In-situ crack and keyhole pore detection in laser directed energy deposition through acoustic signal and deep learning

Cracks and keyhole pores are detrimental defects in alloys produced by laser directed energy deposition (LDED). Laser-material interaction sound may hold information about underlying complex physical events such as crack propagation and pores formation. However, due to the noisy environment and intricate signal content, acoustic-based monitoring in LDED has received little attention. This paper proposes a novel acoustic-based in-situ defect detection strategy in LDED. The key contribution of this study is to develop an in-situ acoustic signal denoising, feature extraction, and sound classification pipeline that incorporates convolutional neural networks (CNN) for online defect prediction. Microscope images are used to identify locations of the cracks and keyhole pores within a part. The defect locations are spatiotemporally registered with acoustic signal. Various acoustic features corresponding to defect-free regions, cracks, and keyhole pores are extracted and analysed in time-domain, frequency-domain, and time-frequency representations. The CNN model is trained to predict defect occurrences using the Mel-Frequency Cepstral Coefficients (MFCCs) of the lasermaterial interaction sound. The CNN model is compared to various classic machine learning models trained on the denoised acoustic dataset and raw acoustic dataset. The validation results shows that the CNN model trained on the denoised dataset outperforms others with the highest overall accuracy (89%), keyhole pore prediction accuracy (93%), and AUC-ROC score (98%). Furthermore, the trained CNN model can be deployed into an in-house developed software platform for online quality monitoring. The proposed strategy is the first study to use acoustic signals with deep learning for insitu defect detection in LDED process.Comment: 36 Pages, 16 Figures, accepted at journal Additive Manufacturin

Design and CFD study of a simple wind turbine

Due to increasing energy requirements, wind turbine blades have increased in size and flexibility. This results in increasing aeroelastic effects, caused by fluid-structure interactions. Therefore, accurate modelling of these fluid-structure interactions is crucial in the development of larger wind turbines. In this study, a wind turbine blade is designed for operation under optimal conditions, an investigation was conducted on the fluid- structure interactions using computational fluid dynamics (CFD) to calculate the aerodynamic loads caused by fluid flow, which are then applied to a static structural analysis of the turbine blade using finite element analysis (FEA). One-way coupling is used to map the CFD loads to the FEA. The stress distributions and blade deflections are also found to facilitate in future design improvements.Bachelor of Engineering (Aerospace Engineering

A novel quality inspection method for aerosol jet printed sensors through infrared imaging and machine learning

The quality of printed electronic sensors by aerosol jet printing (AJP) process is hard to guarantee due to an insufficient reproducibility of the AJP process. This paper proposes a novel quality inspection method to identify defects on the printed sensor by AJP process using infrared imaging and machine learning. Potentially defective regions with high temperature distributions on printed lines are estimated from the infrared imaging when the current is applied. Demanded regions for the repair are identified by a machine learning algorithm. Finally, the applicability of the proposed method has been demonstrated by repair experiments.Nanyang Technological UniversityNational Research Foundation (NRF)This research was supported by Singapore Centre for 3D Printing (SC3DP), the National Research Foundation, Prime Minister's Office, Singapore under its Medium-Sized Centre funding scheme

Diminishing benefits of urban living for children and adolescents’ growth and development

Optimal growth and development in childhood and adolescence is crucial for lifelong health and well-being1–6. Here we used data from 2,325 population-based studies, with measurements of height and weight from 71 million participants, to report the height and body-mass index (BMI) of children and adolescents aged 5–19 years on the basis of rural and urban place of residence in 200 countries and territories from 1990 to 2020. In 1990, children and adolescents residing in cities were taller than their rural counterparts in all but a few high-income countries. By 2020, the urban height advantage became smaller in most countries, and in many high-income western countries it reversed into a small urban-based disadvantage. The exception was for boys in most countries in sub-Saharan Africa and in some countries in Oceania, south Asia and the region of central Asia, Middle East and north Africa. In these countries, successive cohorts of boys from rural places either did not gain height or possibly became shorter, and hence fell further behind their urban peers. The difference between the age-standardized mean BMI of children in urban and rural areas was <1.1 kg m–2 in the vast majority of countries. Within this small range, BMI increased slightly more in cities than in rural areas, except in south Asia, sub-Saharan Africa and some countries in central and eastern Europe. Our results show that in much of the world, the growth and developmental advantages of living in cities have diminished in the twenty-first century, whereas in much of sub-Saharan Africa they have amplified