0000000000640639

AUTHOR

Y. Tina Lee

showing 2 related works from this author

Model-based Engineering for the Integration of Manufacturing Systems with Advanced Analytics

2016

To employ data analytics effectively and efficiently on manufacturing systems, engineers and data scientists need to collaborate closely to bring their domain knowledge together. In this paper, we introduce a domain-specific modeling approach to integrate a manufacturing system model with advanced analytics, in particular neural networks, to model predictions. Our approach combines a set of meta-models and transformation rules based on the domain knowledge of manufacturing engineers and data scientists. Our approach uses a model of a manufacturing process and its associated data as inputs, and generates a trained neural network model as an output to predict a quantity of interest. This pape…

0209 industrial biotechnologyProcess (engineering)Computer scienceneural network02 engineering and technology[INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI][SPI]Engineering Sciences [physics]020901 industrial engineering & automationComputer-integrated manufacturing0202 electrical engineering electronic engineering information engineering[ SPI ] Engineering Sciences [physics][ INFO.INFO-AI ] Computer Science [cs]/Artificial Intelligence [cs.AI]Meta-modelArtificial neural networkbusiness.industrymeta-modelData scienceNeural networkPredictive modelingMetamodelingWorkflowAnalyticsData analyticsData analysisDomain knowledgemanufacturing process020201 artificial intelligence & image processingManufacturing processbusinessSoftware engineeringpredictive modeling
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A Neural Network Meta-Model and its Application for Manufacturing

2015

International audience; Manufacturing generates a vast amount of data both from operations and simulation. Extracting appropriate information from this data can provide insights to increase a manufacturer's competitive advantage through improved sustainability, productivity, and flexibility of their operations. Manufacturers, as well as other industries, have successfully applied a promising statistical learning technique, called neural networks (NNs), to extract meaningful information from large data sets, so called big data. However, the application of NN to manufacturing problems remains limited because it involves the specialized skills of a data scientist. This paper introduces an appr…

[INFO.INFO-AI] Computer Science [cs]/Artificial Intelligence [cs.AI]0209 industrial biotechnology[SPI] Engineering Sciences [physics]Computer scienceneural networkBig dataContext (language use)02 engineering and technologycomputer.software_genreMachine learningCompetitive advantageData modeling[INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI][SPI]Engineering Sciences [physics]020901 industrial engineering & automationPMML0202 electrical engineering electronic engineering information engineering[ SPI ] Engineering Sciences [physics][ INFO.INFO-AI ] Computer Science [cs]/Artificial Intelligence [cs.AI]data analyticsArtificial neural networkbusiness.industrymeta-modelMetamodelingmanufacturingAnalyticsSustainabilityPredictive Model Markup LanguageData analysis020201 artificial intelligence & image processingData miningArtificial intelligencebusinesscomputer
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