Competitor Benchmarking by Structure Reliability Analysis with Improved Response Surface Method

Gao, Haibiao; Qin, Yixiao; Zhao, Linjiang; Gu, Jinpeng; Wang, Feng

doi:10.1007/s13369-022-06845-y

Cited by 3 publications

(1 citation statement)

References 25 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Tis algorithm can enhance the generalization performance of artifcial models. In addition, the technical advantages of GA are high parallelism, initial values independence, and outstanding robustness in the calculation of extreme values [93]. Figures 8 and 9 show the fundamental concepts and structures of ANN and GA, respectively.…”

Section: Artifcial Neural Network (Ann) Integrated With Genetic Algor...mentioning

confidence: 99%

A Damage Detection Approach in the Era of Industry 4.0 Using the Relationship between Circular Economy, Data Mining, and Artificial Intelligence

Gordan,

Sabbagh-Yazdi,

Ghaedi

et al. 2023

Advances in Civil Engineering

View full text Add to dashboard Cite

Over the last decades, the emergence of new technologies has inspired a paradigm shift for the fourth industrial revolution. For example, circular economy, data mining, and artificial intelligence (AI), which are multidisciplinary topics, have recently attracted industrial and academic interests. Sustainable structural health monitoring (SHM) also concerns the continuous structural assessment of civil, mechanical, aerospace, and industrial structures to upgrade conventional SHM systems. A damage detection approach inspired by the principles of data mining with the adoption of circular-economic thinking is proposed in this study. In addition, vibration characteristics of a composite bridge deck structure are employed as inputs of AI algorithms. Likewise, an artificial neural network (ANN) integrated with a genetic algorithm (GA) was also developed for detecting the damage. GA was applied to define the initial weights of the neural network. To aid the aim, a range of damage scenarios was generated and the achieved outcomes confirm the feasibility of the developed method in the fault diagnosis procedure. Several data mining techniques were also employed to compare the performance of the developed model. It is concluded that the ANN integrated with GA presents a relatively fitting capacity in the detection of damage severity.

show abstract

Section: Artifcial Neural Network (Ann) Integrated With Genetic Algor...mentioning

confidence: 99%

A Damage Detection Approach in the Era of Industry 4.0 Using the Relationship between Circular Economy, Data Mining, and Artificial Intelligence

Gordan,

Sabbagh-Yazdi,

Ghaedi

et al. 2023

Advances in Civil Engineering

View full text Add to dashboard Cite

show abstract

Machining accuracy reliability optimization of three-axis CNC machine tools using doubly-weighted vector projection response surface method

Wang,

2024

Int J Adv Manuf Technol

View full text Add to dashboard Cite

The reasonable allocation of geometric errors of NC machine tools can improve the machining accuracy reliability. However, due to the complexity and high nonlinearity of limit state function (LSF) of machining accuracy reliability, the fitting accuracy is usually low when the traditional method is used to approximate LSF. To solve this problem, a doubly-weighted vector projection response surface (DWVPRS) method, which considers not only the approximation results of the test sample point to LSF but the distance between the test sample point and the most probable failure point (MPFP), is proposed. Using the reliability sensitivity analysis method, the key geometric errors were identified and optimized to meet the design requirements. Finally, taking a large gantry guideway grinding machine as an example to verifies the correctness and effectiveness of the DWVPRS method proposed in this paper, the results show that after the optimization of geometric accuracy, the minimum and average reliability value of the grinding machine meet the design requirements. KeywordsDoubly-weighted vector projection response surface method  Machining accuracy reliability  Sensitivity analysis  Geometric error  Accuracy optimization List of symbols δxx Working table linear error in X-direction δyx Working table linear error in Y-direction δzx Working table linear error in Z-direction δxy Vertical slide linear error in X-direction δyy Vertical slide linear error in Y-direction δzy Vertical slide linear error in Z-direction δxz Movable beam linear error in X-direction δyz Movable beam linear error in Y-direction δzz Movable beam linear error in Z-direction

show abstract

Reliability analysis of plate parts with multiple interacting hole-edge cracks using complex variable function

Gao,

Qin,

Jia

et al. 2024

Applied Mathematical Modelling

View full text Add to dashboard Cite

Competitor Benchmarking by Structure Reliability Analysis with Improved Response Surface Method

Cited by 3 publications

References 25 publications

A Damage Detection Approach in the Era of Industry 4.0 Using the Relationship between Circular Economy, Data Mining, and Artificial Intelligence

A Damage Detection Approach in the Era of Industry 4.0 Using the Relationship between Circular Economy, Data Mining, and Artificial Intelligence

Machining accuracy reliability optimization of three-axis CNC machine tools using doubly-weighted vector projection response surface method

Reliability analysis of plate parts with multiple interacting hole-edge cracks using complex variable function

Contact Info

Product

Resources

About