A profile identification system for surface mount printed circuit board assembly
Proceedings of the 21st international conference on Computers and industrial engineering
A modelling tool for the thermal optimisation of the reflow soldering of printed circuit assemblies
Finite Elements in Analysis and Design
An Introduction to Genetic Algorithms for Scientists and Engineers
An Introduction to Genetic Algorithms for Scientists and Engineers
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Response Surface Methodology: Process and Product in Optimization Using Designed Experiments
Response Surface Methodology: Process and Product in Optimization Using Designed Experiments
Comparison of Performance between Different Selection Strategies on Simple Genetic Algorithms
CIMCA '05 Proceedings of the International Conference on Computational Intelligence for Modelling, Control and Automation and International Conference on Intelligent Agents, Web Technologies and Internet Commerce Vol-2 (CIMCA-IAWTIC'06) - Volume 02
Design and Analysis of Experiments
Design and Analysis of Experiments
Using recurrent neural networks to detect changes in autocorrelated processes for quality monitoring
Computers and Industrial Engineering
Integrated multiobjective optimization and a priori preferences using genetic algorithms
Information Sciences: an International Journal
Neuro-genetic approach to optimize parameter design of dynamic multiresponse experiments
Applied Soft Computing
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This paper presents a comparative study for optimizing the thermal parameters of the reflow soldering process using traditional and artificial intelligence (AI) approaches. High yields in the reflow soldering process are essential to a profitable printed circuit board (PCB) assembly operation. A reflow thermal profile is a time-temperature graph which is used to properly control the thermal mass and heat distribution to form robust solder joints between the PCB and electronics components during reflow soldering. An inhomogeneous temperature distribution for a reflow thermal profile can cause various soldering defects, which can jeopardize product reliability and lead to significant productivity loss. In the multi-objective optimization problem, three alternative optimization methods are discussed and compared: response surface methodology (RSM), nonlinear programming (NLP), and a hybrid AI technique. A dataset was gathered using a 3^8^-^4 experimental design for the development of meta-models through response surface quadratic modeling. In the first method, RSM is used to acquire the optimal heating parameters, while in the second method NLP is used to derive a global solution based on the meta-models. The back-propagation neural network (BPN) is used in the third method to formulate the nonlinear relationship between the heating inputs and responses. A genetic algorithm (GA) is then used to elicit the optimal heating parameters from the established BPN model. The evaluation results show that all three methods provide satisfactory soldering performance in terms of the process capability, sigma level, and process window indices (PWIs). Particularly, the hybrid AI approach provides superior nonlinear formulation capability and optimization performance.