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Journal= JoPC Volume= 11 Issue= 06
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Journal= JoPC Volume= 11 Issue= 06
The increased adaptability and usefulness of glass-fiber reinforced polymer has led to its widespread adoption. GFRP laminates can be improved by including filler materials to improve their already impressive set of qualities. The demand for the research and development of advanced composites with enhanced properties has never been greater, and this is especially true for composites that are lightweight but have enhanced tensile and flexural qualities. An example of a laminate lay-up is one in which the lamina plies are stacked at acute angles to one another. Laminates made from continuous fibers are often arranged so that their strength is maximized along the direction of most major stress. In order to improve the GFRP composite laminate’s strength and mechanical qualities, we are including graphene into our dissertation at varying percentages. The GFRP laminate will be put through a battery of ASTM-mandated tensile and flexural tests subsequently. Standardized procedures for tensile and flexural testing are used to establish ASTM guidelines. These tensile and flexural properties are used to examine the impact of graphene addition. Using this method, we can determine whether or not graphene powder can enhance the GFRP laminates’ mechanical qualities.
Keywords: Glass fibre reinforced polymer, GFRP laminates, lamina plies, tensile, flexural properties. Graphene powderTo improve the robot’s welding quality, control welding precision, optimize welding parameters, realize continuous welding quality database optimization, and increase welding defect detection, a fuzzy neural network-based intelligent decision-making system must be built. This study demonstrates how fuzzy control theory and BP neural networks may be used to identify welding issues and enhance process variables. The experimental findings indicate that, with seam classification accuracy close to 90%, enhancing welding parameters and performance is feasible and achievable. Neural networks, fuzzy controls, and expert systems are all used to describe welding process control. Utilizing intelligent welding controllers. Systems with intelligence can identify surface, volume, and linear welding faults. morphological (pits, undercuts, welds, surface cracks), volumetric (voids), or linear (such as slag, partial penetration, unfused, and fractures). Destructive testing benefits from data storage and visibility. Design and demand changes complicate production decisions. AI approaches like neural networks make online decisions. This article uses neural networks to predict SAW weld quality (SAW). Taguchi’s principles are applied in experiments and equations. The use of experimental data and regression analysis in the development of neural network models. Considering the relative speeds and precisions of the models. NNPSO performs better than BPNN, RBFNN, and GA combined (NNGA). The created weld quality prediction technique contains online monitoring, and it is customizable, competent, and accurate. Additionally, the scheme is competent. Models are tested. The strategy that has been presented and developed will assist people in protecting themselves from robot risks.
Keywords: Welding quality, intelligent decision-making system, welding quality, welding precision, optimized welding parameters, fuzzy neural networkThe increased adaptability and usefulness of glass-fiber reinforced polymer has led to its widespread adoption. GFRP laminates can be improved by including filler materials to improve their already impressive set of qualities. The demand for the research and development of advanced composites with enhanced properties has never been greater, and this is especially true for composites that are lightweight but have enhanced tensile and flexural qualities. An example of a laminate lay-up is one in which the lamina plies are stacked at acute angles to one another. Laminates made from continuous fibers are often arranged so that their strength is maximized along the direction of most major stress. In order to improve the GFRP composite laminate’s strength and mechanical qualities, we are including graphene into our dissertation at varying percentages. The GFRP laminate will be put through a battery of ASTM-mandated tensile and flexural tests subsequently. Standardized procedures for tensile and flexural testing are used to establish ASTM guidelines. These tensile and flexural properties are used to examine the impact of graphene addition. Using this method, we can determine whether or not graphene powder can enhance the GFRP laminates’ mechanical qualities.
Keywords: Glass fibre reinforced polymer, GFRP laminates, lamina plies, tensile, flexural properties. Graphene powderTo improve the robot’s welding quality, control welding precision, optimize welding parameters, realize continuous welding quality database optimization, and increase welding defect detection, a fuzzy neural network-based intelligent decision-making system must be built. This study demonstrates how fuzzy control theory and BP neural networks may be used to identify welding issues and enhance process variables. The experimental findings indicate that, with seam classification accuracy close to 90%, enhancing welding parameters and performance is feasible and achievable. Neural networks, fuzzy controls, and expert systems are all used to describe welding process control. Utilizing intelligent welding controllers. Systems with intelligence can identify surface, volume, and linear welding faults. morphological (pits, undercuts, welds, surface cracks), volumetric (voids), or linear (such as slag, partial penetration, unfused, and fractures). Destructive testing benefits from data storage and visibility. Design and demand changes complicate production decisions. AI approaches like neural networks make online decisions. This article uses neural networks to predict SAW weld quality (SAW). Taguchi’s principles are applied in experiments and equations. The use of experimental data and regression analysis in the development of neural network models. Considering the relative speeds and precisions of the models. NNPSO performs better than BPNN, RBFNN, and GA combined (NNGA). The created weld quality prediction technique contains online monitoring, and it is customizable, competent, and accurate. Additionally, the scheme is competent. Models are tested. The strategy that has been presented and developed will assist people in protecting themselves from robot risks.
Keywords: Welding quality, intelligent decision-making system, welding quality, welding precision, optimized welding parameters, fuzzy neural networkWEBSITE DISCLAIMER
Last updated: 2022-06-15
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