Review of Intelligent Grinding and Dressing Operations

Abstract With the increased use of advanced materials, grinding processes become a popular means in the processing of parts, particularly in fabricating products from brittle materials such as ceramics and composites. At present grinding appears to be the only practical and economical means of shaping the parts into the final products with fine surface finish, acceptable surface integrity, and high geometric accuracy (Kovach, J. (1987 Kovach, J. 1987. Improved grinding of ceramic components. Proceedings of the Department of Defence, Machine Tool/Manufacturing Development Conference. 1987. Vol. 7, pp.72–100. AFWAL-TR-4137 [Google Scholar]). Improved grinding of ceramic components. In: Proceedings of the Department of Defence, Machine Tool/Manufacturing Development Conference. AFWAL-TR-4137, Vol. 7, 72–100). In the machining of conventional materials, grinding is also usually used as a finishing operation when high accuracy and surface finish is required, and usually determines the major portion of the processing cost. Despite this importance and popularity, grinding still remains as one of the most difficult to control processes. Unlike machining processes where cutting is performed by a defined single/multiple cutting edge(s), grinding is performed by a number of abrasive grits, which are randomly oriented within a grinding wheel. Therefore, it is impossible to maintain or control the shape of these hard grits, which are active in the cutting process (Malkin, S. (1989 Malkin, S. 1989. Grinding Technology, Theory and Applications of Machining with Abrasives Chister, , England: Ellis Horwood Limited. [Google Scholar]). Grinding Technology, Theory and Applications of Machining with Abrasives, Chister, England: Ellis Horwood Limited; Okafor, A. C., Marcus, M., Tipirneni, R. (1990 Okafor, A. C., Marcus, M. and Tipirneni, R. 1990. Multiple sensor integration via neural networks for estimating surface roughness and bore tolerance in circular end milling. Transaction of NAMRI/SME, : 128–136. [CSA] [Google Scholar]). Multiple sensor integration via neural networks for estimating surface roughness and bore tolerance in circular end milling. Transaction of NAMRI/SME 128–136). Grinding is a very complicated process, consisting of complex interactions between a large numbers of variables. These variables can be grouped into four major categories: (a) machine tool, (b) workpiece material, (c) grinding wheel, and (d) operating parameters (Allor, R. L. Whalen, T. J., Baer, J. R., Kumar, K. V. (1993 Allor, R. L., Whalen, T. J., Baer, J. R. and Kumar, K. V. Machining of silicon nitride: experimental determination of process/property relationships. Proceedings of the International Conference on Machining of Advanced Materials. Washington, DC. pp.223–234. [Google Scholar]). Machining of silicon nitride: experimental determination of process/property relationships. In: Proceedings of the International Conference on Machining of Advanced Materials. Washington, DC, 223–234; Lezanski, P., Rafalowicz, J., Jedrzejewski, J. (1993 Lezanski, P., Rafalowicz, J. and Jedrzejewski, J. 1993. An intelligent monitoring system for cylindrical grinding. Annals of the CIRP, 42(1): 393–396. [Google Scholar]). An intelligent monitoring system for cylindrical grinding. Annals of the CIRP 42(1):393–396; Li, K., Liao, T. W. (1997 Li, K. and Liao, T. W. 1997. Modelling of ceramic grinding processes, number of cutting points and grinding forces per grit. Journal of Materials Processing Technology, 65(1–3): 1–10. [CROSSREF][CSA] [Google Scholar]). Modelling of ceramic grinding processes, number of cutting points and grinding forces per grit. Journal of Materials Processing Technology 65(1–3):1–10). The dressing and truing processes used to prepare the wheel have an important effect on the grinding wheel variables and consequently on the output of the grinding process. To achieve this understanding, many researchers focused on the modeling of grinding processes. Some of these models are completely theoretical, and others are practical with theoretical explanations and empirical equations. The aim of this research was to find the relationship between the process variables, the process parameters, and workpiece quality. This review presents the theoretical and experimental models used, process design algorithms, expert systems, artificial intelligent algorithms, fuzzy logic and neural network algorithms, process monitoring and control, and in particular artificial intelligence control systems.