Step and step-nc as a tool for big data in cloud manufacturing
The terms big data, cloud manufacturing, predictive and additive manufacturing, and Internet of Things (IoT) are being most commonly used in the manufacturing industry nowadays. These terms are related to the fourth industrial revolution that emphasizes automation and data exchange between...
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| Main Authors: | , , , , , , , , , , , , |
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| Format: | Article |
| Language: | en |
| Published: |
Penerbit UTHM
2021
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| Subjects: | |
| Online Access: | https://umpir.ump.edu.my/id/eprint/46362/1/Step%20and%20step-nc%20as%20a%20tool%20for%20big%20data%20in%20cloud%20manufacturing.pdf https://doi.org/10.30880/ijie.2021.13.02.023 https://umpir.ump.edu.my/id/eprint/46362/ |
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| Summary: | The terms big data, cloud manufacturing, predictive and additive manufacturing, and Internet of Things (IoT) are being most commonly used in the manufacturing industry nowadays. These terms are related to the fourth industrial revolution that emphasizes automation and data exchange between manufacturing tools/elements. Communication occurs between machines, products and even technicians or operators through various technologies while creating records of each interaction resulting in rapid growth of amount of data to be stored. Data acquisition is not a major issue since a structure or framework can properly connect these data in improving manufacturing efficiency. However, lack of effort in collecting and storing manufacturing data in the whole product life cycle process has made integration to be almost difficult to achieve. In this study, the adoption of STEP-NC method/technique was demonstrated in suiting the current explosion of big data in the industrial and manufacturing sector. The proposed methodology was developed through a study of an entity file structure and hierarchical concept in STEP and STEP-NC in gathering manufacturing data in a unified database. The challenge would be in making sense of the data, revealing the patterns in it and using them for operational improvements. The outcome of this study will be useful to support strategic decision making in product manufacturing. |
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