Development of a Prediction Model Based on RBF Neural Network for Sheet Metal Fixture Locating Layout Design and Optimization

The growing complexity of industrial processes and manufactured parts, the growing need for safety in service and the desire to optimize the life of parts, require the implementation of increasingly complex quality assessments. Among the various anomalies to consider, sub-millimeter surface defects must be the subject of particular care. These defects are extremely dangerous as they are often the starting point for larger defects such as fatigue cracks, which can lead to the destruction of the parts. Penetrant testing is now widely used for this type of defect, due to its good performance. Nevertheless, it should be abandoned eventually due to environmental standards. Among the possible alternatives, the use of eddy currents (EC) for conductive materials is a reliable, fast, and inexpensive alternative. The study concerns the design and modeling of eddy current probe structures comprising micro-sensors for non-destructive testing. The moving band finite element method is implemented for this purpose to take into account the movement of the sensor, experimental validations were conducted on a nickel-based alloy specimen. The real and imaginary parts of the impedance at every position of the sensor computed by experiments and simulations were in good agreement. The crack detection quality was quantified and the geometric characteristics of the defects were estimated using RBF NN (Radial Basis Function Neural Networks) that were designed and implemented on the acquired signals.

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“…RBF neural network is a feed-forward neural network [27]. The architecture of an RBF neural network consists of two…”

Section: A Rbf (Radial Basis Function) Neural Networkmentioning

confidence: 99%

Eddy Current Microsensor and RBF Neural Networks for Detection and Characterization of Small Surface Defects

Aber

Hamid

Elchikh

et al. 2022

Measurement Science Review

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“…Wang et al. 5 constructed a prediction model that approximates the relationship between the position of the clamp and sheet deformation using RBF neural network. They used the prediction model to find the optimal clamp's location for a simple rectangular sheet.…”

Section: Introductionmentioning

confidence: 99%

Multi-objective optimization of auto-body fixture layout based on an ant colony algorithm

Khodabandeh

Saryazdi

Ohadi

2019

Proceedings of the Institution of Mechanical Engineers, Part C:

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Fixtures are extensively used in many industries such as the car industry, to locate and constrain the sheet part during the assembly stage. Fixture layout affects on deformation of sheet parts. Therefore, fixture layout optimization is crucial to the accuracy and quality of products. In addition, the number of clamps that uses in the fixture is another important factor that must be considered in fixture design. This article presents a novel fixture layout optimization method by combining multi-objective ant colony algorithm (M-ACO) and the finite element method. The proposed method optimizes the fixture layout and the number of clamps simultaneously as a multi-objective problem. An approximation of Pareto frontier is acquired by the proposed method. The fixture layout for the side reinforcement of a car is optimized using the proposed method. The results show that the proposed approach performs effectively to optimize the auto-body fixture layout.

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“…Yaghoobi et al (2016) combined ANFIS and genetic algorithm to optimise the hydroforming process of cylindrical-spherical sheet parts. Radial basis function (RBF) neural network are effectively utilised by Wang et al (2016) to predict and optimise the location layout of sheet metal fixtures. Panthi et al (2016) estimated the velocity of spring back using a back propagation ANN model in straight flanging process.…”

Section: Introductionmentioning

confidence: 99%

Prediction of life of piercing punches using artificial neural network and adaptive neuro fuzzy inference systems

Salunkhe

Rajamani

Esakki

et al. 2019

IJMATEI

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Predicting the life of piercing punch is one of the major concerns in the design of compound dies. Finite element analysis is performed to determine the maximum and minimum principal stresses through which fatigue limit of punch is estimated. The factors affecting the life of punch are examined and a mathematical model is established using artificial neural network (ANN) and adaptive neuro fuzzy inference systems (ANFIS). The developed model is utilised to evaluate the life of punch for varied load conditions. Comparative evaluation of ANN and ANFIS results suggested that the later model is superior in predicting the life of punch and it can be effectively utilised in machine tool applications.

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Development of a Prediction Model Based on RBF Neural Network for Sheet Metal Fixture Locating Layout Design and Optimization

Cited by 18 publications

References 15 publications

Eddy Current Microsensor and RBF Neural Networks for Detection and Characterization of Small Surface Defects

Eddy Current Microsensor and RBF Neural Networks for Detection and Characterization of Small Surface Defects

Multi-objective optimization of auto-body fixture layout based on an ant colony algorithm

Prediction of life of piercing punches using artificial neural network and adaptive neuro fuzzy inference systems

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