On Rough Set-Based Modeling and with Application to Process Decision for Forming Plate by Line Heating

In shipbuilding, forming plate by line heating is atypical complex manufacturing process involving many uncertain factors, so it is difficult to establish an accurate mathematical model. How to establish a knowledge model that can reflect technological laws is the key to the development of an intelligent decision system for forming plate by line heating. In this article, rough set (RS) theory is applied to the modeling of the line heating process. By defining variable-inclusion RSs, an algorithm of knowledge reduction is proposed, which enhances the noise immunity and fault tolerance of the model, and improves the efficiency of knowledge acquisition. Through introducing fuzzy logic, a method of modeling the line heating process based on RSs and fuzzy logic is proposed, which effectively extracts the technological rules of plate formation. Finally, rapid decision-making for process parameters is implemented by fuzzy inference technology. 1. Introduction For some complex manufacturing technologies in modern shipbuilding, such as line heating and welding processes, it is difficult to establish an exact mathematical model because of high nonlinearity, multivariable coupling, and uncertainties of the system. With the development of computer technology and artificial intelligence, soft computing methods such as artificial neural networks, genetic algorithms, fuzzy logic, and rough set (RS) theory have been applied successively in ship manufacturing process modeling, which shows good prospects for intelligent technology in shipbuilding. Shin et al. (1999) at Seoul National University used a single-curvature plate model to simulate the formation of saddle-type shells and deduce the technological parameters of line heating by an artificial neural network. They also proposed a comprehensive algorithm for automatically curving plate by line heating, and further developed an application system that can simulate the deformation of double-curved plates (Shin et al. 2004a, 2004b). Liu et al. (2006) applied a hierarchical genetic algorithm to optimize the technological parameters of an automatic line heating process. In the field of ship welding, fuzzy logic technology has been used to establish a fuzzy model of the relationship between welding variables and weld forming-parameters (Su 2009). Feng (2012) set up a knowledge base of a ship-welding process by a RS method and then implemented ship-welding production design through uncertainty reasoning. Based on RS theory, Chen and Lv (2013) developed a data-driven knowledge base for quality control of ship hull welding.