Name
Title | ||
|---|---|---|
Multi-Objective Knowledge-Based Strategy For Process Parameter Optimization In Micro-Fluidic Chip Production |
Abstract | ||
|---|---|---|
We present an effective optimization strategy for industrial batch processes that is centered around two computational intelligence methods: linear and non-linear predictive mappings (surrogate models) for quality control (QC) indicators and state-of-the-art multi-objective evolutionary algorithms (MOEAs). The proposed construction methodology of the linear and neural network-based mappings integrates implicit expert-based knowledge with a new data-driven sample selection strategy that hybridizes several design of experiments paradigms. Using a case study concerning the production of micro-fluidic chips and 26 QC indicators, we demonstrate how incorporating modeling decisions like cross-validation stability analyses and objective clustering into our optimization strategy enables the discovery of well-performing surrogate models that can guide MOEAs towards high-quality Pareto non-dominated solutions. |
Year | Venue | Keywords |
|---|---|---|
2017 | 2017 IEEE SYMPOSIUM SERIES ON COMPUTATIONAL INTELLIGENCE (SSCI) | process parameters, design of experiments, surrogate modeling, evolutionary multi-objective optimization |
Field | DocType | Citations |
Data mining,Computational intelligence,Evolutionary algorithm,Computer science,Knowledge-based systems,Chip,Artificial neural network,Cluster analysis,Pareto principle,Design of experiments | Conference | 0 |
PageRank | References | Authors |
0.34 | 0 | 6 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Alexandru-Ciprian Zavoianu | 1 | 48 | 6.37 |
| Edwin Lughofer | 2 | 1940 | 99.72 |
| Robert Pollak | 3 | 3 | 2.15 |
| Pauline Meyer-Heye | 4 | 3 | 1.81 |
| Christian Eitzinger | 5 | 164 | 15.33 |
| Thomas Radauer | 6 | 66 | 4.94 |