Optimisation of a solder paste printing process parameters using a hybrid intelligent approach

Title:

Optimisation of a solder paste printing process parameters using a hybrid intelligent approach

Creator:

Yii, Hee Kim, Morad, Norhashimah, and Hitam, Muhamad Suzuri

Identifier:

https://cdk.lib.cas.cz/client/handle/uuid:0db4bc22-8f1d-4c9b-8919-72044b54de59
uuid:0db4bc22-8f1d-4c9b-8919-72044b54de59

Subject:

solder paste printing process, neural networks, genetic algorithms, bootstrap method, multiple regression method, and multi-objective optimisation

Type:

model:article and TEXT

Format:

bez média and svazek

Description:

This paper describes a method of modelling and optimising the solder paste printing process using an artificial iiitelligence approach. A hybrid approach combining the backpropagation neural network and genetic algorithm to model and subsequently optimise the process is developed using actual data collected from a manufacturing plant. Results obtained showed that the neural network developed was able to model the process successfully and the genetic algorithm developed was able to optimise the process parameters using various optimisation criteria.

Language:

English

Rights:

http://creativecommons.org/publicdomain/mark/1.0/
policy:public

Source:

Neural network world: international journal on neural and mass-parallel computing and information systems | 2001 Volume:11 | Number:2

Harvested from:

CDK

Metadata only:

false