for designing the configuration and operational conditions of both open- and closed-circuit grinding mill-classifier systems efficiently.

Design of grinding factors based on response surface methodology

The presented paper describes a systematic methodology for empirical modelling and optimization of the plunge centreless grinding process. The assessment of microgeometric quality defining quantity is supported by post-process surface roughness measurements. The design of grinding factors is based on response surface methodology, which integrates a design of experiment, regression modelling technique for fitting a model to experimental data and basic optimization. Central composite response surface design has been employed to develop a second-order surface roughness model. The model has been fully constructed by determination of its structure and regression coefficients. The final goal of experimental study focuses on determination of optimum centreless grinding system set-up and operating conditions for minimization of surface roughness. The computer-aided single-objective optimization, solved by non-linear programming and genetic algorithm, is applied. The results of two different optimization approaches for determination of optimal operating conditions are compared. Finally, further research directions are presented.

FBF-NN-based modelling of cylindrical plunge grinding process using a GA

In order to make a machining process cost effective as well as to assure the desired objective(s), it is important to find the optimal machining parameter(s) in considering all related output variable(s). However developing many models (each one corresponding to a single output variable) leads to more time consuming as well as difficult to interact them. Moreover it has been observed that some of the output variables are inter-related with each other. In this paper, an intelligent approach based on fuzzy basis function neural network (FBF-NN) is proposed to model the cylindrical plunge grinding process. Two approaches are adopted here for automatic design of rule base (each rule is characterized by the antecedent and consequent parts) of FBF-NN using a genetic algorithm. In the first approach the rule-consequent parts are determined individually and in the second approach, those are obtained based on the inter-relationship exist among the output variables. The results of the FBF-NNs and that obtained using the empirical expressions are compared with the real experimental values, which shows that the FBF-NN-based models give better predictions than mathematical models.