Prediction of Laser Cutting Quality for QFN Package by using Neural Network

Tsai, Ming‐Jong; Li, Chen-Hao; Chen, Cheng-Che; Yao, Sin-Min

doi:10.1109/icma.2006.257519

Cited by 4 publications

(3 citation statements)

References 14 publications

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“…According to the previous reports (Tsai et al, 2005(Tsai et al, , 2006Li et al, 2007), it was known that the dominant laser parameters affecting the cutting quality for a QFN package are the current A, the laser pulse frequency F and the cutting speed V, which are then taken as the main laser control parameters in this paper.…”

Section: Main Laser Control Parametersmentioning

confidence: 98%

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Optimal laser-cutting parameters for QFN packages by utilizing artificial neural networks and genetic algorithm

Tsai

Chen

2008

Journal of Materials Processing Technology

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Section: Main Laser Control Parametersmentioning

confidence: 98%

“…The theoretical models describing their relationships between the average output power Y, the cutting speed V and the six output qualities including the depth of cutting line (D Line ) were described in Tsai et al (2006), Cenna and Mathew (2005) and some experimental results were reported in Li et al (2007).…”

Section: Six Output Cutting Qualitiesmentioning

confidence: 99%

Optimal laser-cutting parameters for QFN packages by utilizing artificial neural networks and genetic algorithm

Tsai

Chen

2008

Journal of Materials Processing Technology

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“…The Levenberg-Marquardt (L-M) algorithm can be used to increase the efficiency of the original BPNN significantly [42]. It is a fast mathematical technique based on the standard numerical optimization method.…”

Section: Lm-bpnnmentioning

confidence: 99%

Ergonomic Reliability Assessment of VDT System for Operation Design Based on Improved BPNN and HCR under Special Circumstances

Liu¹,

Liu²,

Huang³

et al. 2023

Computer Modeling in Engineering &Amp; Sciences

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Ergonomic reliability plays a significant role in the safe operation of devices. With the spread of infectious diseases around the world, in work environments with high loads and high infection rates, medical staff work in a state of high self-protection. The use of visual display terminal (VDT) for medical equipment has undergone fundamental changes, and the traditional medical equipment human-machine interface design needs to be improved. After the completion of design and development, a VDT design enters the experimental testing stage, which has significant limitations for simulating the work of medical staff in the high-load and high-infection environments. The testing cost is high, and subjects face harsh conditions; thus, an ergonomic reliability model that can predict the use of VDT in such special high-infection and high-load circumstances must be established. An ergonomic reliability model based on an improved backpropagation neural network (BPNN) and human cognition reliability (HCR) is proposed for predicting and evaluating operation flows according to medical equipment VDTs. Firstly, a small data sample can be used to train BPNN to generate a network that can ensure suitable accuracy. To prevent the model from falling into local optimal solutions, the bat algorithm is introduced to improve the BPNN. Compared to a traditional BPNN, the superiority of the improved BPNN is clearly demonstrated. Secondly, the HCR method is used to analyze and highlight changes in the human factor reliability of VDTs for medical equipment in different time processes and operating processes according to BPNN prediction results, to provide a reference for selecting the optimal method. Finally, the validity and availability of the proposed method are verified through an eye tracker experiment and statistical analysis results.

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Optimal design and fatigue life prediction for QFN solder joints by BP Artificial Neural Networks and Genetic Algorithm

2010

2010 11th International Conference on Electronic Packaging Technology &Amp; High Density Packaging

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Prediction of Laser Cutting Quality for QFN Package by using Neural Network

Cited by 4 publications

References 14 publications

Optimal laser-cutting parameters for QFN packages by utilizing artificial neural networks and genetic algorithm

Optimal laser-cutting parameters for QFN packages by utilizing artificial neural networks and genetic algorithm

Ergonomic Reliability Assessment of VDT System for Operation Design Based on Improved BPNN and HCR under Special Circumstances

Optimal design and fatigue life prediction for QFN solder joints by BP Artificial Neural Networks and Genetic Algorithm

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