W. Brian Rowe

Analysis and simulation of the grinding process. Part I: Generation of the grinding wheel surface

This paper is in three parts describing the analysis and simulation of the grinding process. This first part is concerned with the generation of the wheel surface by single point diamond dressing. In grinding, the grinding wheel has to be dressed periodically to restore wheel form and cutting efficiency. Understanding the process of generating the grinding wheel surface is important for the control of the grinding process. Generation of the wheel surface is simulated as a single diamond dressing process on a computer generated wheel. The wheel is simulated by grains randomly spaced in the wheel volume. The topography of the wheel cutting surface is generated by simulating the action of an ideal dressing tool as it dresses the wheel. The simulation of the wheel topography takes account of the motion of the dressing tool, grain size, grain spacing, grain fracture and grain break-out. The simulated cutting surface is used for further simulations of grinding. The simulation of grinding using the simulated grinding wheel surface is described in Sections 2 and 3 where a comparison is made of results predicted from simulation with results obtained from experiments. By matching simulated and experimental results, it is possible to explain the relative importance of dressing and grinding parameters.

Thermal analysis of high efficiency deep grinding

Regimes of deep grinding range from creep grinding conducted at low workspeeds to High Efficiency Deep Grinding (HEDG) at fast workspeeds. At intermediate depths of cut, grinding is likely to be impossible due to high temperatures and damage to the workpiece and wheel. Analytical techniques for the determination of temperatures in deep grinding processes are discussed. An explanation is proposed for why it is possible to work efficiently at these two extremes of removal rate without experiencing the severe problems experienced in the intermediate range. Methods are required for determining the transition conditions so that process engineers can select process conditions for efficient material removal and high quality of manufactured products using high efficiency deep grinding. This paper provides a method for order of magnitude estimation of temperatures. It is proposed that the angle of inclination of the contact plane is an important parameter for the achievement of high workspeeds. It is argued that workpiece melting provides an ultimate boundary for energy dissipation within the workpiece.

Analysis and simulation of the grinding process. Part II: Mechanics of grinding

This paper is the second of three parts which describe the analysis and simulation of the grinding process. Generation of the workpiece surface depends on the interactions between the grains of the wheel and the workpiece. The grinding wheel surface generated by dressing was simulated by the method described in Part I. This part describes a method to investigate the process of grinding by simulating the cutting action of every grain which engages with the workpiece. Grinding forces are analysed by simulating the force on each grain which passes a section of the workpiece. The simulated workpiece surface shows features which are similar in nature to the experimental results. A more extensive comparison of simulated and experimental grinding behaviour is presented in Part III.

Applications of Artificial Intelligence in Grinding

The application of AI technologies using modern computers and controllers is seen as a way forward to produce higher quality components more efficiently with smaller batch sizes and more frequent changeovers. Users continue to demand better accuracy, surface integrity, and shorter cycle times with reduced operator intervention and increased flexibility. This paper reviews research into the use of AI methods to harness the knowledge and skills required to plan, set-up, operate and control grinding processes. Basic AI concepts are introduced and discussed particularly in the context of application to grinding. Two main trends are evidenced in the development of AI technologies in grinding: desktop systems to assist tool and parameter selection and self-optimising systems integrated within the machine controller. It is predicted mat future developments will favour increasing communication between these two levels of control within a CIM environment The development of modular systems which are sufficiently robust to plan, supervise and control abrasive processes requires ongoing research and development.

Temperatures in deep grinding of finite workpieces

This paper investigates the diverse thermal effects generated in high efficiency deep grinding (HEDG). Using a new thermal model of circular arc contact with transient analysis, the transient behaviour of the maximum contact temperature has been analysed for various grinding conditions. It is found that steady state conditions can be achieved for the conditions of sufficient workpiece length and high workspeeds. The effect is important for the understanding of the deep grinding process and for the prediction of satisfactory grinding conditions. HEDG conditions also have very apparent effects on the depth of heat penetration to the workpiece. The parameters investigated include mean contact angle, Peclet number and the heat source distribution. Experimental results are presented for specific energy, energy partition and mean temperature for high efficiency deep grinding.

