Research On The Critical Technology Of The Intelligent Manufacturing System In Spraying Workshop

With the trend of economic globalization,plenty of overseas furniture products enter into China market,which leads to more fierce competition in the furniture manufacturing industry.In the meantime,there're more and more mass customization orders to satisfy diversified needs of consumers.And it's difficult for furniture enterprises to adapt to the complicated market circumstances with traditional production management mode.On the other hand,since the proposal of Industry 4.0 and "Made in China 2025" plan,intelligent manufacturing technologies have been constantly upgrading,and gradually become effective measures to improve production management of manufacturing enterprises.Exactly under the circumstance,as a manufacturing company and the national high-tech enterprise,Company A decides to cooperate with our research group to develop intelligent manufacturing technolies.Based on the cooperation program of intelligent manufacturing participated by our research group and Company A,this thesis chooses its spraying workshop as research objective,and mainly focuses on critical technologies research of intelligent manufacturing system for spraying workshop.The main contents of research are as follows:The first chapter introduces the challenges that Company A is faced with,and explains research background and meaning of the research.Then,it analyzes the present state of relative researches abroad and domestic.Based on that,the overall structure of this thesis is givenThe second chapter analyzes the spraying manufacturing processes and present state of spraying workshop production manament in detail.Then,with intelligent manufacturing system reference structure that our research group comes up with,it puts forward critical technology proposals to establish spraying workshop intelligent manufacturing system and improve discovered problems,.The third chapter analyzes the data flow during whole spraying manufacturing process,and designs data acquisition scheme and command control sheme.Then,it designs concrete data tables and mapping their relationships to acquire system conceptual data models.Based on these conceptual data models,it realizes the intelligent manufacturing system database.The fourth chapter,with spraying process principle and stepwise regression analysis,filters out factors that mainly influence spraying workpiece quality and ranks their importance.Then,these factors are considered as input to establish BP neural network prediction model.Through the design and adjustment of model critical parameters,the prediction model is able to predict spraying workpiece detective rate accurately and identifies the quality risk effectively.The fifth chapter summarizes the research and proposes the research work to be done.