Recursive quality optimization of a smart forming tool under use of perception based hybrid datasets for training of a deep neural network


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{
  "metadata": {
    "is_last": true, 
    "publication_date": "Jan 31, 2022, 15:56:37", 
    "edited_by": 576, 
    "version": 1, 
    "license": "Creative Commons Attribution 4.0 International", 
    "license_addendum": null, 
    "_files": [
      {
        "checksum": "md5:a5233cc5e8b5d5fb201611175f5e4b61", 
        "key": "220122_SIMKI_Training_Datasets.zip", 
        "size": 17828319, 
        "description": "This archive contains the training and validation datasets, including the trained neural network of the SIMKI-Project of Hochschule-Aalen."
      }
    ], 
    "mcid": "2022.16", 
    "keywords": [
      "Artificial Intelligence", 
      "Gocator", 
      "Defect Detection"
    ], 
    "contributors": [
      {
        "givennames": "Sebastian", 
        "email": "Sebastian.Feldmann@HS-Aalen.de", 
        "familyname": "Feldmann", 
        "affiliations": [
          "University of Applied Science - Aalen, Beethovenstr. 1, 73430 Aalen, Germany"
        ]
      }, 
      {
        "givennames": "Michael", 
        "email": "Michael.Schmiedt@HS-Aalen.de", 
        "familyname": "Schmiedt", 
        "affiliations": [
          "University of Applied Science - Aalen, Beethovenstr. 1, 73430 Aalen, Germany"
        ]
      }, 
      {
        "givennames": "Julian", 
        "email": "Julian.Schlosser@HS-Aalen.de", 
        "familyname": "Schlosser", 
        "affiliations": [
          "University of Applied Science - Aalen, Beethovenstr. 1, 73430 Aalen, Germany"
        ]
      }, 
      {
        "givennames": "Wolfgang", 
        "email": "Wolfgang.Rimkus@HS-Aalen.de", 
        "familyname": "Rimkus", 
        "affiliations": [
          "University of Applied Science - Aalen, Beethovenstr. 1, 73430 Aalen, Germany"
        ]
      }, 
      {
        "givennames": "Tobias", 
        "email": "Tobias.Stempfle@Studimail.HS-Aalen.de", 
        "familyname": "Stempfle", 
        "affiliations": [
          "University of Applied Science - Aalen, Beethovenstr. 1, 73430 Aalen, Germany"
        ]
      }
    ], 
    "status": "published", 
    "doi": "10.24435/materialscloud:74-x1", 
    "title": "Recursive quality optimization of a smart forming tool under use of perception based hybrid datasets for training of a deep neural network", 
    "id": "1202", 
    "description": "In industrial metal forming processes, the generation of datasets for inline and optical quality assessment is expensive and time-consuming. Within the research project SimKI, conventional metal forming plants were digitalized under use of perception-based sensors in combination with a completely redesigned forming tool. The integration of optical quality observation methods connected with a retrofitting approach of the press tool provides the opportunity to generate an information-feedback loop that predicts part defects prior to their occurrence. The SimKI-method additionally combines conventional statistical measurement methods with AI-based defect detection algorithms that are trained by a) generic datasets of a finite-element simulation, b) real component images of a 3D imaging device, and c) a combination of both. The generated datasets are used to accelerate the training of a DNN-based algorithm in order to identify the position and deviation from the agreed quality. The high degree of innovation is based on obtaining real-time component quality information under use of AI-based optical quality assessment, which in turn provides information to the control algorithm of the smart forming tool.", 
    "owner": 637, 
    "_oai": {
      "id": "oai:materialscloud.org:1202"
    }, 
    "conceptrecid": "1201", 
    "references": [
      {
        "citation": "S. Feldmann, M. Schmiedt, J. M. Schlosser, W. Rimkus, T. Stempfle, Recursive Quality Optimization of a Smart Forming Tool under use of Perception Based Hybrid Datasets for Training of a Deep Neural Network (submitted).", 
        "type": "Journal reference"
      }
    ]
  }, 
  "updated": "2022-01-31T14:56:37.436636+00:00", 
  "revision": 17, 
  "id": "1202", 
  "created": "2022-01-04T17:05:57.999212+00:00"
}