Improving the Heat Treating Process (October 2005)
Heat treating is alive and well, a $20 billion-a-year industry that continues to grow and prosper despite a changing manufacturing base and increased global competition. In the short term, there are many resources available to help the heat treater do his job. You might be asking yourself as I have about who is looking out for our future? Let’s learn more.
Academic industry partnerships
One of the groups that looks ahead and is working to find ways to improve the heat treating process is the Center for Heat Treating Excellence (CHTE), established at Worcester Polytechnic Institute in 1999 (Worcester,Mass.; CHTE Web site). The center is an alliance between heat treaters and university researchers to collaborate to identify and address short and long-term needs of the heat treating industry. The center’s goal is to advance heat treating technology by applying fundamental research to solve real-world problems. A unique aspect of this and similar alliances is that the research projects are industry-member driven, so the work directly benefits heat-treating industry profitability, know-how and public image.
CHTE maintains a dialogue with various technical societies, government agencies and other research facilities to ensure that the needs of the heat treating community are well understood, and it continually communicates its research results to the industry as a whole through public forums and via the Internet.
To help the heat treating industry maintain its position as a cost-competitive technology, the center pursues research and develops innovative processes related to:
• Energy-efficient equipment
• Processing with minimal part distortion
• Diffusion related process optimization
• Environmental friendly by-products/emissions
• Adaptability/flexibility for advanced materials
• Process control and intelligent sensor development
• Outcome prediction based on heat treating modeling and simulation
• Equipment/process integration into manufacturing
Examples of active research projects:
Programs underway at CHTE cover a broad spectrum of heat treating interests as illustrated in the following examples:
Making the heat treating process more efficient.
The goal of this project is modeling and simulation of heat treating processes in batch and continuous furnaces. By looking at loading patterns of parts and thermal cycling of furnaces, this project helps to optimize a given heat treating process by creating a heat treating planning system. The software combines heat-transfer data (convection, radiation and conduction), models the type of furnace being used and inputs materials information into a mini-package, which can be used on the shop floor in daily production (Fig. 1).
Today, these systems have been validated in more than 20 case studies and are now running at some CHTE-member companies. Successful application of the software has reportedly resulted in a 20% energy and cycle time saving through optimization.
Data mining
Data mining is the process of discovering meaningful correlations, patterns and trends by sifting through large amounts of data using pattern recognition technology in combination with statistical and mathematical techniques. Traditionally, production of new materials and the development of new processes have been done by experiment or by trial and error either in the laboratory or in the heat treat shop. Costly and lengthy characterization of data follows to determine the validity of material properties and the interaction of process variables. Today, using data mining techniques and fast computers, the process can be vastly simplified.
At CHTE, a project involved with computational estimation of heat-transfer curves relies on data mining techniques to help build new graphs from old ones (Fig. 2). Existing experiments are clustered based on their resulting graphs. The clustering criteria is analyzed and applied to characterize the cluster, which represents the discovered knowledge. Data mining can then be applied so, for example, given the input conditions of an experiment, you can estimate the resulting graph, or given the desired graph in an experiment, you can estimate a set of input conditions that were used to obtain it.
Understanding high-pressure gas quenching
The goal of the CHTE gas-quenching project is to develop a theoretical and experimental understanding of the variables that control the high-pressure gas quenching (HPGQ) process. In addition, gas quenching capability is to be compared to that of other quenching media.
A combined theoretical-numerical-experimental approach has been employed to achieve the objectives. Numerical simulation was used to explore effects that are difficult to obtain by means of experimental testing. Computational fluid dynamics (CFD) simu- lation (Fig. 3) has been used to study the gas-flow field and the heat-transfer coefficient variation in the quench chamber using a commercially available software package (FLUENT from Fluent Inc., Lebanon, N.H., USA; www.fluent.com). A systematic study of the effects of key variables such as gas type, gas pressure, fan speed, alignment of parts and quench chamber geometry/type on heat transfer capability is now underway.
Material modeling to predict phase transformation, residual stress distribution, distortion and mechanical properties corresponding to quenching process defined by the key variables is being done using a finite element code (DANTE/ABAQUS, Deformation Control Technology Inc., Cleveland, Ohio; http://www.deformationcontrol.com/). Finally, experimental tests are being performed to validate results from the theoretical analysis and the numerical simulation.
For further information on CHTE, contact Professor Diran Apelian (dapelian@wpi.edu).Other academic/industry partnerships doing interesting work that might be of interest to heat treaters include Advanced Steel Processing and Products Research Center (ASPPRC) at the Colorado School of Mines (http://www.mines.edu/research/aspprc/) and The Thermal Processing Technology Center at the Illinois Institute of Technology (tptc.iit.edu).
Sources:
• Diran Apelian, Howmet Professor of Mechanical Engineering and Director,Metal Processing Institute, private communication
• Data Management and Analysis, R&D Magazine, Jan. 2004
• Advances in Data Acquisition, NASA Tech Briefs, (www.tech-briefs.com),March 2005
• Robert Gaster,Deere & Co., private communication
• Lab for Computational Materials Science, Massachusetts Institute of Technology (www.mit.edu)
Additional related information may be found by searching for these (and other) key words/terms via BNP Media LINX at www.industrialheating.com: modeling, simulation, data mining, high-pressure gas quenching, computational fluid dynamics, numerical simulation.
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Last modified: July 15, 2008 14:41:33