Hybrid online surface roughness measurement using a robotic arm
Surface roughness is an important property in the engineering field. It is often used to determine the availability and function of finished parts in both assembly and machinery. However, commercial machines available currently for roughness distinguishing require high maintenance cost and are usual...
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| Format: | Thesis |
| Language: | English |
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2017
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| Online Access: | http://hdl.handle.net/10356/69855 |
| _version_ | 1826129413977145344 |
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| author | Qin, Qin |
| author2 | Zhang Yilei |
| author_facet | Zhang Yilei Qin, Qin |
| author_sort | Qin, Qin |
| collection | NTU |
| description | Surface roughness is an important property in the engineering field. It is often used to determine the availability and function of finished parts in both assembly and machinery. However, commercial machines available currently for roughness distinguishing require high maintenance cost and are usually not precise enough. The objective of this project is to create a durable, effective and precise sensor useable in roughness discrimination. With the development of technology and science in the recent decades roughness testing has been brought to a whole new degree, especially the application concerning robots in the area of high technology such as medical, automobiles and semiconductor industries. Robot is known to use a technique called tactile sensing to distinguish the shape, texture or roughness of an object. Recent researches indicate that tactile sensors are used to simulate functions of a human finger in robotic finger. Tactile sensors are used to imitate the human mechanoreceptors which are divided into the Fast Adapting (FA) and Slow Adapting (SA). In this project, an artificial finger with piezoresistive sensors used to represent the SA mechanoreceptors and piezoelectric sensors used to represent the FA mechanoreceptors is used. Besides, a commercial optical sensor for roughness testing is also used in this project. The sensors adapted are able to produce signals and the signals generated will be processed and analyzed to evaluate the effectiveness of the two sensors in surface roughness testing. |
| first_indexed | 2024-10-01T07:40:12Z |
| format | Thesis |
| id | ntu-10356/69855 |
| institution | Nanyang Technological University |
| language | English |
| last_indexed | 2024-10-01T07:40:12Z |
| publishDate | 2017 |
| record_format | dspace |
| spelling | ntu-10356/698552023-03-11T16:52:48Z Hybrid online surface roughness measurement using a robotic arm Qin, Qin Zhang Yilei School of Mechanical and Aerospace Engineering DRNTU::Engineering::Mechanical engineering Surface roughness is an important property in the engineering field. It is often used to determine the availability and function of finished parts in both assembly and machinery. However, commercial machines available currently for roughness distinguishing require high maintenance cost and are usually not precise enough. The objective of this project is to create a durable, effective and precise sensor useable in roughness discrimination. With the development of technology and science in the recent decades roughness testing has been brought to a whole new degree, especially the application concerning robots in the area of high technology such as medical, automobiles and semiconductor industries. Robot is known to use a technique called tactile sensing to distinguish the shape, texture or roughness of an object. Recent researches indicate that tactile sensors are used to simulate functions of a human finger in robotic finger. Tactile sensors are used to imitate the human mechanoreceptors which are divided into the Fast Adapting (FA) and Slow Adapting (SA). In this project, an artificial finger with piezoresistive sensors used to represent the SA mechanoreceptors and piezoelectric sensors used to represent the FA mechanoreceptors is used. Besides, a commercial optical sensor for roughness testing is also used in this project. The sensors adapted are able to produce signals and the signals generated will be processed and analyzed to evaluate the effectiveness of the two sensors in surface roughness testing. Master of Science (Precision Engineering) 2017-03-30T06:28:54Z 2017-03-30T06:28:54Z 2017 Thesis http://hdl.handle.net/10356/69855 en 75 p. application/pdf |
| spellingShingle | DRNTU::Engineering::Mechanical engineering Qin, Qin Hybrid online surface roughness measurement using a robotic arm |
| title | Hybrid online surface roughness measurement using a robotic arm |
| title_full | Hybrid online surface roughness measurement using a robotic arm |
| title_fullStr | Hybrid online surface roughness measurement using a robotic arm |
| title_full_unstemmed | Hybrid online surface roughness measurement using a robotic arm |
| title_short | Hybrid online surface roughness measurement using a robotic arm |
| title_sort | hybrid online surface roughness measurement using a robotic arm |
| topic | DRNTU::Engineering::Mechanical engineering |
| url | http://hdl.handle.net/10356/69855 |
| work_keys_str_mv | AT qinqin hybridonlinesurfaceroughnessmeasurementusingaroboticarm |