Zmiany

              
    

          
          
        
        

            
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{
            
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  "author": "Toby Dukes",
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  "author": "Toby Dukes",
            
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  "author_email": "toby.dukes@amfg.ai",
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  "author_email": "toby.dukes@amfg.ai",
            
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"smdh-amfg-predictive-tooling-maintenance",
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"smdh-amfg-predictive-tooling-maintenance",
            
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  "creator_user_id": "1a7e53fd-7c0f-4751-80ed-46abbbec41eb",
            
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  "license_id": "notspecified",
            
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  "license_title": "License not specified",
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  "license_title": "License not specified",
            
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  "maintainer": "Toby Dukes",
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  "maintainer": "Toby Dukes",
            
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  "maintainer_email": "toby.dukes@amfg.ai",
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  "maintainer_email": "toby.dukes@amfg.ai",
            
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  "metadata_created": "2025-02-24T17:51:14.568612",
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  "metadata_created": "2025-02-24T17:51:14.568612",
            
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  "name": 
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  "name": 
            
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tonomous-manufacturing-ltd--smdh-amfg-predictive-tooling-maintenance",
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tonomous-manufacturing-ltd--smdh-amfg-predictive-tooling-maintenance",
            
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  "notes": "This is the data provided for each SME for the lighthouse 
18
  "notes": "This is the data provided for each SME for the lighthouse 
            
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project Zero barrier predictive maintenance for SMEs in the machining 
19
project Zero barrier predictive maintenance for SMEs in the machining 
            
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industry. There will be 4 separate XLS files uploaded for each of the 
20
industry. There will be 4 separate XLS files uploaded for each of the 
            
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testbeds that were used for data collection. The data varies slightly 
21
testbeds that were used for data collection. The data varies slightly 
            
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per testbed based on machines and tools used.",
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per testbed based on machines and tools used.",
            
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  "num_tags": 2,
            
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  "organization": {
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  "organization": {
            
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    "approval_status": "approved",
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    "approval_status": "approved",
            
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    "created": "2025-02-24T15:04:07.122089",
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    "created": "2025-02-24T15:04:07.122089",
            
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    "description": "",
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    "image_url": "",
            
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    "is_organization": true,
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    "is_organization": true,
            
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    "name": "autonomous-manufacturing-ltd",
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    "name": "autonomous-manufacturing-ltd",
            
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    "state": "active",
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    "state": "active",
            
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    "title": "Autonomous Manufacturing Ltd",
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    "title": "Autonomous Manufacturing Ltd",
            
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    "type": "organization"
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    "type": "organization"
            
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  },
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  "owner_org": "f59d8904-f418-45f0-b94b-bc5980219766",
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  "owner_org": "f59d8904-f418-45f0-b94b-bc5980219766",
            
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      "cloud_storage_key": 
            
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nomous-manufacturing-ltd/smdh-amfg-predictive-tooling-maintenance/SME1 
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nomous-manufacturing-ltd/smdh-amfg-predictive-tooling-maintenance/SME1 
            
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Tooling data.",
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Tooling data.",
            
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      "created": "2025-02-24T17:53:19.676938",
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      "datastore_active": false,
            
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      "description": "Data for the SME1 - including parts created, 
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      "description": "Data for the SME1 - including parts created, 
            
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tools / machines used. part geometry and tooling information. \r\nSME 
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tools / machines used. part geometry and tooling information. \r\nSME 
            
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creates small, bespoke parts.",
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creates small, bespoke parts.",
            
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      "format": "XLSX",
            
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      "hash": "",
            
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      "name": "SME1 Tooling data.",
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      "name": "SME1 Tooling data.",
            
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      "state": "active",
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      "state": "active",
            
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      "tags": "Method-Machine_learning",
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      "tags": "Method-Machine_learning",
            
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      "url": 
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      "url": 
            
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ce/9153bbd8-cbeb-4c36-84eb-946eb2ec0b79/download/sme1_smdh_data.xlsx",
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ce/9153bbd8-cbeb-4c36-84eb-946eb2ec0b79/download/sme1_smdh_data.xlsx",
            
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nomous-manufacturing-ltd/smdh-amfg-predictive-tooling-maintenance/SME2 
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nomous-manufacturing-ltd/smdh-amfg-predictive-tooling-maintenance/SME2 
            
