Development of smart machining
Smart Machining has been extremely popular in the manufacturing industry since its debut. This caused companies to leverage on technology capabilities, leading to the automation and intelligence known as Industry 4.0. The Mazak CNC QuickTurn 250 machine was equipped with sensors which record data su...
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| Format: | Final Year Project (FYP) |
| Language: | English |
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Nanyang Technological University
2020
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| Online Access: | https://hdl.handle.net/10356/141412 |
| _version_ | 1811677481687580672 |
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| author | Seah, Yee Loong |
| author2 | Yeo Swee Hock |
| author_facet | Yeo Swee Hock Seah, Yee Loong |
| author_sort | Seah, Yee Loong |
| collection | NTU |
| description | Smart Machining has been extremely popular in the manufacturing industry since its debut. This caused companies to leverage on technology capabilities, leading to the automation and intelligence known as Industry 4.0. The Mazak CNC QuickTurn 250 machine was equipped with sensors which record data such as the Tri-Axial Cutting Force, Tri-Axial Acceleration, Cutting Temperature, Coolant Pressure, Power, and Acoustic Emission. Aluminium was used as the main material for cutting experiments and the relationship between the variables was studied. This can be further expanded to cover different working materials with minimal modifications. In addition to the sensor data, the CNC machine provided data such as the feed rate and cutting speed. Surface roughness readings were also recorded using a surface roughness tester and through experiments, it was proven that this is affected by the cutting speed, feed rate, and coolant pressure. Data analytics and Machine Learning were subsequently done to generate a regression model that was able to predict the cutting force and surface roughness based on the dependent variables. Additionally, Decision Trees, Supported Vector Machine, and Neural Networks algorithms were built, which could classify between a sharp and worn cutting tool up to a 90% accuracy. |
| first_indexed | 2024-10-01T02:38:03Z |
| format | Final Year Project (FYP) |
| id | ntu-10356/141412 |
| institution | Nanyang Technological University |
| language | English |
| last_indexed | 2024-10-01T02:38:03Z |
| publishDate | 2020 |
| publisher | Nanyang Technological University |
| record_format | dspace |
| spelling | ntu-10356/1414122023-03-04T19:45:24Z Development of smart machining Seah, Yee Loong Yeo Swee Hock School of Mechanical and Aerospace Engineering MSHYEO@ntu.edu.sg Engineering::Manufacturing Engineering::Aeronautical engineering Smart Machining has been extremely popular in the manufacturing industry since its debut. This caused companies to leverage on technology capabilities, leading to the automation and intelligence known as Industry 4.0. The Mazak CNC QuickTurn 250 machine was equipped with sensors which record data such as the Tri-Axial Cutting Force, Tri-Axial Acceleration, Cutting Temperature, Coolant Pressure, Power, and Acoustic Emission. Aluminium was used as the main material for cutting experiments and the relationship between the variables was studied. This can be further expanded to cover different working materials with minimal modifications. In addition to the sensor data, the CNC machine provided data such as the feed rate and cutting speed. Surface roughness readings were also recorded using a surface roughness tester and through experiments, it was proven that this is affected by the cutting speed, feed rate, and coolant pressure. Data analytics and Machine Learning were subsequently done to generate a regression model that was able to predict the cutting force and surface roughness based on the dependent variables. Additionally, Decision Trees, Supported Vector Machine, and Neural Networks algorithms were built, which could classify between a sharp and worn cutting tool up to a 90% accuracy. Bachelor of Engineering (Aerospace Engineering) 2020-06-08T06:20:42Z 2020-06-08T06:20:42Z 2020 Final Year Project (FYP) https://hdl.handle.net/10356/141412 en C042 application/pdf Nanyang Technological University |
| spellingShingle | Engineering::Manufacturing Engineering::Aeronautical engineering Seah, Yee Loong Development of smart machining |
| title | Development of smart machining |
| title_full | Development of smart machining |
| title_fullStr | Development of smart machining |
| title_full_unstemmed | Development of smart machining |
| title_short | Development of smart machining |
| title_sort | development of smart machining |
| topic | Engineering::Manufacturing Engineering::Aeronautical engineering |
| url | https://hdl.handle.net/10356/141412 |
| work_keys_str_mv | AT seahyeeloong developmentofsmartmachining |