Anomaly Detection for the Prediction of Ultimate Tensile Strength in Iron Casting Production

Mechanical properties are the attributes that measure the faculty of a metal to withstand several loads and tensions. In particular, ultimate tensile strength is the force a material can resist until it breaks. This property is one of the variables to control in the foundry process. The only way to examine this feature is to apply destructive inspections that make the casting invalid with the subsequent cost increment. Modelling the foundry process as an expert knowledge cloud allows machine-learning algorithms to forecast the value of a certain variable; in this case, the probability of a certain value of ultimate tensile strength for a foundry casting. Nevertheless, this approach needs to label every instance in the training dataset for generating the model that can foresee the value of ultimate tensile strength. In this paper, we present a new approach for detecting castings with an invalid ultimate tensile strength value based on anomaly detection methods. This approach represents correct castings as feature vectors of information extracted from the foundry process. A casting is then classified as correct or not correct by measuring its deviation to the representation of normality (correct castings). We show that this method is able to reduce the cost and the time of the tests currently used in foundries.