SQA(TM): Surface Quality Assured Steel Bar Program
Notice bibliographique
Résumé
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.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,002 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».