Improved DNN Network Method for SMT Production Lines Quality Prediction
With the continuous improvement of function diversification,size refinement and device com-plexity of airborne electrical assembly modules,new challenges have been posed to the quality of electrical assembly of SMT production lines.At present,the manufacture process of SMT production lines generally has problems such as weak correlation of product quality inspection data,lagging product quality analysis and weak predictability.However,the traditional statistical analysis method cannot effectively extract knowledge and rules from massive disordered data,this paper proposes a deep learning-based electronic assembly quality prediction method.To begin with,the quality evaluation method is constructed to deter-mine the influencing factors of electric assembly quality.Then,use Principal Components Analysis(PCA)to preprocess the quality data and eliminate the non-relevant features.Furthermore,the DNN network is introduced to construct the quality prediction model,and BFO-PSO optimization algorithm is used to search the optimal hidden layer number and node number of DNN network.Finally,based on the actual manufacturing data of the aviation SMT production lines,the module quality prediction model is simulated and tested to verify the effectiveness and scientificity of the proposed method.
SMT production lineselectronic assembly quality predictionBFO-PSO optimization algo-rithmDNN networkintelligent manufacturing
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万方数据
