Innovative Design Of Electric Forklift On BP Neural Network

With the transformation of the economic system and the upgrading of public consumption demand,user needs are gradually diversified,and user perceptual images play an increasingly important role in equipment product design.In the past,people’s understanding of the design of electric forklift trucks was limited to structural design,single appearance design,or purely perceptual engineering research.Functional structure and modeling research were usually conducted on a separate track basis,resulting in a series of issues such as mismatching product design and user perceived imagery,unable to achieve the real effect required by users,and lack of practicality.Solutions based on the above issues,this article takes the first generation electric forklift truck of the practical company(Guangdong Ouneng New Energy Equipment Co.,Ltd.)as the research object,based on existing design methods,combined with coordinated and optimized design of structure and shape,proposes a design strategy and completes the design scheme of the second generation electric forklift truck.Firstly,based on perceptual engineering theory,build a sample database of electric forklift trucks and a user image space.Obtain user perceptual image evaluation values through a questionnaire,conduct factor analysis and cluster analysis on the initial data of the questionnaire,and ultimately select representative image words and typical samples.At the same time,morphological analysis is used to complete the modeling decomposition of electric forklift trucks from multiple dimensions such as vehicle body,counterweight,and roof frame.Secondly,a BP neural network model for modeling and user perceptual images of electric forklift trucks is constructed.This thesis discusses the basic structure and learning algorithm of BP neural network based on the main method of BP neural network,and studies the correlation between user perceptual images and the modeling design of electric forklift trucks.The BP neural network is trained,tested,and calculated in three steps to finally calculate the optimal design scheme for electric forklifts,which is used to guide the design process of electric forklifts in enterprises.Then,a collaborative optimization scheme for modeling and structure is proposed.The design scheme calculated based on BP neural network and the first generation of electric forklift trucks in the enterprise,this thesis explores the impact of changes in engineering equipment design on consumers from a structural perspective.In order to solve the problem of poor compatibility between modeling design and functional structure,finite element analysis of design elements(top guard)was conducted in ANSYS software.Compared to the first generation of electric forklift truck,the quality of the top guard was reduced by 13.4%,and the results showed that the design scheme achieved joint optimization of modeling and structure.Finally,design verification and evaluation.In order to demonstrate the practicality of the design scheme proposed in this article,the design scheme is put into the market for user research,and the BP neural network constructed in this article is used for calculation.The results show that the user satisfaction is very close to the calculated value of the BP neural network.