Towards an Adaptive Production Chain for Sustainable Sheet-Metal Blanked Components

[1]  F. Arnaud,et al.  From core referencing to data re-use: two French national initiatives to reinforce paleodata stewardship (National Cyber Core Repository and LTER France Retro-Observatory) , 2017 .

[2]  Konstantinos Salonitis,et al.  Forty Sixth CIRP Conference on Manufacturing Systems 2013 Energy efficient manufacturing from machine tools to manufacturing systems , 2013 .

[3]  Ton van den Boogaard,et al.  Estimating product-to-product variations in metal forming using force measurements , 2017 .

[4]  Zhen Zhao,et al.  State-of-the-art and future challenge in fine-blanking technology , 2019, Prod. Eng..

[5]  Peter Nyhuis,et al.  Integrative factory, technology, and product planning-systemizing the information transfer on the operational level , 2010, Prod. Eng..

[6]  Matthew Doolan,et al.  Using stamping punch force variation for the identification of changes in lubrication and wear mechanism , 2017 .

[7]  Klaus Wehrle,et al.  A Case for Integrated Data Processing in Large-Scale Cyber-Physical Systems , 2019, HICSS.

[8]  Fritz Klocke,et al.  A predictive model for die roll height in fine blanking using machine learning methods , 2018 .

[9]  Thomas Bergs,et al.  Punch-to-Punch Variations in Stamping Processes , 2020, 2020 IEEE 18th World Symposium on Applied Machine Intelligence and Informatics (SAMI).

[10]  Christian Brecher,et al.  FactDAG: Formalizing Data Interoperability in an Internet of Production , 2020, IEEE Internet of Things Journal.

[11]  Ruxu Du,et al.  Fault diagnosis of stamping process based on empirical mode decomposition and learning vector quantization , 2007 .

[12]  T. Jayakumar,et al.  A review of the application of acoustic emission techniques for monitoring forming and grinding processes , 2005 .

[13]  Fritz Klocke,et al.  Modeling of the temperature field in the workpiece external zone as a function of the grinding wheel topography , 2018 .

[14]  Li Yan,et al.  Applications of artificial intelligence in grinding , 1994 .

[15]  Masao Murakawa,et al.  Prediction of die-roll in fine blanking by use of profile parameters , 2017 .

[16]  T. Bergs,et al.  AI-based Framework for Deep Learning Applications in Grinding , 2020, 2020 IEEE 18th World Symposium on Applied Machine Intelligence and Informatics (SAMI).

[17]  Ichiro Inasaki,et al.  Monitoring Systems for Grinding Processes , 2006 .

[18]  P. Niemietz,et al.  In-situ material classification in sheet-metal blanking using deep convolutional neural networks , 2019, Production Engineering.

[19]  Erik Schultes,et al.  The FAIR Guiding Principles for scientific data management and stewardship , 2016, Scientific Data.

[20]  Klaus Wehrle,et al.  Stamping Process Modelling in an Internet of Production , 2020 .

[21]  Berend Denkena,et al.  Methodology for integrative production planning in highly dynamic environments , 2019 .

[22]  C. Herrmann,et al.  Determining optimal process parameters to increase the eco-efficiency of grinding processes , 2014 .

[23]  Rainer Stiefelhagen,et al.  CNN-based sensor fusion techniques for multimodal human activity recognition , 2017, SEMWEB.

[24]  Andrew Kusiak,et al.  Fundamentals of smart manufacturing: A multi-thread perspective , 2019, Annu. Rev. Control..

[25]  Peter Groche,et al.  Feature-based Supervision of Shear Cutting Processes on the Basis of Force Measurements: Evaluation of Feature Engineering and Feature Extraction , 2019, Procedia Manufacturing.