Reducing waste in casting with a predictive neural model

Abstract

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.

A Survey of Smart Manufacturing for High-Mix Low-Volume Production in Defense and Aerospace Industries

“…The transformation in consumer demand has resulted in low volume higher volume manufacturing practices which have brought about the need for revaluation of the manufacturing and supply chain system as a whole. Companies are renovating existing equipment with the capability to produce complex products in optimum amounts of time and reduce unnecessary waste [22,23]. Value stream mapping is developed to improve different system components of the processes by detailed categorization of the system and finding areas for improvement [24].…”

A comparative predictive analysis of neural networks (NNs), nonlinear regression and classification and regression tree (CART) models

“…Feng and Wang (2002) compared nonlinear regression and neural network models in computer-aided reverse engineering and automatic inspection applications. Coit, Jackson, and Smith (1998); Martinez, Smith and Bidanda (1994); Moon and Na (1997); Petri, Billo, and Bidanda (1998);Smith (1993) and Yarlagadda (2000), among others, used NNs in manufacturing processes and operations modeling. Yang and Lee (2000) applied NNs for data processing in reverse engineering.…”

Neural Networks in Materials Science.

“…Tribology issues in machining, Including the use of neural networks, have been reviewed by Jahanmir.72) Neural networks are also used routinely in the control of cast ceramic products made using the slip casting technique, using variables such as the ambient conditions, raw materlal information andproduction line settings. 73) In another application, scanning electron microscope images of ceramic powders were digitised and processed to obtain the particle boundary profile; this informatlon was then classified using a neural approach, with exceptionally good results even on unseen data. 74) 14.…”