Construction of fuzzy control charts by using triangular and gaussian fuzzy numbers for solder paste thickness
Control chart is one of the seven problem solving tools in Statistical Process Control (SPC) and become a very popular technique in improving productivity, preventing defects and avoid purposeless process adjustment. Real data or problems nowadays are too complicated to handle and the difficulty inv...
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| Format: | Thesis |
| Language: | English English English |
| Published: |
2018
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| Subjects: | |
| Online Access: | http://eprints.uthm.edu.my/321/1/24p%20NUR%20AIN%20ZAFIRAH%20AHMAD%20BASRI.pdf http://eprints.uthm.edu.my/321/2/NUR%20AIN%20ZAFIRAH%20AHMAD%20BASRI%20COPYRIGHT%20DECLARATION.pdf http://eprints.uthm.edu.my/321/3/NUR%20AIN%20ZAFIRAH%20AHMAD%20BASRI%20WATERMARK.pdf |
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| Summary: | Control chart is one of the seven problem solving tools in Statistical Process Control (SPC) and become a very popular technique in improving productivity, preventing defects and avoid purposeless process adjustment. Real data or problems nowadays are too complicated to handle and the difficulty involves with the level of uncertainty which might come from human, measurement devices or environmental conditions. This study aims to generate fuzzy numbers by using triangular and Gaussian approaches and to analyse the algorithm of fuzzy control charts by using α-cut and to analyse the algorithm of traditional control charts of -R and -S towards the solder paste thickness of integrated circuit data. The fuzzy numbers were generated by using random number between 0 to 1.2% for each observation. Next, performance of these control charts are compared by using average run length (ARL) to select the best chart to control the production process. Results showed that the new fuzzy control charts by using Gaussian fuzzy numbers are the best chart in monitoring the solder paste thickness showed by the lowest value of ARL compared to fuzzy control charts by using triangular fuzzy numbers and traditional control charts. Therefore, this fuzzy control charts by using Gaussian fuzzy numbers can be used to monitor the quality of a product. |
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