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Tooling Data",
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Tooling Data",
            
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      "created": "2025-02-25T12:21:44.983320",
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      "datastore_active": false,
            
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      "description": "Tooling Data capture from SME2 Shows part pass / 
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      "description": "Tooling Data capture from SME2 Shows part pass / 
            
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failure rate based on the expected v actual tool life calculation.",
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failure rate based on the expected v actual tool life calculation.",
            
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      "format": ".xlsx",
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      "format": ".xlsx",
            
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      "name": "SME2 Tooling Data",
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      "name": "SME2 Tooling Data",
            
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      "state": "active",
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      "tags": "Method-Machine_learning",
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      "tags": "Method-Machine_learning",
            
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      "url": 
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      "url": 
            
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ce/f87a6ccb-aa38-42e0-bb4f-8f37c7cfe8c7/download/sme2_smdh_data.xlsx",
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ce/f87a6ccb-aa38-42e0-bb4f-8f37c7cfe8c7/download/sme2_smdh_data.xlsx",
            
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nomous-manufacturing-ltd/smdh-amfg-predictive-tooling-maintenance/SME3 
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nomous-manufacturing-ltd/smdh-amfg-predictive-tooling-maintenance/SME3 
            
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Tooling Data",
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Tooling Data",
            
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      "created": "2025-02-27T12:08:47.094294",
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      "datastore_active": false,
            
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      "description": "Tooling and part failure rate for SME3 ",
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      "description": "Tooling and part failure rate for SME3 ",
            
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      "format": "XLSX",
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      "format": "XLSX",
            
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      "name": "SME3 Tooling Data",
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      "name": "SME3 Tooling Data",
            
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      "package_id": "b21a9006-60f7-4a45-ba1b-cbb5e99d9251",
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      "state": "active",
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      "state": "active",
            
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      "tags": "Method-Machine_learning",
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      "tags": "Method-Machine_learning",
            
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      "url": 
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      "url": 
            
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91e645df-a6c6-471a-934f-baa3c23de09f/download/sme3-tooling-data.xlsx",
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91e645df-a6c6-471a-934f-baa3c23de09f/download/sme3-tooling-data.xlsx",
            
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      "url_type": "upload"
            
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      "created": "2025-02-28T10:05:41.870730",
            
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      "datastore_active": false,
            
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      "description": "Tooling data for SME4 including part level data, 
            
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machine level data and tool level data, resulting in a part pass / 
            
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fail based on tool life remaining compared to maintenance 
            
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prediction.",
            
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  "state": "active",
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  "state": "active",
            
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  "tags": [
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  "tags": [
            
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      "name": "Method-Machine_learning",
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    {
            
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      "name": "Method-Machine_learning",
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      "name": "Method-Machine_learning",
            
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      "vocabulary_id": "55a807af-3944-4bf2-b2d8-585c8b1b8e04"
            
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    },
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    },
            
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    {
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    {
            
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      "name": "Method-Machine_learning",
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      "name": "Method-Machine_learning",
            
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      "vocabulary_id": "55a807af-3944-4bf2-b2d8-585c8b1b8e04"
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      "vocabulary_id": "55a807af-3944-4bf2-b2d8-585c8b1b8e04"
            
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    },
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    },
            
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    {
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    {
            
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      "name": "Method-Machine_learning",
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      "name": "Method-Machine_learning",
            
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      "vocabulary_id": "55a807af-3944-4bf2-b2d8-585c8b1b8e04"
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      "vocabulary_id": "55a807af-3944-4bf2-b2d8-585c8b1b8e04"
            
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    },
            
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    {
            
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      "name": "Method-Machine_learning",
            
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      "vocabulary_id": "55a807af-3944-4bf2-b2d8-585c8b1b8e04"
            
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    }
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    }
            
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  ],
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  ],
            
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  "title": "SMDH / AMFG predictive tooling maintenance",
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  "title": "SMDH / AMFG predictive tooling maintenance",
            
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  "type": "dataset",
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  "type": "dataset",
            
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  "url": "",
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  "url": "",
            
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  "version": "1.0"
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  "version": "1.0"
            
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}
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}