Research On The Quality Design Method Of Key Components Of Complex Equipment Based On Grey QFD

Complex equipment is a high-precision product of the equipment manufacturing industry.The research and development design of complex equipment represents the frontier level of the modern high-tech industry.The quality level of production,assembly,use,and maintenance of the components directly affect the final function and performance of the complex equipment.It is important to study the quality research and development design of key components of complex equipment.However,due to the high customization,the complex development,and even the inability of products to match customer requirements,enterprises lose market competitiveness.As a customeroriented design method,quality function deployment(QFD)provides a systematic quality design research and analysis framework for key components.The QFD method can integrate customer requirements into product design to improve customer satisfaction.Therefore,it is important to research quality design on key components of complex equipment.This paper focuses on the customer requirements and engineering characteristics in the design of key parts of the complex equipment and proposes a grey QFD method to meet the customer requirements and the high-quality requirements based on uncertain conditions.The main research works are as follows:(1)Aiming at customer requirements of key components of the complex equipment,this paper integrates interval grey numbers(IGN)and best worst method(BWM)into the QFD framework to address the problem of weight distribution under uncertain conditions.Firstly,the interval grey number theory is adopted to quantify the data of the customer requirements and retain the fuzzy uncertainty information in the expression process.Secondly,the grey BWM is adopted to construct the weight distribution model to compare and analyze the customer requirements to obtain the grey reference comparison vector.Based on the nonlinear constraint optimization model,the relative weight of customer requirements is obtained.Finally,a competitive analysis model is constructed to correct the relative weight to obtain the final weight of the customer requirements.(2)With the problem of weight distribution of engineering characteristics under uncertain conditions,this paper integrates IGN and grey relational analysis(GRA)into the QFD framework.Firstly,the interval grey number theory is adopted to measure the relationship between customer requirements and engineering characteristics and establish the relationship matrix.Secondly,the IGN-GRA method is used to construct the weight distribution model of engineering characteristics to determine the grey correlation degree of engineering characteristics.Finally,based on the autocorrelation relationship between engineering characteristics,a correlations matrix is established to correct the grey correlation degree and obtain the final weight of engineering characteristics.(3)Aiming at the priority ranking problem of engineering characteristics under uncertain conditions,the interval probability ranking model is introduced to order the engineering characteristics.Firstly,the weight comparison model is established based on the interval probability comparison theory.Secondly,the vague weight of engineering characteristics is compared and analyzed to obtain the comparison probability.Finally,the priority is determined according to the comparison probability to identify critical engineering characteristics.The feasibility of the grey QFD method was verified by a turbine engine example.The effectiveness of this method was verified by the comparative analysis of different QFD methods.Based on the expert weight in grey BWM,the parameter scenario study was carried out to clarify the influence of expert weight fluctuation on the priority to verify the robustness of the turbine engine ranking results.