scholarly journals Fast Image Classification for Grain Size Determination

Metals ◽  
2021 ◽  
Vol 11 (10) ◽  
pp. 1547
Author(s):  
Jen-Chun Lee ◽  
Hsiao-Hung Hsu ◽  
Shang-Chi Liu ◽  
Chung-Hsien Chen ◽  
Huang-Chu Huang
Keyword(s):  
Grain Size ◽  
Deep Learning ◽  
High Performance ◽  
Classification Model ◽  
Grain Size Analysis ◽  
Size Analysis ◽  
Metallographic Analysis ◽  
Network Architectures ◽  
Size Determination ◽  
Deep Learning Network

With the increasing application of steel materials, the metallographic analysis of steel has gained importance. At present, grain size analysis remains the task of experts who must manually evaluate photos of the structure. Given the software currently available for this task, it is impossible to effectively determine the grain size because of the limitations of traditional algorithms. Artificial intelligence is now being applied in many fields. This paper uses the concept of deep learning to propose a fast image classifier (FIC) to classify grain size. We establish a classification model based on the grain size of steel in metallography. This model boasts high performance, fast operation, and low computational costs. In addition, we use a real metallographic dataset to compare FIC with other deep learning network architectures. The experimental results show that the proposed method yields a classification accuracy of 99.7%, which is higher than existing methods, and boasts computational demands, which are far lower than with other network architectures. We propose a novel system for automatic grain size determination as an application for metallographic analysis.

scholarly journals Geochemical and Mineralogical Characterisation of Historic Zn–Pb Mine Waste, Plombières, East Belgium

Minerals ◽  
2020 ◽  
Vol 11 (1) ◽  
pp. 28
Author(s):  
Srećko Bevandić ◽  
Rosie Blannin ◽  
Jacqueline Vander Auwera ◽  
Nicolas Delmelle ◽  
David Caterina ◽  
...  
Keyword(s):  
Grain Size ◽  
Mine Waste ◽  
Geophysical Methods ◽  
Grain Size Analysis ◽  
Size Analysis ◽  
Mine Wastes ◽  
Metallurgical Waste ◽  
High Concentration ◽  
Geochemical Properties ◽  
Potential Applications

Mine wastes and tailings derived from historical processing may contain significant contents of valuable metals due to processing being less efficient in the past. The Plombières tailings pond in eastern Belgium was selected as a case study to determine mineralogical and geochemical characteristics of the different mine waste materials found at the site. Four types of material were classified: soil, metallurgical waste, brown tailings and yellow tailings. The distribution of the mine wastes was investigated with drill holes, pit-holes and geophysical methods. Samples of the materials were assessed with grain size analysis, and mineralogical and geochemical techniques. The mine wastes dominantly consist of SiO2, Al2O3 and Fe2O3. The cover material, comprising soil and metallurgical waste is highly heterogeneous in terms of mineralogy, geochemistry and grain size. The metallurgical waste has a high concentration of metals (Zn: 0.1 to 24 wt.% and Pb: 0.1 to 10.1 wt.%). In the tailings materials, Pb and Zn vary from 10 ppm to 8.5 wt.% and from 51 ppm to 4 wt.%, respectively. The mining wastes comprises mainly quartz, amorphous phases and phyllosilicates, with minor contents of Fe-oxide and Pb- and Zn-bearing minerals. Based on the mineralogical and geochemical properties, the different potential applications of the four waste material types were determined. Additionally, the theoretical economic potential of Pb and Zn in the mine wastes was estimated.

scholarly journals Centennial Impacts of the East Asian Summer Monsoon on Holocene Deltaic Evolution of the Huanghe River, China

2021 ◽  
Vol 11 (6) ◽  
pp. 2799
Author(s):  
Yanping Chen ◽  
Wenzhe Lyu ◽  
Tengfei Fu ◽  
Yan Li ◽  
Liang Yi
Keyword(s):  
Grain Size ◽  
Yellow River ◽  
Asian Summer Monsoon ◽  
River Delta ◽  
Grain Size Analysis ◽  
Size Analysis ◽  
Sediment Grain Size ◽  
Huanghe River ◽  
Huanghe River Delta ◽  
The Huanghe River

The Huanghe River (Yellow River) is the most sediment laden river system in the world, and many efforts have been conducted to understand modern deltaic evolution in response to anthropological impacts. However, the natural background and its linkage to climatic changes are less documented in previous studies. In this work, we studied the sediments of core YDZ–3 and marine surface samples by grain-size analysis to retrieve Holocene dynamics of the Huanghe River delta in detail. The main findings are as follows: The mean value of sediment grain size of the studied core is 5.5 ± 0.9 Φ, and silt and sand contents are 5.2 ± 2.3% and 8.2 ± 5.3%, respectively, while the variance of clay particles is relatively large with an average value of 86.4 ± 8.5%. All grain-size data can be mathematically partitioned by a Weibull-based function formula, and three subgroups were identified with modal sizes of 61.1 ± 28.9 μm, 30.0 ± 23.9 μm, and 2.8 ± 1.6 μm, respectively. There are eight intervals with abrupt changes in modal size of core YDZ–3, which can be correlated to paleo-superlobe migration of the Huanghe River in the Holocene. Based on these observations, the presence of seven superlobes in the history are confirmed for the first time and their ages are well constrained in this study, including Paleo-Superlobes Lijin (6400–5280 yr BP), Huanghua (4480–4190 yr BP), Jugezhuang (3880–3660 yr BP), Shajinzi (3070–2870 yr BP), Nigu (2780–2360 yr BP), Qikou (2140–2000 yr BP), and Kenli (1940–1780 and 1700–1650 yr BP). By tuning geomorphological events to a sedimentary proxy derived from core YDZ–3 and comparing to various paleoenvironmental changes, we proposed that winter climate dominated Holocene shifts of the Huanghe River delta on millennial timescales, while summer monsoons controlled deltaic evolution on centennial timescales.

Predicting well productivity from grain size analysis and logging while drilling

1996 ◽  
Vol 2 (1) ◽  
pp. 1-15 ◽  
Author(s):  
Andrew J. C. Hogg ◽  
Alan W. Mitchell ◽  
Susan Young
Keyword(s):  
Grain Size ◽  
Grain Size Analysis ◽  
Size Analysis ◽  
Well Productivity ◽  
Logging While Drilling
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