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Optimization of sand casting performance parameters and missing data prediction.

Qingwei Xu1, Kaili Xu1, Li Li1

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Summary
This summary is machine-generated.

This study optimizes sand casting performance using grey relational analysis and predicts venting quality with back propagation (BP) neural networks. Objective entropy weight method removes human bias, enhancing casting process reliability and efficiency.

Keywords:
back propagation neural networkgrey relational analysisoptimizationperformance parameterspredictionsand casting

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Area of Science:

  • Materials Science and Engineering
  • Manufacturing Processes
  • Computational Intelligence

Background:

  • Sand casting is crucial in modern manufacturing due to its diverse applications.
  • Optimizing sand casting performance is essential for improving product quality and process efficiency.
  • Data-driven approaches are increasingly important for process optimization and prediction in manufacturing.

Purpose of the Study:

  • To optimize sand casting performance parameters using grey relational analysis.
  • To predict missing data, specifically venting quality, using a back propagation (BP) neural network.
  • To introduce an objective method for weighting evaluation indicators and determining neural network parameters.

Main Methods:

  • Objective entropy weight method to eliminate human factors and assign indicator weights.
  • Grey relational analysis to optimize sand casting performance parameters.
  • Back propagation (BP) neural network for predicting missing data (venting quality) and determining hidden neuron count via mean square error.
  • Grey system theory for validating BP neural network predictions.

Main Results:

  • Human factor influence was successfully mitigated using the objective entropy weight method.
  • Sand casting performance parameters were optimized, providing a reference for sand milling processes.
  • A novel method for determining hidden neurons in BP networks was proposed, and venting quality was accurately predicted.
  • The validity of the BP neural network for missing data prediction was confirmed using grey system theory.

Conclusions:

  • The integrated approach of grey relational analysis and BP neural networks effectively optimizes sand casting performance and predicts critical quality attributes.
  • The objective entropy weight method enhances the reliability of performance evaluations by removing subjective human bias.
  • The proposed method for determining BP neural network parameters and its application in predicting venting quality demonstrate a significant advancement in data-driven manufacturing optimization.