The present invention generally relates to methods and systems for optimizing complex manufacturing processes, such as grinding processes, to achieve various objectives, such as cost minimization, productivity maximization, and process control.
Grinding is one of the most complex and least understood machining processes, due to a large number of characteristic variables that not only influence the process outcome, but also each other. Although extensive research has been performed to describe grinding processes, the models proposed to date can be only partially defined, are applicable to a limited range of processes, or are not suitable for industrial practice. Therefore, in industry, the prevailing methods for designing grinding processes have relied on prior experience and handbooks, and most often grinding operations are performed under less than optimal conditions due to the difficulties in integrating all the information obtained from various sources to determine desirable grinding conditions.
Thus, more systematic approaches have been sought to minimize cost and/or maximize productivity. In recent years, great efforts have been made to develop intelligent methodologies to address these needs. Rowe et al., Application of Intelligent CNC in Grinding, Comput. Ind., 31, 45-60 (1996), and Rowe et al., Application of Artificial Intelligence in Grinding. Ann. CIRP, 43, 521-531 (1994), provided an extensive review on diverse applications of artificial intelligence (AI) to grinding processes, and categorized them by the underlying techniques: knowledge-based expert systems, fuzzy logic, neural networks, genetic algorithms (GAs), and adaptive control for optimization (ACO).
Knowledge-based expert systems in grinding normally use a knowledge base that is based on human experts and production rules, and they provide a solution through an inference procedure, for example, on desirable grinding conditions for a given grinding situation or on selection of a grinding wheel. These approaches, however, have limitations. The basic premise is that most of knowledge needed must exist in the form of heuristic rules, which are often limited. Different types of knowledge, which are available in the form of mathematical equations and experimental data, cannot be incorporated into the existing knowledge-based expert systems. Furthermore, the number of experts is limited and even decreasing due to system automation and rapid changes in the manufacturing industry, and thus accumulating knowledge and experience will be more difficult in the future. Most knowledge-based expert systems do not offer the flexibility needed for requisite frequent updates to cope with continual introduction of new materials and processes.
Due to its inherent capability of handling uncertainty and flexibility, fuzzy logic has also been applied to grinding optimization problems. Since expert knowledge and production rules can be expressed in the form of if-then rules and then easily converted to fuzzy rules, fuzzy logic-based schemes can maintain the benefits of the simple rule-based systems while being able to manage the possible imprecision or vagueness of obtained knowledge. The prior art has proposed methodologies of incorporating mathematical models and empirical data into a fuzzy logic-based optimization scheme via an automatic rule generation mechanism.
Another approach to the optimization of grinding processes has been artificial neural networks (ANN). ANN-based methods usually focus on modeling the process rather than optimization. Neural networks have a good learning ability from data and have proven their excellent performance for poorly understood problems, such as many grinding applications. However, ANN-based approaches are applicable only where abundant training data are available and its usage has often been limited by the difficulties of finding sufficient reliable training data to cover the entire domain of interest.
In addition to the efforts made on modeling grinding processes, much research has been also carried out to improve the optimization algorithm itself for grinding processes. Such efforts have included the review if various grinding optimization approaches and categorizing them into simple data retrieval methods, empirical model methods, rule-based reasoning, case-based reasoning, ANN-based methods, and hybrid methods. It has also been shown that an optimization scheme could be incorporated into an adaptive control system to provide initial optimal grinding conditions for the adaptive control. In most research, traditional optimization techniques have been applied to grinding processes, but mainly developed for a specific process or application.
In recent years, evolutionary algorithms (EA) and their adaptations have become popular means for the optimization of grinding processes since these methods generally have the capability of finding the global optimum in the presence of several local optima and perform better for ill-defined problems, which are difficult to solve by conventional algorithms. There are mainly three branches of EAs: GAs (genetic algorithms), evolutionary strategies (ES), and evolutionary programming (EP). Among them, GA has been mostly frequently used to optimize grinding processes.
As mentioned above, single representation approaches such as production rules, ANN, analytical models or empirical models have their own limitations in describing the grinding processes. To overcome these shortcomings, a more comprehensive hybrid approach would be required that is capable of describing complex grinding processes. By incorporating different knowledge, Lee et al., Evolutionary Modeling and Optimization of Grinding Processes, Int. J. Prod. Res., 38, 2787-2813 (2000), hereinafter Lee (2000), proposed a Generalized Intelligent Grinding Advisory System (GIGAS), which was a model-based optimization system applicable to a general class of grinding processes. Analytical models formulated in generalized form as well as empirical models were used to describe grinding processes. A Fuzzy Basis Function Network (FBFN) with an autonomous learning algorithm was also employed due to its capability of incorporating experimental data and heuristic knowledge in a unified fashion. The developed ES-based optimization algorithm provided a fast convergence to optimal points and was demonstrated as a good tool for the optimization of ill-defined problems or difficult problems, such as constrained non-linear optimization problems with mixed-discrete variables. Subsequent to Lee (2000), Wang et al., Prediction of Surface Roughness in Cylindrical Traverse Grinding Based on ALS Algorithm, Proceedings of the Fourth International Conference on Machine Learning and Cybernetics, Guangzhou (Aug. 18-21, 2005), similarly reports the use of an algorithm developed for FBFN to model surface roughness in cylindrical grinding processes.
Notwithstanding the advancements provided by Lee (2000) and Wang et al., there is an ongoing need for optimization algorithms or utilities capable of optimizing complex grinding processes to achieve various objectives, such as cost minimization, productivity maximization, and process control.