Analysing machine learning techniques for predicting the hole-filling in pin-in-paste technology
作者： Péter Martinek , Oliver Krammer
DOI: 10.1016/J.CIE.2019.07.033
关键词:
摘要: … technology in printed circuit board assembly (PCBA) for … these defects, intensive optimisations of the stencil printing … for predicting the hole-filling of solder paste in pin-in-paste …
sci-hub.se 参考文章(39)
Ameet Talwalkar, Ameet Talwalkar, Mehryar Mohri, Afshin Rostamizadeh, Afshin Rostamizadeh, Foundations of Machine Learning ,(2012)
Richard A Olshen, Charles J Stone, Leo Breiman, Jerome H Friedman, Classification and regression trees ,(1983)
Xiu-Juan Zhang, Ke-Zhang Chen, Xin-An Feng, Material selection using an improved Genetic Algorithm for material design of components made of a multiphase material Materials & Design. ,vol. 29, pp. 972- 981 ,(2008) , 10.1016/J.MATDES.2007.03.026
Balázs Illés, Gábor Harsányi, Heating characteristics of convection reflow ovens Applied Thermal Engineering. ,vol. 29, pp. 2166- 2171 ,(2009) , 10.1016/J.APPLTHERMALENG.2008.10.014
J.-S.R. Jang, ANFIS: adaptive-network-based fuzzy inference system systems man and cybernetics. ,vol. 23, pp. 665- 685 ,(1993) , 10.1109/21.256541
Martin Fodslette Møller, Original Contribution: A scaled conjugate gradient algorithm for fast supervised learning Neural Networks. ,vol. 6, pp. 525- 533 ,(1993) , 10.1016/S0893-6080(05)80056-5
D. Teixidor, M. Grzenda, A. Bustillo, J. Ciurana, Modeling pulsed laser micromachining of micro geometries using machine-learning techniques Journal of Intelligent Manufacturing. ,vol. 26, pp. 801- 814 ,(2015) , 10.1007/S10845-013-0835-X
Abolfazl Gharaei, Bahman Naderi, Mohammad Mohammadi, Optimization of rewards in single machine scheduling in the rewards-driven systems Management Science Letters. ,vol. 5, pp. 629- 638 ,(2015) , 10.5267/J.MSL.2015.4.002
K.L. Edwards, Towards more effective decision support in materials and design engineering Materials & Design. ,vol. 15, pp. 251- 258 ,(1994) , 10.1016/0261-3069(94)90071-X
Taho Yang, Tsung-Nan Tsai, A neurofuzzy-based quality-control system for fine pitch stencil printing process in surface mount assembly Journal of Intelligent Manufacturing. ,vol. 15, pp. 711- 721 ,(2004) , 10.1023/B:JIMS.0000037719.35871.AA