Predictive model to aid selection of heat treating process parameters for alloy steel forgings

Heat treatment is a very complex process involving lot of process parameters. It is important to select optimum process parameters to achieve desired mechanical properties. Predictive models are very useful tools to aid decision making while selecting heat treatment process parameters. This research deals with developing a predictive model to aid heat-treating process parameter selection for UNS G41300 grade of steel using regression analysis. The study gives a better understanding of factors affecting hardness of heat-treated forgings. The model developed has been proved to be satisfactory within the standard operating procedures, to serve as an aid in selection of the critical parameter, temper temperature at Gulf Coast Machine and Supply Company (Gulfco), Beaumont, Texas. Suggestions have also been made to improve the monitoring and control of heat treatment process. Gulfco will substantially benefit from this research work by being able to reduce the rework with better selection of process parameters, monitoring and control of heat treatment.