Analysis and simulation of the grinding process. Part IV: Effects of wheel wear

A method of simulating dressing and grinding was described in Parts I and II of this paper. In Part IV, the effects of wheel wear and wheel characteristics on grinding performance are simulated and compared with experimental results. The results show that grinding performance is strongly affected by dressing conditions immediately after dressing. As grinding continues, the grinding power, and also the surface roughness, tends to converge towards similar values for all dressing conditions when the same grinding conditions are employed. Results from the simulation show that the influence of wheel wear is affected by the wheel fracture characteristics. The convergence of the grinding behaviour shown in the simulation and experiments suggests that stable grinding performance in a wheel redress life cycle may be achieved by selecting dressing conditions, taking account of the grinding behaviour.

Analysis and simulation of the grinding process. Part III: Comparison with experiment

A method of simulating dressing and grinding was described in Parts I and II of this three-part series. In Part III, the effects on grinding performance of varying the dressing conditions are simulated and compared with experimental results. The results show that a coarse dressing condition leads to low grinding force and grinding power but a high workpiece surface roughness. The grinding performance of the wheel in the dwell period for “spark-out” is simulated. Simulated and experimental results both show that grinding power in the dwell period decreases following an exponential decay function, however the reduction of surface roughness does not follow an exponential decay.

A Grinding Power Model for Selection of Dressing and Grinding Conditions

The grinding power is often used as a parameter for monitoring the grinding process. The power may also be used to monitor the effects of dressing. Empirical models are required to guide the selection of the dressing and grinding conditions. In this paper, the effects of dressing conditions and grinding conditions on grinding force and grinding power are reviewed. The effects of grinding conditions and dressing conditions on grinding force and grinding power are related to the shape of the idealized chip thickness. It is found that the grinding force and grinding power can be related to the dressing operation by considering the effective density of the cutting edges on the wheel surface. The semi-empirical model developed in this paper can be used to predict the variation of the grinding power during the wheel redress life cycle. Therefore the model can be used to guide the selection of dressing and grinding conditions. The potential use of the model for adaptive control of the grinding process is also described.

Application of intelligent CNC in grinding

Abstract The application of AI technologies using modern computers and controllers is seen as a way forward to produce higher quality components more efficiently with smaller batch sizes and more frequent changeovers. Users continue to demand better accuracy, surface integrity, and shorter cycle times with reduced operator intervention and increased flexibility. This paper reviews research into the use of intelligent control and optimisation techniques in grinding and propose the incorporation of intelligent techniques into computer numerical controls (CNCs). Two main trends are evidenced in the development of AI technologies in grinding: desktop systems to assist tool and parameter selection and self-optimising systems integrated within the machine controller. It is predicted that future developments will favour increasing incorporation of intelligence into CNC. The development of modular systems which are sufficiently robust to plan, supervise and control abrasive processes requires ongoing research and development.

An Intelligent Multiagent Approach for Selection of Grinding Conditions

The advantages of a multi-agent approach are presented for the selection of grinding conditions. The agents consist of case based reasoning, neural network reasoning and rule based reasoning. Case based reasoning is employed as the main problem-solving agent to select combinations of the grinding wheel and values of control parameters. Rule based reasoning is employed where relevant data are not available in the case base. A neural network is employed to select a grinding wheel if required. The operator makes the final decision about the wheel or the values of control parameters. The multi-agent approach combines the strengths of the different agents employed, to generate hybrid solutions and overcomes the limitations of any single approach. A blackboard method was used as the means of integrating the multi-agent system. The system works as expected and demonstrates the potential of using artificial intelligence for selection of grinding conditions, as well as the capability to develop a powerful database by learning from experience.