JPWO2023181935A5 - - Google Patents

Download PDF

Info

Publication number
JPWO2023181935A5
JPWO2023181935A5 JP2024509971A JP2024509971A JPWO2023181935A5 JP WO2023181935 A5 JPWO2023181935 A5 JP WO2023181935A5 JP 2024509971 A JP2024509971 A JP 2024509971A JP 2024509971 A JP2024509971 A JP 2024509971A JP WO2023181935 A5 JPWO2023181935 A5 JP WO2023181935A5
Authority
JP
Japan
Prior art keywords
measurement information
prediction
measuring
composite material
fiber composite
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Pending
Application number
JP2024509971A
Other languages
English (en)
Japanese (ja)
Other versions
JPWO2023181935A1 (https=
Filing date
Publication date
Application filed filed Critical
Priority claimed from PCT/JP2023/008748 external-priority patent/WO2023181935A1/ja
Publication of JPWO2023181935A1 publication Critical patent/JPWO2023181935A1/ja
Publication of JPWO2023181935A5 publication Critical patent/JPWO2023181935A5/ja
Pending legal-status Critical Current

Links

JP2024509971A 2022-03-24 2023-03-08 Pending JPWO2023181935A1 (https=)

Applications Claiming Priority (2)

Application Number Priority Date Filing Date Title
JP2022047924 2022-03-24
PCT/JP2023/008748 WO2023181935A1 (ja) 2022-03-24 2023-03-08 予測装置、予測システムおよび予測プログラム

Publications (2)

Publication Number Publication Date
JPWO2023181935A1 JPWO2023181935A1 (https=) 2023-09-28
JPWO2023181935A5 true JPWO2023181935A5 (https=) 2024-11-29

Family

ID=88101259

Family Applications (1)

Application Number Title Priority Date Filing Date
JP2024509971A Pending JPWO2023181935A1 (https=) 2022-03-24 2023-03-08

Country Status (2)

Country Link
JP (1) JPWO2023181935A1 (https=)
WO (1) WO2023181935A1 (https=)

Families Citing this family (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
WO2025142133A1 (ja) * 2023-12-27 2025-07-03 コニカミノルタ株式会社 予測システム、予測方法及びプログラム
WO2025142134A1 (ja) * 2023-12-27 2025-07-03 コニカミノルタ株式会社 予測システム、予測方法及びプログラム

Family Cites Families (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US4959548A (en) * 1989-05-02 1990-09-25 The United States Of America As Represented By The United States Department Of Energy Neutron apparatus for measuring strain in composites
JP6478189B2 (ja) * 2015-09-18 2019-03-06 株式会社リガク 応力解析装置、方法およびプログラム
JP7118941B2 (ja) * 2018-11-16 2022-08-16 東レエンジニアリング株式会社 樹脂成形解析方法、プログラムおよび記録媒体
CN113892107A (zh) * 2019-05-24 2022-01-04 多材料焊接有限公司 连接物至物体的超声波装配

Similar Documents

Publication Publication Date Title
JPWO2023181935A5 (https=)
Jin et al. Recent advances and applications of machine learning in experimental solid mechanics: A review
Godin et al. Challenges and limitations in the identification of acoustic emission signature of damage mechanisms in composites materials
Wei et al. A deep learning method for the impact damage segmentation of curve-shaped CFRP specimens inspected by infrared thermography
Chandarana et al. Early damage detection in composites during fabrication and mechanical testing
Yuan et al. Timber moisture detection using wavelet packet decomposition and convolutional neural network
Ghorbel et al. Characterization of thermo-mechanical and fracture behaviors of thermoplastic polymers
Sapidis et al. A deep learning approach for autonomous compression damage identification in fiber-reinforced concrete using piezoelectric lead zirconate titanate transducers
JP7358648B2 (ja) 成形体領域の検査プログラム、成形体領域の検査方法、成形体領域の検査装置
Krampikowska et al. The use of the acoustic emission method to identify crack growth in 40CrMo steel
Siracusano et al. Automatic crack classification by exploiting statistical event descriptors for deep learning
CN112699907B (zh) 数据融合的方法、装置和设备
JPWO2023181936A5 (https=)
Mills et al. Identifying defects in aerospace composite sandwich panels using high-definition distributed optical fibre sensors
Yu et al. Real-time life-cycle monitoring of composite structures using piezoelectric-fiber hybrid sensor network
Wandowski et al. Analysis of air-coupled transducer-based elastic waves generation in CFRP plates
Hall et al. In situ thermoset cure sensing: A review of correlation methods
Muir et al. Quantitative benchmarking of acoustic emission machine learning frameworks for damage mechanism identification
Zhang et al. Adaptive crack damage identification based on multi-scale sample entropy under variable temperature environment
Muller et al. Investigation of self-heating and damage progression in woven carbon fibre composite materials, following the fibres direction, under static and cyclic loading
Affronti et al. Analysis of forming limits in sheet metal forming with pattern recognition methods. Part 1: Characterization of onset of necking and expert evaluation
Moustakidis et al. Deep learning autoencoders for fast fourier transform-based clustering and temporal damage evolution in acoustic emission data from composite materials
Shiozawa et al. Fatigue damage evaluation of short carbon fiber reinforced plastics based on phase information of thermoelastic temperature change
Palacios Moreno et al. Damage Mechanism Characterization of Glass Fiber-Reinforced Polymer Composites: A Study Using Acoustic Emission Technique and Unsupervised Machine Learning Algorithms
Habib et al. Performance degradation assessment of concrete beams based on acoustic emission burst features and Mahalanobis—Taguchi system