A Study on the Effect of Radiometric variations on a Fuzzy Stereo matching algorithm: A Statistical Analysis

Shetty, Akhil; Hegde, Navya Thirumaleshwar; Vaz, Aldrin Claytus; Srinivasan, C R

doi:10.30919/es8d568

Cited by 4 publications

(3 citation statements)

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“…Taguchi Design of Experiments has been used widely in engineering applications to achieve best levels of a quality characteristic under different conditions. Taguchi's approach to design of experiments is easy to adopt and apply for users with limited knowledge of statistics which resulted wide popularity in engineering and scientific community [31][32][33]. In this experimental layout, S/N ratio characteristic 'smaller the better' for has been adopted as given in the equation (1).…”

Section: Taguchi's Design Of Experimentsmentioning

confidence: 99%

Design of experiments integrated with neural networks for optimization and predictive modelling of electrode wear of novel Ti-6Al-4V-SiCp composites during die sinking electric discharge machining

Hegde,

Shetty,

Nayak

et al. 2024

Mater. Res. Express

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Electric Discharge Machining (EDM) is a widely used manufacturing process for shaping hard and electrically conductive materials. This study investigates the effects of various electrode materials such as, Ti-6Al-4V-SiCp, Brass and Copper on the machining performance of AISI 316L Stainless Steel workpieces using EDM. The methodology involved manipulating parameters such as Electrode Material, Discharge Current, Gap Voltage, Spark Gap, Pulse-on Time, and Pulse-off Time. From the extensive experimantation it was observed that the combination of Ti-6Al-4V-SiCp electrode material, 8Amp Discharge Current, 90V Gap Voltage, 75µm Spark Gap, 100µs Pulse-on Time and 15µs Pulse-off Time has resulted in lowest Electrode Wear Rate, Machining Time, and Electrode Surface Roughness Ratio. Ti-6Al-4V-SiCp electrodes possess higher hardness and electrical conductivity compared to Brass and Copper Electrodes leading to higher wear resistance against repeated thermal shocks during electric discharge machining operation. Feed Forward Artificial Neural Network is successfully applied to predict the output characteristics of the experimentation with high accuracy of 98.3% (Electrode Wear Rate), 94.6% (Machining Time) and 93.8% (Electrode Surface Roughness Ratio). Further, microstructure analysis concludes that lowest wear is observed in Ti-6Al-4V-SiCp electrodes compared to Brass and Copper electrodes.

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Section: Taguchi's Design Of Experimentsmentioning

confidence: 99%

Design of experiments integrated with neural networks for optimization and predictive modelling of electrode wear of novel Ti-6Al-4V-SiCp composites during die sinking electric discharge machining

Hegde,

Shetty,

Nayak

et al. 2024

Mater. Res. Express

View full text Add to dashboard Cite

show abstract

“…To study the manufacturing processes comprehensively, statistical techniques like Taguchi's design of experiments are employed. This statistical method involves the creation of mathematical models through experimental testing to predict potential outcomes based on input factors [42,43]. Analysis of variance (ANOVA) is employed to examine the contribution percentage of process input factors [44,45].…”

Section: Introductionmentioning

confidence: 99%

Comprehensive Investigation of Hardness, Wear and Frictional Force in Powder Metallurgy Engineered Ti-6Al-4V-SiCp Metal Matrix Composites

