Double-layer-order product quality prediction method based on residual error correction
In traditional production management, the hysteresis quality of quality prediction may cause a large number of unqualified products. Therefore, the invention provides a double-layer-order product quality prediction method based on residual error correction, and the method comprises the steps: firstl...
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| creator | XU XINSHENG CHEN XINHANG HUANG SIYUAN CAO LI OH SONG TAEK |
| description | In traditional production management, the hysteresis quality of quality prediction may cause a large number of unqualified products. Therefore, the invention provides a double-layer-order product quality prediction method based on residual error correction, and the method comprises the steps: firstly predicting the processing parameters through a random forest algorithm, and guaranteeing the parameter integrity; secondly, analyzing parameters by using a regression model constructed by combining a genetic algorithm and a fully connected neural network (NSGA-FCNN), and predicting quality features and residual errors; in order to solve the problem of low prediction precision, residual error correction is carried out by adopting residual error analysis to train an NSGA-FCNN model. Finally, a product quality prediction result and a residual error correction result are combined to form a double-layer-order product quality prediction method. The quality prediction value obtained through the method is compared with t |
| format | Patent |
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Therefore, the invention provides a double-layer-order product quality prediction method based on residual error correction, and the method comprises the steps: firstly predicting the processing parameters through a random forest algorithm, and guaranteeing the parameter integrity; secondly, analyzing parameters by using a regression model constructed by combining a genetic algorithm and a fully connected neural network (NSGA-FCNN), and predicting quality features and residual errors; in order to solve the problem of low prediction precision, residual error correction is carried out by adopting residual error analysis to train an NSGA-FCNN model. Finally, a product quality prediction result and a residual error correction result are combined to form a double-layer-order product quality prediction method. 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| subjects | CALCULATING COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS COMPUTING COUNTING DATA PROCESSING SYSTEMS OR METHODS, SPECIALLY ADAPTED FORADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORYOR FORECASTING PURPOSES ELECTRIC DIGITAL DATA PROCESSING PHYSICS SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE,COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTINGPURPOSES, NOT OTHERWISE PROVIDED FOR |
| title | Double-layer-order product quality prediction method based on residual error correction |
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