Non-contact approach of roundness inspection for machined parts / Azmi Mohamed
The geometrical tolerance verification of machined part is a process composed of a set of inspection procedures and rules that are complex, tedious and slow. The methods and instruments used to inspect geometrical tolerance of the parts are quite conventional and require a high skill and knowledge t...
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my-uitm-ir.164052022-04-13T01:36:32Z Non-contact approach of roundness inspection for machined parts / Azmi Mohamed 2013 Mohamed, Azmi The geometrical tolerance verification of machined part is a process composed of a set of inspection procedures and rules that are complex, tedious and slow. The methods and instruments used to inspect geometrical tolerance of the parts are quite conventional and require a high skill and knowledge to assess the quality of the machined parts. For this reason, this research develop a method to effectively perform the inspection process by recommending non-contact approach using machine vision and new simple mathematical models that can be used for the creation of an inspection system to assist in the verification of an important form tolerance of machined parts. The main goal of this research is to develop method and procedure of roundness measurement that are simple to implement but at the same time is fast and effective to provide reliable technique that help the metrologist to make evaluation for the inspected parts. Two samples of cylindrical machined parts are selected to be measured by this non-contact approach. A test-rig set-up which consists of main components such as workholding fixture, CCD camera, lighting device and motor was developed in order to carry out this study. This research proposes new procedure in image processing by using WiT software. In addition, a new mathematical model for evaluation of roundness error is proposed according to the analogy given by Minimum Zone Circle (MZC) method. The proposed approach and mathematical models were analyzed using several set of number of part images. The results showed that the noncontact inspection system for roundness error were effective and reliable enough to assess this form tolerance. This concept of measurement can be further improved to obtain better accuracy of the roundness error assessment. In summary, this research suggests a new method for geometrical tolerance inspection for machined parts by using machine vision. This system provides flexibility in term of the inspection set-up and is potentially applied for in-line and hundred percent (100%) inspection of the cylindrical machined parts. 2013 Thesis https://ir.uitm.edu.my/id/eprint/16405/ https://ir.uitm.edu.my/id/eprint/16405/1/16405.pdf text en public phd doctoral Universiti Teknologi MARA Faculty of Mechanical Engineering |
| institution |
Universiti Teknologi MARA |
| collection |
UiTM Institutional Repository |
| language |
English |
| description |
The geometrical tolerance verification of machined part is a process composed of a set of inspection procedures and rules that are complex, tedious and slow. The methods and instruments used to inspect geometrical tolerance of the parts are quite conventional and require a high skill and knowledge to assess the quality of the machined parts. For this reason, this research develop a method to effectively perform the inspection process by recommending non-contact approach using machine vision and new simple mathematical models that can be used for the creation of an inspection system to assist in the verification of an important form tolerance of machined parts. The main goal of this research is to develop method and procedure of roundness measurement that are simple to implement but at the same time is fast and effective to provide reliable technique that help the metrologist to make evaluation for the inspected parts. Two samples of cylindrical machined parts are selected to be measured by this non-contact approach. A test-rig set-up which consists of main components such as workholding fixture, CCD camera, lighting device and motor was developed in order to carry out this study. This research proposes new procedure in image processing by using WiT software. In addition, a new mathematical model for evaluation of roundness error is proposed according to the analogy given by Minimum Zone Circle (MZC) method. The proposed approach and mathematical models were analyzed using several set of number of part images. The results showed that the noncontact inspection system for roundness error were effective and reliable enough to assess this form tolerance. This concept of measurement can be further improved to obtain better accuracy of the roundness error assessment. In summary, this research suggests a new method for geometrical tolerance inspection for machined parts by using machine vision. This system provides flexibility in term of the inspection set-up and is potentially applied for in-line and hundred percent (100%) inspection of the cylindrical machined parts. |
| format |
Thesis |
| qualification_name |
Doctor of Philosophy (PhD.) |
| qualification_level |
Doctorate |
| author |
Mohamed, Azmi |
| spellingShingle |
Mohamed, Azmi Non-contact approach of roundness inspection for machined parts / Azmi Mohamed |
| author_facet |
Mohamed, Azmi |
| author_sort |
Mohamed, Azmi |
| title |
Non-contact approach of roundness inspection for machined parts / Azmi Mohamed |
| title_short |
Non-contact approach of roundness inspection for machined parts / Azmi Mohamed |
| title_full |
Non-contact approach of roundness inspection for machined parts / Azmi Mohamed |
| title_fullStr |
Non-contact approach of roundness inspection for machined parts / Azmi Mohamed |
| title_full_unstemmed |
Non-contact approach of roundness inspection for machined parts / Azmi Mohamed |
| title_sort |
non-contact approach of roundness inspection for machined parts / azmi mohamed |
| granting_institution |
Universiti Teknologi MARA |
| granting_department |
Faculty of Mechanical Engineering |
| publishDate |
2013 |
| url |
https://ir.uitm.edu.my/id/eprint/16405/1/16405.pdf |
| _version_ |
1783733523397476352 |