Hegde,

Nayak,

Bolar

et al. 2024

J. Compos. Sci.

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Metal matrix composites (MMCs) have achieved significant attention in engineering applications because of their exceptional properties, like increased strength-to-weight ratiosand resistance to wear. However, their manufacturing processes pose challenges for industries, such as oxidation, porosity, and chemical reactions. To address these challenges, this study investigates the processing and sintering (500 °C) of Ti-6Al-4V-SiCp composites and their mechanical properties, particularly hardness, wear and frictional force using a statistical approach. The main objective of this research is to identify optimal processing conditions for Ti-6Al-4V-SiCp composites that yield maximum hardness, minimal wear and frictional force. Thisstudy varies three key parameters, namely compaction pressure (Ton/sq.inch), SiC (wt.%), and PVA binder (wt.%) using Taguchi’s design of experiments (TDOE). Further, the response surface methodology (RSM) is used to develop second-order models to predict the output values under different processing conditions, by correlating with the values obtained from TDOE. The results indicate that the most significant influence on the output is exerted by SiC (wt.%), followed by PVA binder (wt.%) and compaction pressure (Ton/sq.inch). To achieve higher hardness with minimal wear and frictional force during processing, SiCp (15 wt.%), compaction pressure (4 Ton/sq.inch), and PVA binder (3 wt.%) arerecommended. Finally, microstructural analysis using (SEM) scanning electron microscope images, optical macrographs and (AFM) atomic force microscopy revealed that the inclusion of 15 wt.% SiCp resulted in improved hardness, wear and frictional force compared to 20 wt.% SiCp. In conclusion, this study provides valuable insights into optimizing the processing parameters of Ti-6Al-4V-SiCp samples, enabling the production of materials with enhanced hardness and wear resistance.

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“…This information is then provided to a path planning algorithm and implemented on a robot. The authors adopted feature-based matching algorithm over deep learning methodologies to overcome higher processing power [11][12][13]. In application which work on limited resources, this would prove to be quite a limitation.…”

Section: Introductionmentioning

confidence: 99%

Multi Cost Function Fuzzy Stereo Matching Algorithm for Object Detection and Robot Motion Control

Shetty,

Hegde,

Vaz

et al. 2023

Journal of Robotics and Control

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Stereo matching algorithms work with multiple images of a scene, taken from two viewpoints, to generate depth information. Authors usually use a single matching function to generate similarity between corresponding regions in the images. In the present research, the authors have considered a combination of multiple data costs for disparity generation. Disparity maps generated from stereo images tend to have noisy sections. The presented research work is related to a methodology to refine such disparity maps such that they can be further processed to detect obstacle regions. A novel entropy based selective refinement (ESR) technique is proposed to refine the initial disparity map. The information from both the left disparity and right disparity maps are used for this refinement technique. For every disparity map, block wise entropy is calculated. The average entropy values of the corresponding positions in the disparity maps are compared. If the variation between these entropy values exceeds a threshold, then the corresponding disparity value is replaced with the mean disparity of the block with lower entropy. The results of this refinement are compared with similar methods and was observed to be better. Furthermore, in this research work, the v-disparity values are used to highlight the road surface in the disparity map. The regions belonging to the sky are removed through HSV based segmentation. The remaining regions which are our ROIs, are refined through a u-disparity area-based technique. Based on this, the closest obstacles are detected through the use of k-means segmentation. The segmented regions are further refined through a u-disparity image information-based technique and used as masks to highlight obstacle regions in the disparity maps. This information is used in conjunction with a kalman filter based path planning algorithm to guide a mobile robot from a source location to a destination location while also avoiding any obstacle detected in its path. A stereo camera setup was built and the performance of the algorithm on local real-life images, captured through the cameras, was observed. The evaluation of the proposed methodologies was carried out using real life out door images obtained from KITTI dataset and images with radiometric variations from Middlebury stereo dataset.

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A Study on the Effect of Radiometric variations on a Fuzzy Stereo matching algorithm: A Statistical Analysis

Cited by 4 publications

References 0 publications

Design of experiments integrated with neural networks for optimization and predictive modelling of electrode wear of novel Ti-6Al-4V-SiCp composites during die sinking electric discharge machining

Design of experiments integrated with neural networks for optimization and predictive modelling of electrode wear of novel Ti-6Al-4V-SiCp composites during die sinking electric discharge machining

Comprehensive Investigation of Hardness, Wear and Frictional Force in Powder Metallurgy Engineered Ti-6Al-4V-SiCp Metal Matrix Composites

Multi Cost Function Fuzzy Stereo Matching Algorithm for Object Detection and Robot Motion Control

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