A neural network-based prediction model for fine pitch stencil-printing quality in surface mount assembly

Authors:
Taho Yang;Tsung-Nan Tsai;Junwu Yeh
Affiliations:
Institute of Manufacturing Engineering, National Cheng Kung University, No. 1, Ta Hsueh Road, Tainan 70101, Taiwan;Department of Industrial Management, Shu-Te University, Kaohsiung 824, Taiwan;Institute of Manufacturing Engineering, National Cheng Kung University, No. 1, Ta Hsueh Road, Tainan 70101, Taiwan
Venue:
Engineering Applications of Artificial Intelligence
Year:
2005

Citing 4
Cited 2

Parallel distributed processing: explorations in the microstructure of cognition, vol. 1: foundations

Parallel distributed processing: explorations in the microstructure of cognition, vol. 1: foundations
Neural networks—Their applications and perspectives in intelligent machining

Computers in Industry - Special issue on IMS'91—Learning in IMS
Industrial application of fuzzy systems: adaptive fuzzy control of solder paste stencil printing

Information Sciences: an International Journal
Fuzzy and Neural Approaches in Engineering

Fuzzy and Neural Approaches in Engineering

The dynamic transfer batch-size decision for thin film transistor-liquid crystal display array manufacturing by artificial neural-network

Computers and Industrial Engineering
Review: A review of data mining applications for quality improvement in manufacturing industry

Expert Systems with Applications: An International Journal

Quantified Score

Hi-index	0.00

Visualization

Abstract

The soldering problems in surface mount assembly can represent considerable production cost increases and yield loss. About 60% of the soldering defect problems can be attributed to the solder paste stencil printing process. This paper proposes to solve a solder-paste stencil-printing quality problem by a neural network approach. Employment of a neuro-computing approach allows multiple inputs to the generation of multiple outputs. In this study, the inputs are composed of eight important factors in modeling the nonlinear behavior of the stencil-printing process for predicting deposited paste volumes. A 3^8^-^3 fractional factorial experimental design is conducted to efficiently collect structured data used for neural network training and testing. The results show that the proposed neural-network model is effective in solving a practical application.