A platform for research: civil engineering, architecture and urbanism
A platform for research: civil engineering, architecture and urbanism
Optimization of uncertainty in hole diameter measurements using a novel approach of ANN-regression-WASPAS
https://orcid.org/http://orcid.org/0000-0003-1854-2767
https://orcid.org/http://orcid.org/0000-0001-7239-625X
The component’s measurement is a step in the manufacturing process where the product’s quality is significantly impacted by measurement uncertainty factors like operator skill, the number of measuring points, and the number of samples. To minimize the effects of measurement uncertainties, proper training, measuring instrument calibration, and standardized procedures are important. This work introduces a novel methodology ‘ANN-Regression-WASPAS’ used for estimating the uncertainty in hole diameter measurements. To measure the hole diameters, an experiment was designed using a Taguchi L27 orthogonal array. The ANN model was used for predicting the variations in hole diameter measurement. Further to this, a regression model was used to define the relationships between predicted values, actual values, and input factors. To mitigate measurement uncertainty, an estimated matrix was constructed by identifying the minimum values between the actual hole diameters and predicted hole diameters. The WASPAS method was used to optimize the obtained estimated matrix, and its Taguchi analysis was utilized for further confirmation. The experimental findings showed that the ‘ANN-Regression-WASPAS' method performed better than the traditional WASPAS approach using actual measured data, leading to a reduction of about − 1.67% in the uncertainty of hole diameters. Furthermore, the ANN-Regression approach decreased the percentage uncertainty of the actual measured data by − 5.62%. Finally, using the proposed approach, the uncertainty in hole diameter measurements was estimated to be 0.74%, which was regarded as satisfactory. The proposed methodology offers benefits to metrology researchers, quality control engineers, manufacturing engineers, design engineers, and optimization experts.
Optimization of uncertainty in hole diameter measurements using a novel approach of ANN-regression-WASPAS
https://orcid.org/http://orcid.org/0000-0003-1854-2767
https://orcid.org/http://orcid.org/0000-0001-7239-625X
The component’s measurement is a step in the manufacturing process where the product’s quality is significantly impacted by measurement uncertainty factors like operator skill, the number of measuring points, and the number of samples. To minimize the effects of measurement uncertainties, proper training, measuring instrument calibration, and standardized procedures are important. This work introduces a novel methodology ‘ANN-Regression-WASPAS’ used for estimating the uncertainty in hole diameter measurements. To measure the hole diameters, an experiment was designed using a Taguchi L27 orthogonal array. The ANN model was used for predicting the variations in hole diameter measurement. Further to this, a regression model was used to define the relationships between predicted values, actual values, and input factors. To mitigate measurement uncertainty, an estimated matrix was constructed by identifying the minimum values between the actual hole diameters and predicted hole diameters. The WASPAS method was used to optimize the obtained estimated matrix, and its Taguchi analysis was utilized for further confirmation. The experimental findings showed that the ‘ANN-Regression-WASPAS' method performed better than the traditional WASPAS approach using actual measured data, leading to a reduction of about − 1.67% in the uncertainty of hole diameters. Furthermore, the ANN-Regression approach decreased the percentage uncertainty of the actual measured data by − 5.62%. Finally, using the proposed approach, the uncertainty in hole diameter measurements was estimated to be 0.74%, which was regarded as satisfactory. The proposed methodology offers benefits to metrology researchers, quality control engineers, manufacturing engineers, design engineers, and optimization experts.
Optimization of uncertainty in hole diameter measurements using a novel approach of ANN-regression-WASPAS
https://orcid.org/http://orcid.org/0000-0003-1854-2767
https://orcid.org/http://orcid.org/0000-0001-7239-625X
The component’s measurement is a step in the manufacturing process where the product’s quality is significantly impacted by measurement uncertainty factors like operator skill, the number of measuring points, and the number of samples. To minimize the effects of measurement uncertainties, proper training, measuring instrument calibration, and standardized procedures are important. This work introduces a novel methodology ‘ANN-Regression-WASPAS’ used for estimating the uncertainty in hole diameter measurements. To measure the hole diameters, an experiment was designed using a Taguchi L27 orthogonal array. The ANN model was used for predicting the variations in hole diameter measurement. Further to this, a regression model was used to define the relationships between predicted values, actual values, and input factors. To mitigate measurement uncertainty, an estimated matrix was constructed by identifying the minimum values between the actual hole diameters and predicted hole diameters. The WASPAS method was used to optimize the obtained estimated matrix, and its Taguchi analysis was utilized for further confirmation. The experimental findings showed that the ‘ANN-Regression-WASPAS' method performed better than the traditional WASPAS approach using actual measured data, leading to a reduction of about − 1.67% in the uncertainty of hole diameters. Furthermore, the ANN-Regression approach decreased the percentage uncertainty of the actual measured data by − 5.62%. Finally, using the proposed approach, the uncertainty in hole diameter measurements was estimated to be 0.74%, which was regarded as satisfactory. The proposed methodology offers benefits to metrology researchers, quality control engineers, manufacturing engineers, design engineers, and optimization experts.
Optimization of uncertainty in hole diameter measurements using a novel approach of ANN-regression-WASPAS
Int J Interact Des Manuf
Zende, Rohit (author) / Pawade, Raju (author)
2025-03-01
22 pages
Article (Journal)
Electronic Resource
English