Study On Variation Source Diagnosis Based On Ayesian Networks In Auto-body Assembly Processes

The dimensional variation of auto bodies is one of the most important factors thataffect the product quality, manufacturing cost and the market shares, so the techniqueson the root cause diagnosis and process control are paid great attention to by bothresearchers and manufacturers. The auto body is assembled with hundreds ofcompliant sheet metal parts in dozens of assembly stations. In the assembly process,different variation sources such as incoming parts, tooling, welding etc, will affect theassembly variation. The relationship between variation sources and the assemblyvariation are extremely complicated. Besides, the measurement data about the processare always small samples in some limited stations, which only provide incompleteinformation for the process monitoring and diagnosis. The traditional fault diagnosismethods, such as pattern matching method, are different to diagnose the root cause ofthe assembly variations by considering the incomplete measurement data. In realassembly process, variation sources diagnosis is still mainly relying on theengineering experience.The Bayesian network theory is one of the most effective methods for knowledgerepresentation and uncertain reasoning. It can describe the complex relationshipbetween root causes and assembly deviation with an uncertain probability model. Onthe other hand, multi-source information, such as engineering experience, designknowledge and also the measurement data can be incorporated into the model to avoidthe shortcomings of small sample data. Compared with the traditional statisticmethods, Bayesian networks have a significant advantage for assembly variationdiagnosis under incomplete measurement data set.In this paper, Bayesian networks are used to solve the fault diagnosis problem inthe auto body assembly process. The Bayesian network model is used to illustrate thecausal relationships between the root cause nodes and the sensor nodes. A newmodeling method under small sample data set by considering multi-sourceinformation is proposed. Based on the diagnostic model, the methods on probabilityreasoning, diagnosability of the root nodes and optimal sensor placement aredeveloped. At last, the assembly diagnostic cases are studied. The results andcomparative analysis showed the proposed methods are effective and reliable.The main contents are shown as follows:(1) Description of the dimensional variation source diagnosis problem in theassembly process with Bayesian networks For the diagnosis problem in the assembly process, the Bayesian network theoryis introduced to describe the causal relationship of the assembly deviations. At first,the root nodes and sensors nodes of the diagnostic network are acquired by extractingthe key characteristics in the assembly process. Afterwards, the directed acyclic graphand conditional probability tables are learned based on the learning algorithms underlarge data sets. Furthermore, the probability reasoning methods based on thediagnostic Bayesian network and the variation sources diagnosis procedures arepresented. At last, the further studies concerning the small sample problem in the realmeasurement process are proposed.(2) The Bayesian network modeling of the causal relationship of theassembly variation by considering multi-source informationFor the Bayesian network modeling problem under the small sample data set, anew method based on multi-source information fusion is proposed. At first, theoriginal Bayesian network structure and conditional probabilities are acquired basedon the mapping of the variation simulation results which are obtained in the variationdesign stage. Furthermore, the diagnostic model is updated by incorporated with thenew measurement data set. The causal relationships are updated based on theconditional independence test method, and the corresponding parameters of thenetwork nodes are updated based on Bayesian estimation approach. At last, on thebasis on constructing the diagnostic information matrix, the effective independencemethod is used for optimal sensor placement in the auto body assembly process.(3) Multiple fault diagnosis in the auto body assembly process based onBayesian networksMultiple fault diagnosis in the multi-station assembly process is always different.Based on the above Bayesian network model, methods on the process monitoring forevidence variables, diagnostic reasoning and diagnostic capability analysis are studied.At first, under small sample data condition, a parameter estimation method based onBayesian approach is presented. The estimation results are used as evidence states ofthe sensor nodes for probability reasoning. Afterwards, the diagnostic methods andprocedures based on the Bayesian network are proposed by updating the posteriorprobability of the root nodes. At last, by classifying the diagnosis results, thesensitivity analysis by considering the number of evidence variables and measurementnoises is performed to evaluate the diagnostic performance of Bayesian networks.(4) Application to the assembly variation source diagnosisBased on the above theoretical research, the diagnostic method based on Bayesiannetworks is applied in the real assembly cases. The corresponding programmingmodule for assembly process monitoring is developed. The side frame and front areaassembly cases are studied for fixture fault diagnosis and optimal sensor placement. The results showed the Bayesian network method for multiple fault diagnosis in theauto body process is practicable and effective.This paper presented a framework of Bayesian network model and fault diagnosisin the auto body assembly process, which provides a practicable variation sourcesdiagnosis method under small sample data sets. The proposed methods in this papercan not only be used in dimensional quality improvement of auto bodies, but also canbe applied to aircraft, train bodies and other mechanical products for assembly qualityimprovement.