Cover Image

WearFormer-MO: Code, Model Snapshot and Evaluation Cells for TBM Disc-Cutter Wear Vision Measurement

Fuente: Zenodo

Saved in:

Bibliographic Details
Main Authors:	Liu, Guangyuan, Hongmei, Wang, Runqi, Li, Huiping, Zhang, Hongfei, Xie, Bright, Liu, Siyong, Zhang, Guizhen, Liu, Hongtao, Zhu, Dun, Liu, Sai, Ma
Format:	Recurso digital
Published:	Zenodo 2027
Subjects:	"Tunnel boring machine", "TBM", "Disc cutter wear", "Machine vision", "Multi-view learning", "Reliability-weighted attention", "Ordinal regression", "Vision Transformer", "SWAG pretraining", "Industrial measurement", "Underground construction"
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

_version_	1866902103949049856
author	Liu, Guangyuan Hongmei, Wang Runqi, Li Huiping, Zhang Hongfei, Xie Bright, Liu Siyong, Zhang Guizhen, Liu Hongtao, Zhu Dun, Liu Sai, Ma
author_facet	Liu, Guangyuan Hongmei, Wang Runqi, Li Huiping, Zhang Hongfei, Xie Bright, Liu Siyong, Zhang Guizhen, Liu Hongtao, Zhu Dun, Liu Sai, Ma
contents	<p>Code, frozen model snapshot, and full evaluation outputs (1500 per-config cells, aggregated metrics, figure data, cached SWAG ViT-B/16 features) accompanying the manuscript 'Vision Measurement of TBM Disc-Cutter Wear Using Reliability-Weighted Multi-View Aggregation and Ordinal-Metric Estimation' submitted to Measurement Science and Technology (2026). The image dataset (5.2 GB) will be added in a subsequent version of this record once the industrial co-author institutions complete their public-release authorisation. Headline numbers (8-view inference, 5-seed mean): ICS protocol MAE 0.33 mm and R² 0.987; LOCO protocol on unseen cutters MAE 2.44 mm and R² 0.80.</p>
format	Recurso digital
id	zenodo_https___doi_org_10_5281_zenodo_20396792
institution	Zenodo
language
publishDate	2027
publisher	Zenodo
record_format	zenodo
spellingShingle	WearFormer-MO: Code, Model Snapshot and Evaluation Cells for TBM Disc-Cutter Wear Vision Measurement Liu, Guangyuan Hongmei, Wang Runqi, Li Huiping, Zhang Hongfei, Xie Bright, Liu Siyong, Zhang Guizhen, Liu Hongtao, Zhu Dun, Liu Sai, Ma "Tunnel boring machine", "TBM", "Disc cutter wear", "Machine vision", "Multi-view learning", "Reliability-weighted attention", "Ordinal regression", "Vision Transformer", "SWAG pretraining", "Industrial measurement", "Underground construction" <p>Code, frozen model snapshot, and full evaluation outputs (1500 per-config cells, aggregated metrics, figure data, cached SWAG ViT-B/16 features) accompanying the manuscript 'Vision Measurement of TBM Disc-Cutter Wear Using Reliability-Weighted Multi-View Aggregation and Ordinal-Metric Estimation' submitted to Measurement Science and Technology (2026). The image dataset (5.2 GB) will be added in a subsequent version of this record once the industrial co-author institutions complete their public-release authorisation. Headline numbers (8-view inference, 5-seed mean): ICS protocol MAE 0.33 mm and R² 0.987; LOCO protocol on unseen cutters MAE 2.44 mm and R² 0.80.</p>
title	WearFormer-MO: Code, Model Snapshot and Evaluation Cells for TBM Disc-Cutter Wear Vision Measurement
topic	"Tunnel boring machine", "TBM", "Disc cutter wear", "Machine vision", "Multi-view learning", "Reliability-weighted attention", "Ordinal regression", "Vision Transformer", "SWAG pretraining", "Industrial measurement", "Underground construction"
url	https://doi.org/10.5281/zenodo.20396792