Abstract
A master welder uses his sensory perceptions to evaluate the process and connect them with his/her knowledge base to take the necessary corrective measures with his/her acquired skills to make a good weld. All these actions must take place in real time. Success depends on intuition and skills, and the procedure is labor-intensive and frequently unreliable. The solution is intelligent weld manufacturing. The ultimate goal of intelligent weld manufacturing would involve sensing and control of heat source position, weld temperature, weld penetration, defect formation and ultimately control of microstructure and properties. This involves a solution to a problem (welding) with many highly coupled and nonlinear variables. The trend is to use an emerging tool known as intelligent control. This approach enables the user to choose a desirable end factor such as properties, defect control, or productivity to derive the selection of process parameters such as current, voltage, or speed to provide for appropriate control of the process. Important elements of intelligent manufacturing are sensing and control theory and design, process modeling, and artificial intelligence. Significant progress has been made in all these areas. Integrated computational welding engineering (ICWE) is an emerging field that will aid in the realization of intelligent weld manufacturing. The paper will discuss the progress in process modeling, microstructure, properties, and process control and automation and the importance of ICWE. Also, control and automation strategies for friction stir welding will be discussed.
| Original language | English |
|---|---|
| Title of host publication | Transactions on Intelligent Welding Manufacturing |
| Publisher | Springer |
| Pages | 3-30 |
| Number of pages | 28 |
| DOIs | |
| State | Published - 2018 |
Publication series
| Name | Transactions on Intelligent Welding Manufacturing |
|---|---|
| ISSN (Print) | 2520-8519 |
| ISSN (Electronic) | 2520-8527 |
Funding
Doyle and Conrady describe a program for the design, construction, and demonstration of a prototype programmable automated welding system [146]. The program, known as the programmable automated welding system (PAWS), was sponsored by the US Naval Surface Warfare Center. Doyle and Conrady developed a system with control capabilities to accept, arbitrate, and reach its inputs from multiple sensors. The authors would like to acknowledge Ms. Shirin Badlani for her help in preparing the manuscript. We would also like to acknowledge Prof. Yuming Zhang (University of Kentucky) and Prof. G.E. Cook (Vanderbilt University) for their review and valuable comments. This material is based upon work supported by the U.S. Department of Energy, Office of Science, Office of Basic Energy Sciences, Office of Nuclear Energy, and Office of Energy Efficiency and Renewable Energy under a prime contract with Oak Ridge National Laboratory (ORNL). ORNL is managed by UT-Battelle, LLC for the U.S. Department of Energy under Contract DE-AC05-00OR22725. This manuscript has been authored by UT-Battelle, LLC, under contract DE-AC05-00OR22725 with the US Department of Energy (DOE). The US government retains and the publisher, by accepting the article for publication, acknowledges that the US government retains a nonexclusive, paid-up, irrevocable, worldwide license to publish or reproduce the published form of this manuscript, or allow others to do so, for US government purposes. DOE will provide public access to these results of federally sponsored research in accordance with the DOE Public Access Plan (http://energy.gov/downloads/doe-public-access-plan). Acknowledgements The authors would like to acknowledge Ms. Shirin Badlani for her help in preparing the manuscript. We would also like to acknowledge Prof. Yuming Zhang (University of Kentucky) and Prof. G.E. Cook (Vanderbilt University) for their review and valuable comments. This material is based upon work supported by the U.S. Department of Energy, Office of Science, Office of Basic Energy Sciences, Office of Nuclear Energy, and Office of Energy Efficiency and Renewable Energy under a prime contract with Oak Ridge National Laboratory (ORNL). ORNL is managed by UT-Battelle, LLC for the U.S. Department of Energy under Contract DE-AC05-00OR22725. This manuscript has been authored by UT-Battelle, LLC, under contract DE-AC05-00OR22725 with the US Department of Energy (DOE). The US government retains and the publisher, by accepting the article for publication, acknowledges that the US government retains a nonexclusive, paid-up, irrevocable, worldwide license to publish or reproduce the published form of this manuscript, or allow others to do so, for US government purposes. DOE will provide public access to these results of federally sponsored research in accordance with the DOE Public Access Plan (http://energy.gov/downloads/doe-public-access-plan).
| Funders | Funder number |
|---|---|
| DOE Public Access Plan | |
| U.S. Department of Energy | |
| Office of Science | |
| Office of Energy Efficiency and Renewable Energy | |
| Office of Nuclear Energy | |
| Basic Energy Sciences | |
| Oak Ridge National Laboratory | DE-AC05-00OR22725 |
| Vanderbilt University | |
| Naval Surface Warfare Center |
Keywords
- Automation
- Control
- Convection
- Friction stir welding
- Geometry
- Integration
- Intelligent
- Modeling
- Sensing
- Solidification
- Weld manufacturing
- Weld pool
