Browsing by Author "FathEl-Bab, Ahmed M. R."
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- Design and experimental testing of a tactile sensor for self-compensation of contact error in soft tissue stiffness measurementErukainure, Frank Efe; Parque, Victor; Hassan, Mohsen A.; FathEl-Bab, Ahmed M. R. (Korean Society for Mechanical Engineers, 2022-10)The measurement of viscoelastic properties of soft tissues has become a research interest with applications in the stiffness estimation of soft tissues, sorting and quality control of postharvest fruit, and fruit ripeness estimation. This paper presents a tactile sensor configuration to estimate the stiffness properties of soft tissues, using fruit as case study. Previous stiffness-measuring tactile sensor models suffer from unstable and infinite sensor outputs due to irregularities and inclination angles of soft tissue surfaces. The proposed configuration introduces two low stiffness springs at the extreme ends of the sensor with one high stiffness spring in-between. This study also presents a closed form mathematical model that considers the maximum inclination angle of the tissue’s (fruit) surface, and a finite element analysis to verify the mathematical model, which yielded stable sensor outputs. A prototype of the proposed configuration was fabricated and tested on kiwifruit samples. The experimental tests revealed that the sensor’s output remained stable, finite, and independent on both the inclination angle of the fruit surface and applied displacement of the sensor. The sensor distinguished between kiwifruit at various stiffness and ripeness levels with an output error ranging between 0.18 % and 3.50 %, and a maximum accuracy of 99.81 %, which is reasonable and competitive compared to previous design concepts.
- Estimating the stiffness of kiwifruit based on the fusion of instantaneous tactile sensor data and machine learning schemesErukainure, Frank Efe; Parque, Victor; Hassan, M. A.; FathEl-Bab, Ahmed M. R. (Elsevier, 2022-10)Measuring the ripeness of fruits is one of the critical factors in achieving real-time quality control and sorting of fruit by growers and postharvest managers. However, recent tactile sensing approaches for fruit ripeness detection have suffered setbacks due to: (1) the nonlinear relationship between the sensor output and the true stiffness of fruits; and (2) the angle of contact, referred to as the inclination angle, between the sensor and the outer surface of the fruit. In this paper, we propose a non-destructive tactile sensing approach for estimating the stiffness of fruits, using kiwifruit as a case study. Our sensor configuration is based on a three-probe piezoresistive cantilever beam, allowing us to obtain relatively stable sensor outputs that are independent of the inclination angle of the fruit surface. Our stiffness estimation approach is based on the combination of instantaneous sensor outputs with 63 regression-based machine learning models comprising of neural networks, Gaussian process, support vector machines, and decision trees. For experiments, we used several kiwifruit samples at diverse ripeness levels. The extracted sensor data was used to train the learning models over a 10-fold cross-validation technique, allowing us to find the nonlinear relationships between the instantaneous sensor outputs and the ground truth stiffness of the fruit. Our pairwise statistical comparison by the Wilcoxon test at 5% significance revealed the competitive performance frontiers of our approach for stiffness prediction; the Gaussian process kernel functions and the binary trees outperformed other models at a mean squared error (MSE) of 1.0 and 2×10−23, respectively. Most neural network models achieved competitive learning performance at MSE less than 10−5 and the utmost performance being a pyramidal class of feed-forward neural architectures. The results portray the potential of achieving accurate ripeness estimation of fruit using intelligent tactile sensors with fast machine learning schemes across the supply chain.