Data-Driven Modeling Using System Integration Scaling Factors and Positioning Performance of an Exposure Machine System | Zendy

Jinn-Tsong Tsai | Zendy; Cheng-Chung Chang | Zendy; Wen-Ping Chen | Zendy; Jyh-Horng Chou | Zendy

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Data-Driven Modeling Using System Integration Scaling Factors and Positioning Performance of an Exposure Machine System

Author(s) -

Jinn-Tsong Tsai,

Cheng-Chung Chang,

Wen-Ping Chen,

Jyh-Horng Chou

Publication year - 2017

Publication title -

ieee access

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 0.587

H-Index - 127

ISSN - 2169-3536

DOI - 10.1109/access.2017.2699279

Subject(s) - aerospace , bioengineering , communication, networking and broadcast technologies , components, circuits, devices and systems , computing and processing , engineered materials, dielectrics and plasmas , engineering profession , fields, waves and electromagnetics , general topics for engineers , geoscience , nuclear engineering , photonics and electrooptics , power, energy and industry applications , robotics and control systems , signal processing and analysis , transportation

A data-driven modeling approach is proposed for using system integration scaling factors and positioning performance of an exposure machine system to build models for predicting positioning errors and for analyzing parameter sensitivity. The proposed approach uses a uniform experimental design (UED), multiple regression (MR), back-propagation neural network (BPNN), adaptive neuro-fuzzy inference system (ANFIS), and analysis of variance (ANOVA). The UED reduces the number of experimental runs needed to collect data for modeling. The MR, BPNN, and ANFIS are used to construct positioning models of an exposure machine system. The significant system integration scaling factors are determined by ANOVA. The inputs to the data-driven model are system integration scaling factors f_x, f_y, and f_q, and the output is the positioning error. The UED was used to collect 41 experimental data, which comprised 0.0595% of the full-factorial experimental data. Performance tests demonstrated the excellent performance of the UED in collecting data used to build the MR, BPNN, and ANFIS data-driven models. The data-driven models can accurately predict positioning errors during validation. In addition, a sensitivity analyses of parameters showed that design parameters f_x and f_y have the greatest influence on positioning performance.

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