This paper describes an interactive neural network model that predicts the quality of cast ceramic products using multiple quantitative and qualitative inputs. This has been done to enable a major sanitary ware manufacturer to reduce product waste by better control of the slip casting process. The input variables describe the raw materials, ambient conditions and line settings for the ceramic casting process. The neural network predictive model assigns one of seven quality categories to the cast based on the input data. This prediction is used by the quality control engineer to make a priori adjustments to materials and line settings so that a good quality cast is produced without trial and error. The neural model can also be used to determine optimum settings of each adjustable input variable in the light of values of non-adjustable input variables.
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Martinez, S.E., Smith, A.E. & Bidanda, B. Reducing waste in casting with a predictive neural model. J Intell Manuf 5, 277–286 (1994). https://doi.org/10.1007/BF00123699
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DOI: https://doi.org/10.1007/BF00123699