Machine Learning Based Quantitative Damage Monitoring of Composite Structure

Qing, Xinlin; Liao, Yunlai; Wang, Yihan; Chen, Binqiang; Zhang, Fanghong; Wang, Yishou

doi:10.1080/19475411.2022.2054878

Cited by 46 publications

(15 citation statements)

References 170 publications

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“…The first and largest category of papers relates to systems that use sensor data to build machine learning models that then predict the loads on the aircraft over time. A few such as Oldersma [6], Qing [7], Mucha [8] and Gallimard [9], use physical monitoring sensors such as strain gauges and temperature sensors to determine the state of the loads and train the machine learning model. Others, such as Isom [10] and Qing [7], use external validation sensors like vibration, Piezoelectric sensors, or accelerometers to infer the state of the aircraft at a point in time.…”

Section: Related Workmentioning

confidence: 99%

“…A few such as Oldersma [6], Qing [7], Mucha [8] and Gallimard [9], use physical monitoring sensors such as strain gauges and temperature sensors to determine the state of the loads and train the machine learning model. Others, such as Isom [10] and Qing [7], use external validation sensors like vibration, Piezoelectric sensors, or accelerometers to infer the state of the aircraft at a point in time. Sikorsky has implemented onboard their rotary-wing aircraft their Virtual Monitoring of Loads technology, a ML-based technology for load estimation relying on aircraft sensor measurements and their onboard real-time HUMS data [10,11].…”

Section: Related Workmentioning

confidence: 99%

“…For the weighted average ensembles, the weight of a configuration is the normalised inverse of the rank sum or rank product of the configuration. The weightings for the rank sum ensemble used Equation ( 6), while the rank product ensemble weightings followed Equation (7):…”

Section: Simple and Weighted Average Ensemblementioning

confidence: 99%

See 2 more Smart Citations

Ensemble approaches for leveraging machine learning models in load estimation

Cheung,

Seabrook,

Valdés

et al. 2023

Aeronaut. j.

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Helicopter component load estimation can be achieved through a variety of machine learning techniques and algorithms. A range of ensemble integration techniques were investigated in order to leverage multiple machine learning models to estimate main rotor yoke loads from flight state and control system parameters. The techniques included simple averaging, weighted averaging and forward selection. Performance of the models was evaluated using four metrics: root mean squared error, correlation coefficient and the interquartile ranges of these two metrics. When compared, every ensemble outperformed the best individual model. The ensembles using forward selection achieved the best performance. The resulting output is more robust, more highly correlated and achieves lower error values as compared to the top individual models. While individual model outputs can vary significantly, confidence in their results can be greatly increased through the use of a diverse set of models and ensemble techniques.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

See 1 more Smart Citation

Ensemble approaches for leveraging machine learning models in load estimation

Cheung,

Seabrook,

Valdés

et al. 2023

Aeronaut. j.

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show abstract

“…With advances in neurosciences and high-capability computing devices, recent research is focused on application of machine learning (ML) algorithms based on Artificial Neural Networks (ANN) for guided wave damage identification, localization and qualification including an assessment on the probability of occurrence of damage in metallic and composite structural members. [14][15][16][17][18][19] Jiahui et al 20 utilized probabilistic imaging algorithm and statistical method to reduce the impact of composite anisotropy in Lamb wave-based damage localization and quantification in composite plate like structures. The algorithm was validated by experiments and results indicate an accurate prediction of the damage localization and quantification with an absolute error within 11 mm and 2.2 mm respectively for a sensor spacing of 100 mm.…”

Section: Introductionmentioning

confidence: 99%

Detection and Quantification of Asymmetrically Located Structural Damages by Mode Converted Guided Waves Using Piezo Electric Elements

Patnaik¹,

Renji²,

Gopal³

2023

The International Journal of Acoustics and Vibration

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Though damage identification using guided waves generated using ultrasonics is well proven, its usage for structural health monitoring poses difficulties. Piezo electric actuation and sensing overcomes this difficulty to some extent. In this work, usage of such guided waves for damage identification is investigated. Piezo electric wafer transducers are used for generating and sensing the guided waves. Presence of multiple modes and comparatively higher speeds of the guided waves throw up difficulties in damage identification. It is shown here that this problem can be addressed by considering different sensor location with respect to the damage with suitable interpretation of the results. Usage of fundamental antisymmetric (Ao) mode is found to be more suitable in localizing the damage compared to the fundamental symmetric (So) mode. Asymmetrically located damage causes mode conversion. It is demonstrated in this work that the mode converted guided wave (So) could be advantageously used for identification, localization, and quantification of the damage. Damage identification and localization schemes are evolved based on the location of the sensors with respect to the damage. It is shown that the reduction in the magnitude of the mode converted wave can be utilized for assessing the depth of the damage. 3D finite element based numerical models incorporating a PZT sensor are developed and validated with experimental results in terms of the characteristics of the waves, mode conversion due to damage and influence of the defect size on the received signals which are necessary for quantification of the damage.

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“…Therefore, its production and quality inspection technology become particularly important. As the chip carrier of integrated circuit, the semiconductor lead frame is a key structural part that can realize the electrical connection between the internal circuit lead and the external lead by means of bonding materials (gold wire, aluminum wire, copper wire) and form an electrical circuit [4][5][6]. Its main functions include connection with external circuits, heat dissipation, mechanical support, etc.…”

Section: Introductionmentioning

confidence: 99%

Full-size visual inspection system and method for semiconductor packaging lead-frame

Han¹,

2023

2022 Workshop on Electronics Communication Engineering

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Semiconductor packaging lead-frame has the characteristics of high density, high precision, refinement, and miniaturization. However, traditional manual detection has a series of problems such as difficulty, low efficiency, and high miss rate. Aiming at this industry problem, this paper develops a full-scale detection system and corresponding detection method of semiconductor packaging lead frame based on machine vision. The developed system is composed of a motion control platform and system, hardware control systems such as light source camera, and image algorithm platform. Through the optimized visual detection algorithm and the accurate correction method of workpiece attitude under motion measurement conditions, it can realize the one-click adjustment of several key dimensions such as frame length, width, loading area thickness, pin thickness, bending height, and aperture Micron level measurement, high precision, and fast speed, effectively ensure the genuine product rate and control the scrap rate, to assist semiconductor packaging enterprises to save labor costs and improve production efficiency.

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Machine Learning Based Quantitative Damage Monitoring of Composite Structure

Cited by 46 publications

References 170 publications

Ensemble approaches for leveraging machine learning models in load estimation

Ensemble approaches for leveraging machine learning models in load estimation

Detection and Quantification of Asymmetrically Located Structural Damages by Mode Converted Guided Waves Using Piezo Electric Elements

Full-size visual inspection system and method for semiconductor packaging lead-frame

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