Evidence map›Paper›PMID 41413432›Full record

ArticleScientific reports2025

Incorporating defects and moisture in MOE evaluation for automated timber grading.

Min Ji, Wei Zhang, Liping Cai, Yang Zhao, Hu Miao, Rui Gao, Guofu Wang, Jiakai Han, Xingliang Diao, Ziqiong Zha

Abstract read
In one paragraph

Article in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

10 authors.

Min JiResearch Institute of Wood Industry, Chinese Academy of Forestry, Beijing, 100091, China.
Wei ZhangResearch Institute of Wood Industry, Chinese Academy of Forestry, Beijing, 100091, China. wzhang@caf.ac.cn.
Liping CaiResearch Institute of Wood Industry, Chinese Academy of Forestry, Beijing, 100091, China.
Yang ZhaoResearch Institute of Wood Industry, Chinese Academy of Forestry, Beijing, 100091, China.
Hu MiaoResearch Institute of Wood Industry, Chinese Academy of Forestry, Beijing, 100091, China.
Rui GaoResearch Institute of Wood Industry, Chinese Academy of Forestry, Beijing, 100091, China.
Guofu WangResearch Institute of Wood Industry, Chinese Academy of Forestry, Beijing, 100091, China.
Jiakai HanResearch Institute of Wood Industry, Chinese Academy of Forestry, Beijing, 100091, China.
Xingliang DiaoResearch Institute of Wood Industry, Chinese Academy of Forestry, Beijing, 100091, China.
Ziqiong ZhaResearch Institute of Wood Industry, Chinese Academy of Forestry, Beijing, 100091, China.

Funding

Fujian Provincial Science and Technology Major Project:Key technology and equipment for continuous and intelligent production of glued laminated bamboo 2024HZ026011National Key R&D Program of China 2023YFD2201500National Key Research and Development Plan project: Key equipment technology for wood and bamboo harvesting and processing 2024YFD2200700National Natural Science Foundation of China (CN) 31670721
6 · The paper itself

Abstract

In today’s industrial landscape, automated timber structural grading plays a pivotal role in optimizing productivity and operational efficiency. The anisotropic nature of wood renders standard visual grading a challenging task, often resulting in a grading process with low repeatability. Moreover, traditional grading methods are not only time-consuming but also susceptible to human bias and inconsistencies, which may lead to underestimation of the timber’s mechanical potential. To address these challenges, this study proposes a grading method that integrates external quality evaluation of timber and the internal elastic modulus properties. Additionally, an automated production line is developed featuring automatic loading and unloading, machine vision optimized sorting, online moisture content detection, mechanical stress grading, multi-sensor data integration, and remote operation and maintenance diagnostic functions. The developed intelligent automated grading system included five sections, including Automatic loading and unloading, Machine vision, Moisture content detection, Mechanical stress grading, and multi-sensor data coupling and remote O&M diagnosis. The novelty of this study included integrating the influences of multiple factors such as defects and moisture content, as well as their dynamic modulus of elasticity in the grading operations of structural timber. The methodologies employed not only promoted broader adoption and deeper understanding of automation in the wood processing industry but also positioned themselves as exemplary applications of advanced automation technology. The practical applicability of automated grading technology in industrial production lines was thoroughly validated. Experimental outcomes unequivocally demonstrated that the automated solution markedly enhanced grading efficiency while concomitantly reducing labor costs. This automated structural timber grading line was successfully certified by the Japanese JAS (Japanese Agricultural Standard) in 2023, which is the highest level of certification for organic agricultural products by the Ministry of Agriculture, Forestry, and Fisheries of Japan. This ensures that the graded structural timber exported to Japan meets the Japanese Agricultural Standards. By incorporating advanced automation technology, this grading process section can be flexibly integrated into automated wood processing production lines. This advancement anticipated the realization of intelligent and automated production line management, thereby providing robust technical underpinnings for the transformation and upgrading of the wood manufacturing industry.

Indexed as

AutomationConstructional timberDetectionGradingWooden buildings

Identifiers

PMID41413432
PMCPMC12905361

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.