Optimization of Microstructure Development During Deformation Processing Using Control Theory Principles

A two stage approach based on modem control theory has been proposed to control the microstructure development during hot working. This method was utilized for optimal design of hot extrusion process. In the first stage, equations for dynamic recrystallization of plain carbon steel were utilized to obtain an optimal deformation path such that the grain size of the product would be 26 μm. In the second stage, the geometric mapping was utilized to develop an extrusion die profile such that the strain rate profile during extrusion matches with the optimal trajectory computed in the first stage. An extrusion experiment was performed to validate the proposed methodology, by utilizing the extrusion die geometry obtained in the second stage. The as-extruded grain size was observed to be in close agreement with the optimal design performed in the first stage. The results of the present investigation revealed that the principles of control theory can be reliably applied for the optimization and control of microstructure during deformation processing

Optimization of Microstructure Development During Deformation Processing Using Control Theory Principles

A two stage approach based on modem control theory has been proposed to control the microstructure development during hot working. This method was utilized for optimal design of hot extrusion process. In the first stage, equations for dynamic recrystallization of plain carbon steel were utilized to obtain an optimal deformation path such that the grain size of the product would be 26 μm. In the second stage, the geometric mapping was utilized to develop an extrusion die profile such that the strain rate profile during extrusion matches with the optimal trajectory computed in the first stage. An extrusion experiment was performed to validate the proposed methodology, by utilizing the extrusion die geometry obtained in the second stage. The as-extruded grain size was observed to be in close agreement with the optimal design performed in the first stage. The results of the present investigation revealed that the principles of control theory can be reliably applied for the optimization and control of microstructure during deformation processing

Development of an Intelligent Apprentice System for Extrusion Die Design and Process Simulation

The costly build-and-test methods used by the experts today for optimizing extrusion die design increase lead time before production which, in turn, adversely affects operation of the aerospace factory. The success of such designs is strongly dependent upon having an experience base available. Also, the recent work on the computer aided engineering (CAE) approach to extrusion of difficult-to-extrude materials, carried out by the current investigators, reveals that the build-and-test methods are not feasible for extrusion of new aerospace alloys. To overcome these difficulties a prototype intelligent apprentice system was developed using an approach which synthesizes the available techniques from software engineering (SE), data base management systems (DBMS), operating systems (OS), and artificial intelligence (AI) to exploit the power of existing analytical techniques with the help of heuristic rules. The system offers a potential capability for prompting and aiding the design engineer in his task of finding effective solutions to complex problems. In this paper, the extrusion die design criteria and methodology, the approach and the various steps used in the development of the system, and the results of the validation are discussed. The results clearly indicate that the current engineering approach to using AI is more practical and beneficial for solving immediate problems in automation of die design than a pure AI approach

Development of an Intelligent Apprentice System for Extrusion Die Design and Process Simulation

The costly build-and-test methods used by the experts today for optimizing extrusion die design increase lead time before production which, in turn, adversely affects operation of the aerospace factory. The success of such designs is strongly dependent upon having an experience base available. Also, the recent work on the computer aided engineering (CAE) approach to extrusion of difficult-to-extrude materials, carried out by the current investigators, reveals that the build-and-test methods are not feasible for extrusion of new aerospace alloys. To overcome these difficulties a prototype intelligent apprentice system was developed using an approach which synthesizes the available techniques from software engineering (SE), data base management systems (DBMS), operating systems (OS), and artificial intelligence (AI) to exploit the power of existing analytical techniques with the help of heuristic rules. The system offers a potential capability for prompting and aiding the design engineer in his task of finding effective solutions to complex problems. In this paper, the extrusion die design criteria and methodology, the approach and the various steps used in the development of the system, and the results of the validation are discussed. The results clearly indicate that the current engineering approach to using AI is more practical and beneficial for solving immediate problems in automation of die design than a pure AI approach

Optimization of Microstructure Development: Application to Hot Metal Extrusion

A new process design method for controlling microstructure development during hot metal deformation processes is presented. This approach is based on modern control theory and involves state- space models for describing the material behavior and the mechanics of the process. The challenge of effectively controlling the values and distribution of important microstructural features can now be systematically formulated and solved in terms of an optimal control problem. This method has been applied to the optimization of grain size and certain process parameters such as die geometry profile and ram velocity during extrusion of plain carbon steel. Various case studies have been investigated, and experimental results show good agreement with those predicted in the design stage

Optimization of Microstructure Development During Hot Working Using Control Theory

A new approach for controlling microstructure development during hot working processes is proposed. This approach is based on optimal control theory and involves state-space type models for describing the material behavior and the mechanics of the process. The effect of process control parameters such as strain, strain rate, and temperature on important microstructural features can be systematically formulated and then solved as an optimal control problem. This method has been applied to the optimization of grain size and process parameters such as die geometry and ram velocity during the extrusion of plain carbon steel. Experimental results of this investigation show good agreement with those predicted in the design stage