Patent ID: 9731344
Date: 2017-08-15
CPC Classifications: B22C,G05B

Claim:
1. A computer implemented system for optimization of sand for reducing casting rejections in a foundry, said system comprising: a first repository configured to store therein data pertaining to a set of primary sand parameters, wherein said primary sand parameters is are selected from the group consisting of Green Compression Strength (GCS), compact-ability index, moisture content, active clay, inert fines content, Loss On Ignition percent (LOI), permeability index, wet tensile strength, volatile matter content, grain fineness number (GFN), American Foundry Society grain fineness number (AFS), oolitics content and pH value of the sand; a second repository configured to store therein data pertaining to rejection type and rejection quantity of the castings; a third repository configured to store therein pattern generated using the primary sand parameters corresponding to the casting rejections; an input module cooperating with said first repository, said input module configured to receive at least a value corresponding to at least one primary sand parameter for an instance; a processor module cooperating with said first repository, said second repository, said third repository and said input module, said processor module configured to correlate primary sand parameter values, the primary sand parameters, the casting rejections, and the rejection type, and provide correlation between the primary sand parameter values, the primary sand parameters, the casting rejections, and the rejection type for the instance; a pattern forming engine coupled with the processor module, said pattern forming engine configured to store a set of pattern forming rules, said pattern forming engine configured to apply said pattern forming rules to determine at least a pattern based on the correlations of the primary sand parameter values, primary sand parameters, casting rejections, and rejection type, the pattern generated being at least a complex equation which takes into account each of the correlated data to form a complex multi-dimensional curve; and a predictive-prescriptive module cooperating with said pattern forming engine and said processor module, said predictive-prescriptive module configured to receive input values corresponding to the primary sand parameters fed by a user, said predictive-prescriptive module being configured to map the input values into the complex equations and generate at least a predictive/prescriptive solution including a degree of probability, wherein the predictive/prescriptive solution is utilized to adjust sand primary parameters; and a self-learning editor cooperating with said pattern forming engine and said predictive-prescriptive module, said self-learning editor configured to store each and every multi-dimensional curve generated into a temporary repository, determine predicted casting rejections and a rejection type present in the predictive/prescriptive solution and compare and correct predicted values corresponding to the casting rejections and the rejection type with actual rejections and rejection types inputted by the user.