SQA(TM): Surface Quality Assured Steel Bar Program
Bibliographic record
Abstract
OG Technologies, Inc. (OGT) has led this SQA (Surface Quality Assured Steel Bar) program to solve the major surface quality problems plaguing the US special quality steel bars and rods industry and their customers, based on crosscutting sensors and controls technologies. Surface defects in steel formed in a hot rolling process are one of the most common quality issues faced by the American steel industry, accounting for roughly 50% of the rejects or 2.5% of the total shipment. Unlike other problems such as the mechanical properties of the steel product, most surface defects are sporadic and cannot be addressed based on sampling techniques. This issue hurts the rolling industry and their customers in their process efficiency and operational costs. The goal of this program is to develop and demonstrate an SQA prototype, with synergy of HotEye® and other innovations, that enables effective rolling process control and efficient quality control. HotEye®, OGT’s invention, delivers high definition images of workpieces at or exceeding 1,450°C while the workpieces travel at 100 m/s. The elimination of surface defect rejects will be achieved through the integration of imaging-based quality assessment, advanced signal processing, predictive process controls and the integration with other quality control tools. The SQA program team, composed of entities capable of and experienced in (1) research, (2) technology manufacturing, (3) technology sales and marketing, and (4) technology end users, is very strong. There were 5 core participants: OGT, Georgia Institute of Technology (GIT), University of Wisconsin (UW), Charter Steel (Charter) and ArcelorMittal Indiana Harbor (Inland). OGT served as the project coordinator. OGT participated in both research and commercialization. GIT and UW provided significant technical inputs to this SQA project. The steel mills provided access to their rolling lines for data collection, design of experiments, host of technology test and verification, and first-hand knowledge of the most advanced rolling line operation in the US. This project lasted 5 years with 5 major tasks. The team successfully worked through the tasks with deliverables in detection, data analysis and process control. Technologies developed in this project were commercialized as soon as they were ready. For instance, the advanced surface defect detection algorithms were integrated into OGT’s HotEye® RSB systems late 2005, resulting in a more matured product serving the steel industry. In addition to the commercialization results, the SQA team delivered 7 papers and 1 patent. OGT was also recognized by two prestigious awards, including the R&D100 Award in 2006. To date, this SQA project has started to make an impact in the special bar quality industry. The resulted product, HotEye® RSB systems have been accepted by quality steel mills worldwide. Over 16 installations were completed, including 1 in Argentina, 2 in Canada, 2 in China, 2 in Germany, 2 in Japan, and 7 in the U.S. Documented savings in reduced internal rejects, improved customer satisfaction and simplified processes were reported from various mills. In one case, the mill reported over 50% reduction in its scrap, reflecting a significant saving in energy and reduction in emission. There exist additional applications in the steel industry where the developed technologies can be used. OGT is working toward bringing the developed technologies to more applications. Examples are: in-line inspection and process control for continuous casting, steel rails, and seamless tube manufacturing.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.028 | 0.010 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".