Smart and Sustainable Manufacturing Systems最新文献_第8页

A Generalized Multisensor Real-Time Tool Condition–Monitoring Approach Using Deep Recurrent Neural Network 基于深度递归神经网络的广义多传感器工具状态实时监测方法

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Smart and Sustainable Manufacturing Systems Pub Date : 2019-02-01 DOI: 10.1520/ssms20190020

M. Hassan, A. Sadek, M. Attia

引用次数: 8

A Self-Organizing Evolutionary Method to Model and Optimize Correlated Multiresponse Metrics for Additive Manufacturing Processes 增材制造过程中相关多响应指标建模与优化的自组织进化方法

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Smart and Sustainable Manufacturing Systems Pub Date : 2019-02-01 DOI: 10.1520/ssms20190024

Osama Aljarrah, Jun Li, Wenzhen Huang, A. Heryudono, Jing Bi

引用次数: 0

Utilizing Detector Filters for Noise Reduction in X-Ray Computer Tomography Scanning for the Inspection of the Structural Integrity of Additive Manufactured Metal Parts 利用检测器滤波器在x射线计算机断层扫描中降噪以检测增材制造金属零件的结构完整性

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Smart and Sustainable Manufacturing Systems Pub Date : 2019-01-01 DOI: 10.1520/ssms20180042

A. Tawfik, S. Nicholson, R. Racasan, L. Blunt, P. Bills

引用次数: 1

A Standardized PMML Format for Representing Convolutional Neural Networks with Application to Defect Detection. 卷积神经网络在缺陷检测中的标准化PMML格式。

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Smart and Sustainable Manufacturing Systems Pub Date : 2019-01-01

Max Ferguson, Yung-Tsun Tina Lee, Anantha Narayanan, Kincho H Law

{"title":"A Standardized PMML Format for Representing Convolutional Neural Networks with Application to Defect Detection.","authors":"Max Ferguson, Yung-Tsun Tina Lee, Anantha Narayanan, Kincho H Law","doi":"","DOIUrl":"","url":null,"abstract":"<p><p>Convolutional neural networks are becoming a popular tool for image processing in the engineering and manufacturing sectors. However, managing the storage and distribution of trained models is still a difficult task, partially due to the lack of standardized methods for deep neural network representation. Additionally, the interoperability between different machine learning frameworks remains poor. This paper seeks to address this issue by proposing a standardized format for convolutional neural networks, based on the Predictive Model Markup Language (PMML). A new standardized schema is proposed to represent a range of convolutional neural networks, including classification, regression and semantic segmentation systems. To demonstrate the practical application of this standard, a semantic segmentation model, which is trained to detect casting defects in Xray images, is represented in the proposed PMML format. A high-performance scoring engine is developed to evaluate images and videos against the PMML model. The utility of the proposed format and the scoring engine is evaluated by benchmarking the performance of the defect detection models on a range of different computational platforms.</p>","PeriodicalId":51957,"journal":{"name":"Smart and Sustainable Manufacturing Systems","volume":"3 1","pages":"79-97"},"PeriodicalIF":1.0,"publicationDate":"2019-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7537490/pdf/nihms-1588433.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"38470468","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Learn to Learn: Application to Topology Optimization 学习学习:拓扑优化的应用

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Smart and Sustainable Manufacturing Systems Pub Date : 2018-12-12 DOI: 10.1520/SSMS20180039

Qi Wei, I. Akrotirianakis, A. Dasgupta, A. Chakraborty

引用次数: 2

A Knowledge-Enriched Computational Model to Support Lifecycle Activities of Computational Models in Smart Manufacturing 支持智能制造中计算模型生命周期活动的知识丰富计算模型

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Smart and Sustainable Manufacturing Systems Pub Date : 2018-11-30 DOI: 10.1520/SSMS20180036

Heng Zhang, U. Roy

{"title":"A Knowledge-Enriched Computational Model to Support Lifecycle Activities of Computational Models in Smart Manufacturing","authors":"Heng Zhang, U. Roy","doi":"10.1520/SSMS20180036","DOIUrl":"https://doi.org/10.1520/SSMS20180036","url":null,"abstract":"Due to the needs in supporting lifecycle activities of computational models in Smart Manufacturing (SM), a Knowledge Enriched Computational Model (KECM) is proposed in this dissertation to capture and integrate domain knowledge with standardized computational models. The KECM captures domain knowledge into information model(s), physics-based model(s), and rationales. To support model development in a distributed environment, the KECM can be used as the medium for formal information sharing between model developers. A case study has been developed to demonstrate the utilization of the KECM in supporting the construction of a Bayesian Network model. To support the deployment of computational models in SM systems, the KECM can be used for data integration between computational models and SM systems. A case study has been developed to show the deployment of a Constraint Programming optimization model into a Business To Manufacturing Markup Language (B2MML) -based system. In another situation where multiple computational models need to be deployed, the KECM can be used to support the combination of computational models. A case study has been developed to show the combination of an Agent-based model and a Decision Tree model using the KECM. To support model retrieval, a semantics-based method is suggested in this dissertation. As an example, a dispatching rule model retrieval problem has been addressed with a semantics-based approach. The semantics-based approach has been verified and it demonstrates good capability in using the KECM to retrieve computational models. A KNOWLEDGE ENRICHED COMPUTATIONAL MODEL TO SUPPORT LIFECYCLE ACTIVITIES OF COMPUTATIONAL MODELS IN SMART MANUFACTURING","PeriodicalId":51957,"journal":{"name":"Smart and Sustainable Manufacturing Systems","volume":"14 1","pages":""},"PeriodicalIF":1.0,"publicationDate":"2018-11-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"78449020","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 2

Defect Detection and Monitoring in Metal Additive Manufactured Parts through Deep Learning of Spatially Resolved Acoustic Spectroscopy Signals 基于空间分辨声光谱信号深度学习的金属增材制造零件缺陷检测与监测

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Smart and Sustainable Manufacturing Systems Pub Date : 2018-11-19 DOI: 10.1520/SSMS20180035

Jacob M. Williams, P. Dryburgh, A. Clare, Prahalada K. Rao, A. Samal

引用次数: 22

Modeling Type-1 Singleton Fuzzy Logic Systems Using Statistical Parameters in Foundry Temperature Control Application 用统计参数建模1型单态模糊逻辑系统在铸造厂温控中的应用

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Smart and Sustainable Manufacturing Systems Pub Date : 2018-11-16 DOI: 10.1520/SSMS20180031

Pascual Noradino Montes-Dorantes, Adriana Mexicano Santoyo, G. Mendez

引用次数: 1

Use of On-Demand Cloud Services to Model the Optimization of an Austenitization Furnace 使用按需云服务对奥氏体化炉的优化进行建模

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Smart and Sustainable Manufacturing Systems Pub Date : 2018-11-13 DOI: 10.1520/SSMS20180024

P. Korambath, Harish Ganesh, Jianwu Wang, M. Baldea, James F. Davis

引用次数: 4

Optimization of the Cool-Down Process for a System of Sintering Furnaces 某烧结炉系统冷却过程的优化

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Smart and Sustainable Manufacturing Systems Pub Date : 2018-07-05 DOI: 10.1520/SSMS20170015

A. Rogers, Bryan P. Rasmussen

引用次数: 0