Evaluation of the Surface Integrity and Recast Layer in Electrical Discharge Turning of WC-Co Composites.

Tungsten carbide (WC) and its composites are typically associated with high hardness and high wear resistance, posing challenges in conventional machining processes like turning. To address the machining difficulties of WC-Co, electrical discharge turning (EDT) was proposed. The rotational speed in EDT is a key factor influencing the machining results; however, conflicting reports exist about its impact on the EDT process. Therefore, the effect of rotational speed on three different machining regimes, including roughing, semi-finishing, and finishing, was investigated using energy-dispersive X-ray spectroscopy (EDX), SEM, and roughness tests. Additionally, elemental mapping was applied to illustrate the element distribution on the machined surface. The results indicated that increasing the rotational speed led to a 10% to 17% decrease in the recast layer thickness and a 14% to 54% reduction in the surface roughness (Ra).

Prediction of micro-hardness in thread rolling of St37 by convolutional neural networks and transfer learning.

This study introduces a non-destructive method by applying convolutional neural networks (CNN) to predict the micro-hardness of the thread-rolled steel. Material microstructure images were collected for our research, and micro-hardness tests were conducted to label the extracted microstructure images. In recent years, researchers have used machine learning (ML) and deep learning (DL) models to predict material properties for forming, machining, additive manufacturing, and other processes. However, they encountered industrial limitations primarily because of the absence of historical information on new and unknown materials, which are necessary to predict material properties by DL models. These problems can be solved by employing CNN models. In our work, we used a CNN model with two convolutional layers and visual geometry group (VGG19) as transfer learning (TL). We predicted four classes of micro-hardness of the St37 rolled threads. The prediction results of the micro-hardness test images by our proposed CNN model and pre-trained VGG19 model are comparable. Our proposed model has produced the same precision and recall scores as VGG19 for class B and class C hardness. VGG19 performed slightly better than our model for precision in class A and recall in class D. We observed that the training time of our proposed model using the CPU (central processing unit) was approximately nine times faster than the VGG19 model. Our proposed CNN and VGG19 have direct applications in advanced manufacturing (AM). They can automatically predict the micro-hardness in the thread rolling of St37. Our proposed model requires less memory and computational power and can be deployed more efficiently than the VGG19 model. Supplementary Information: The online version contains supplementary material available at 10.1007/s00170-022-10355-4.

Modeling Internal Movement of Children Born in Hong Kong to Nonlocal Mothers.

In Hong Kong, approximately 300,000 children were born to Mainland China couples in the period 1991-2012. According to Basic Law, the mini constitution of Hong Kong Special Administrative Region (SAR) government, these parents do not have residence rights, but their children do. As a result, most of these children have returned to Mainland China with their parents. An important consideration for policymakers is how many of these children (who are now adults in some cases) will return to Hong Kong for good, and when, as this will have a significant impact on social service provision, especially in the education sector, where it will be necessary to ensure there is capacity to meet the additional demand. Prior survey results conducted by the government suggested that more than 50% of these children would return to Hong Kong before age six. It is important to be able to provide a timely projection of the demand into the future. Here, we make use of the immigration records on the actual movement of these children and propose a Markov chain model to estimate their return rates in the future. Our results show that only about 25% of these children would return rather than 50% estimated by the survey. We also find that parents with better educational attainment levels are associated with lower return rates of their children. Timely and relevant social and public policies are needed to prepare for their return to minimize disruption to the local population and promote social harmony for the whole community.

Assessing the impact of the economic crises in 1997 and 2008 on suicides in Hong Kong, Taiwan and South Korea using a strata-bootstrap algorithm.

The Asian economic crises of 1997 and the 2008 Global Financial Crisis (GFC) had far-reaching impacts on Asian and other global economies. Turmoil in the banking and finance sectors led to downturns in stock markets, resulting in bankruptcies, house repossessions and high unemployment. These crises have been shown to be correlated with a deterioration in mental health and an increase in suicides, and it is important to understand the implication of these impacts and how such recessions affect the health of affected populations. With the benefit of hindsight, did lessons learned from the negative effects of the 1997 Asian economic recession impact the aftermath of the 2008 GFC in Asian countries? Utilising a framework based on a simple strata-bootstrap algorithm using daily data - where available - we investigate the trend in suicide rates over time in three different populations (Hong Kong, Taiwan and South Korea), and examine whether there were any changes in the pattern of suicide rates in each country subsequent to both the 1997 Asian and 2008. We find that each country responded differently to each of the crises and the suicide rates for certain age-gender specific groups in each country were more affected.