Optimizing The Markov Model To Predict The Configuration Design Process Of Complex Products

Complex products are typical Engineer To Order products,and their compositional structures are complex and the types of modules are numerous,and the management and maintenance functions of the modules for the enterprises are still lacking.When receiving customer orders,the company's technical personnel must not only quickly design a product configuration structure,but also ensure that the product functions meet the needs of customers.This will inevitably lead to higher costs and other problems for the company.Therefore,in the current situation of severe market competition,if enterprises that produce complex products want to gain market advantage,they must first improve their product configuration design capabilities,and then provide market customers with high-quality products at a low price.This paper points out that based on the main model and the property characteristic table,the product main structure configuration model is established,and the constraint relationship between the product modules is expressed based on the onto-logy and transaction property table.The implementation of this technology not only provides a better main structure model for the product configuration and design stage,further enhances the design efficiency,but also reduces the unfavorable factors such as duplicate design and wins the strategic goals for the enterprise.On the basis of the complete configuration model,when the enterprise receives the customer's order,it firstly uses the QFD technology to convert the functional requirements of the customer's verbal description into technical parameters,which provides a theoretical basis for selecting the module during the configuration design phase.However,due to the complexity of complex products,there may be more than one satisfying condition when selecting a component or module with a certain function.At this time,the configuration solution is required.This paper optimizes the Markov forecast model based on the quadratic exponential smoothing coefficient method.To select the optimal module,add it to the product main structure model according to the product configuration knowledge rules.In the end,the verification of the actual data of a steam turbine plant proves the feasibility of the proposed method and obtains more accurate results.