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http://dx.doi.org/10.30693/SMJ.2020.9.3.31

Genetic Programming based Manufacutring Big Data Analytics  

Oh, Sanghoun (한국 방송 통신 대학교 바이오-정보통계학과 대학원)
Ahn, Chang Wook (광주과학기술원(GIST) AI대학원)
Publication Information
Smart Media Journal / v.9, no.3, 2020 , pp. 31-40 More about this Journal
Abstract
Currently, black-box-based machine learning algorithms are used to analyze big data in manufacturing. This algorithm has the advantage of having high analytical consistency, but has the disadvantage that it is difficult to interpret the analysis results. However, in the manufacturing industry, it is important to verify the basis of the results and the validity of deriving the analysis algorithms through analysis based on the manufacturing process principle. To overcome the limitation of explanatory power as a result of this machine learning algorithm, we propose a manufacturing big data analysis method using genetic programming. This algorithm is one of well-known evolutionary algorithms, which repeats evolutionary operators such as selection, crossover, mutation that mimic biological evolution to find the optimal solution. Then, the solution is expressed as a relationship between variables using mathematical symbols, and the solution with the highest explanatory power is finally selected. Through this, input and output variable relations are derived to formulate the results, so it is possible to interpret the intuitive manufacturing mechanism, and it is also possible to derive manufacturing principles that cannot be interpreted based on the relationship between variables represented by formulas. The proposed technique showed equal or superior performance as a result of comparing and analyzing performance with a typical machine learning algorithm. In the future, the possibility of using various manufacturing fields was verified through the technique.
Keywords
Manufacturing Big Data; Machine Learning; Genetic Programming; Predicion;
Citations & Related Records
Times Cited By KSCI : 4  (Citation Analysis)
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