Design and Analysis of Slotted Block Horn Used For Ultrasonic Power Applications

Sarraf, Ziad Shakeeb Al; Saeed, Majid Midhat

doi:10.35741/issn.0258-2724.54.5.28

Cited by 5 publications

(2 citation statements)

References 8 publications

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“…The displacement amplitude of both horns was measured using laser Doppler vibrometer and results were good in agreement with FEA results. Al Sarraf et al 14 designed and simulated the ultrasonic slotted block horn to optimize the slot positions using FEA. The FEA results showed that block horn with two slots improves the amplitude of vibration.…”

Section: Introductionmentioning

confidence: 99%

Design optimization of block horn for ultrasonic joining application using RSM–FEA–GA integration approach

Anand

Elangovan

2022

Proceedings of the Institution of Mechanical Engineers, Part E:

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This study investigates the design optimization of block horn used in ultrasonic insertion application. Ultrasonic insertion is the process of joining a metal insert with thermoplastic component. As the performance of ultrasonically produced joint depends upon the uniformity of amplitude of vibration generated by horn, the uniformity of displacement amplitude can be improved by optimizing the block horn design. The horn is computationally designed and acoustically analysed by integrating finite element analysis (FEA) with response surface methodology (RSM). The polynomial model for displacement amplitude is developed using RSM. Further, the design of horn is optimized to improve the displacement amplitude by coupling the developed polynomial model with genetic algorithm (GA) as fitness function. The optimized design is acoustically analysed, the results show that the optimal design yields maximum displacement amplitude of 29.57 µm, and the maximum stress induced in the optimal design is 16.3 MPa, which is much lower than fatigue strength of horn material. As the heat developed at joint is quality indicator, the temperature obtained at joint is measured using data acquisition system (DAQ). The measured temperature is 138°C, which correlates well with displacement amplitude developed by optimal design of block horn. The results of this study revealed that the proposed RSM–FEA–GA integration approach managed to find the optimal combination of design variables, which yields maximum displacement amplitude and results good joint performance.

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Section: Introductionmentioning

confidence: 99%

Design optimization of block horn for ultrasonic joining application using RSM–FEA–GA integration approach

Anand

Elangovan

2022

Proceedings of the Institution of Mechanical Engineers, Part E:

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“…Ultrasonic welding technique remain one of the major inescapable joining method, cost effective, robust and essential process that significantly apply on joining materials, polymers, composites and covered a wide range of manufacturing and industrial applications. The joining state of materials by ultrasonic is recognized through forming a solid-state condition at intimate surfaces without any fusion or melting (Lucas et al, 2008;Ziad Shakeeb Al Sarraf, 2019). Generally, ultrasonic welding system consist of driving piezoelectric transducer that convert the alternative current 50-60 Hz and raise up to 20 KHz or above by means the effect of piezoceramic discs, booster the second component which is optionally added to increase the amount of vibration amplitude delivering by transducer to the working area and to hold the welding rig securely and rigidly, while the last component which is described to be an essential component of welding system is a sonotrode or horn.…”

Section: Introductionmentioning

confidence: 99%

Prediction of Weld Strength in Power Ultrasonic Spot Welding Process Using Artificial Neural Network (ANN) and Back Propagation Method

Sarraf

2022

IJIE

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In this presented work, the employment of artificial neural network (ANN) connected with back propagation method was performed to predict the strength of joining materials that carried out by using ultrasonic spot welding process. The models which created in this study were investigated and their process parameters were analysed. These parameters were classified and set as input variables like for example applying pressure, time of duration weld and trigger of vibrating amplitude while weld strength of joining dissimilar materials (Al-Cu) is set as output parameters. The identification from the process parameters are obtained using number of experiments and finite element analyses based prediction. The results of actual and numerical are accurate and reliability, however its complexity has significant effect due to sensitive to the condition variation of welding processes. Therefore, the needed for an efficient technique like artificial neural network coupled with back propagation method is required to use the experiments as an input data in simulation of ultrasonic welding process, finding the adequacy of modeling process in prediction of weld strength and to confirm the performance of using mathematical methods. The results of the selecting non-linear models show a noticeable potency when using ANN with back propagation method in providing high accuracy compared with other results obtained by conventional models.

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Preliminary Results for Optimal Design of the Horn with Complex Welding Profiles in Ultrasonic Welding

Ngo-Nhu,

Nguyen-Van,

Nguyen-Thi

et al. 2024

Lecture Notes in Networks and Systems

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Design and Analysis of Slotted Block Horn Used For Ultrasonic Power Applications

Cited by 5 publications

References 8 publications

Design optimization of block horn for ultrasonic joining application using RSM–FEA–GA integration approach

Design optimization of block horn for ultrasonic joining application using RSM–FEA–GA integration approach

Prediction of Weld Strength in Power Ultrasonic Spot Welding Process Using Artificial Neural Network (ANN) and Back Propagation Method

Preliminary Results for Optimal Design of the Horn with Complex Welding Profiles in Ultrasonic Welding

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