ManufacturingNet: A machine learning toolbox for engineers
The growing deployability of artificial intelligence (AI), accessibility to large amounts of data and new computing technologies are causing a disruption in the manufacturing industry. Artificial Intelligence tools need a considerable amount of programming knowledge and, thus, remain obscure to engi...
| Main Authors: | , , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Elsevier
2023-07-01
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| Series: | SoftwareX |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2352711023001747 |
| _version_ | 1797679467393449984 |
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| author | Akshay Antony Chakradhar Guntuboina Rishikesh Magar Lalit Ghule Ruchit Doshi Aman Khalid Sharan Seshadri Amir Barati Farimani |
| author_facet | Akshay Antony Chakradhar Guntuboina Rishikesh Magar Lalit Ghule Ruchit Doshi Aman Khalid Sharan Seshadri Amir Barati Farimani |
| author_sort | Akshay Antony |
| collection | DOAJ |
| description | The growing deployability of artificial intelligence (AI), accessibility to large amounts of data and new computing technologies are causing a disruption in the manufacturing industry. Artificial Intelligence tools need a considerable amount of programming knowledge and, thus, remain obscure to engineers inexperienced with programming. To overcome these barriers, we propose ManufacturingNet, an open-source machine learning tool that enables engineers to develop complex machine learning and deep learning models with minimal programming and data science experience. We have also curated nine publicly-available datasets and benchmarked their performance. We believe ManufacturingNet will enable engineers around the world to develop machine learning models with ease, contributing towards the larger movement of the 4th industrial revolution. |
| first_indexed | 2024-03-11T23:15:06Z |
| format | Article |
| id | doaj.art-ff0c1542205e4ca6a05f4941d2413b46 |
| institution | Directory Open Access Journal |
| issn | 2352-7110 |
| language | English |
| last_indexed | 2024-03-11T23:15:06Z |
| publishDate | 2023-07-01 |
| publisher | Elsevier |
| record_format | Article |
| series | SoftwareX |
| spelling | doaj.art-ff0c1542205e4ca6a05f4941d2413b462023-09-21T04:37:37ZengElsevierSoftwareX2352-71102023-07-0123101478ManufacturingNet: A machine learning toolbox for engineersAkshay Antony0Chakradhar Guntuboina1Rishikesh Magar2Lalit Ghule3Ruchit Doshi4Aman Khalid5Sharan Seshadri6Amir Barati Farimani7Department of Mechanical Engineering, Carnegie Mellon University, Pittsburgh, PA 15213, USADepartment of Electrical and Computer Engineering, Carnegie Mellon University, Pittsburgh, PA 15213, USADepartment of Mechanical Engineering, Carnegie Mellon University, Pittsburgh, PA 15213, USADepartment of Mechanical Engineering, Carnegie Mellon University, Pittsburgh, PA 15213, USADepartment of Mechanical Engineering, Carnegie Mellon University, Pittsburgh, PA 15213, USACollege of Literature, Science, and the Arts, University of Michigan, Ann Arbor, MI 48109, USADepartment of Electrical and Computer Engineering, Carnegie Mellon University, Pittsburgh, PA 15213, USADepartment of Mechanical Engineering, Carnegie Mellon University, Pittsburgh, PA 15213, USA; Corresponding author.The growing deployability of artificial intelligence (AI), accessibility to large amounts of data and new computing technologies are causing a disruption in the manufacturing industry. Artificial Intelligence tools need a considerable amount of programming knowledge and, thus, remain obscure to engineers inexperienced with programming. To overcome these barriers, we propose ManufacturingNet, an open-source machine learning tool that enables engineers to develop complex machine learning and deep learning models with minimal programming and data science experience. We have also curated nine publicly-available datasets and benchmarked their performance. We believe ManufacturingNet will enable engineers around the world to develop machine learning models with ease, contributing towards the larger movement of the 4th industrial revolution.http://www.sciencedirect.com/science/article/pii/S2352711023001747Artificial intelligenceMachine learningDeep learning |
| spellingShingle | Akshay Antony Chakradhar Guntuboina Rishikesh Magar Lalit Ghule Ruchit Doshi Aman Khalid Sharan Seshadri Amir Barati Farimani ManufacturingNet: A machine learning toolbox for engineers SoftwareX Artificial intelligence Machine learning Deep learning |
| title | ManufacturingNet: A machine learning toolbox for engineers |
| title_full | ManufacturingNet: A machine learning toolbox for engineers |
| title_fullStr | ManufacturingNet: A machine learning toolbox for engineers |
| title_full_unstemmed | ManufacturingNet: A machine learning toolbox for engineers |
| title_short | ManufacturingNet: A machine learning toolbox for engineers |
| title_sort | manufacturingnet a machine learning toolbox for engineers |
| topic | Artificial intelligence Machine learning Deep learning |
| url | http://www.sciencedirect.com/science/article/pii/S2352711023001747 |
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