Temperature feedback control of laser cladding using high resolution hyperspectral imaging

2016 ◽  
Author(s):  
Wim Devesse ◽  
Dieter De Baere ◽  
Michaël Hinderdael ◽  
Patrick Guillaume
Keyword(s):  
High Resolution ◽  
Feedback Control ◽  
Hyperspectral Imaging ◽  
Laser Cladding ◽  
Temperature Feedback

Model-Based Temperature Feedback Control of Laser Cladding Using High-Resolution Hyperspectral Imaging

2017 ◽  
Vol 22 (6) ◽  
pp. 2714-2722 ◽  
Author(s):  
Wim Devesse ◽  
Dieter De Baere ◽  
Michael Hinderdael ◽  
Patrick Guillaume
Keyword(s):  
High Resolution ◽  
Feedback Control ◽  
Hyperspectral Imaging ◽  
Laser Cladding ◽  
Model Based ◽  
Temperature Feedback

scholarly journals High-resolution grain size distribution of sediment core with hyperspectral imaging

2019 ◽  
Vol 393-394 ◽  
pp. 105536 ◽  
Author(s):  
Kévin Jacq ◽  
Charline Giguet-Covex ◽  
Pierre Sabatier ◽  
Yves Perrette ◽  
Bernard Fanget ◽  
...  
Keyword(s):  
Grain Size ◽  
High Resolution ◽  
Size Distribution ◽  
Hyperspectral Imaging ◽  
Grain Size Distribution ◽  
Sediment Core

Physical management and interpretation of an environmentally controlled composting ecosystem

1992 ◽  
Vol 32 (5) ◽  
pp. 657 ◽  
Author(s):  
E Harper ◽  
FC Miller ◽  
BJ Macauley
Keyword(s):  
Feedback Control ◽  
Mass Balance ◽  
Heat Production ◽  
Dry Matter ◽  
Heat Evolution ◽  
Processing Conditions ◽  
Mushroom Cultivation ◽  
Maximum Heat ◽  
Temperature Feedback ◽  
Wet Weight

Compost for mushroom cultivation was prepared in an environmentally controlled composting(ECC) system of 10 t maximum loading. Early in processing, ventilation was manually controlled to provide aerobic conditions. When the desired compost temperatures were reached, control through temperature feedback was used. Physical uniformity of processing conditions was achieved by recirculating large volumes of air within the reactor. Heat production was found to peak early in the composting process, reaching a maximum of about 8-9 W/kg initial wet (67-71%) substrate. When compost temperatures were allowed to rise to 63�C, maximum heat production occurred at 55-63�C. Total heat production per initial wet weight averaged 1.23 MJ/kg (range 0.92-1.51 MJ/kg), or 5.11 MJ/kg (range 4.04-7.57 MJ/kg) when measured per initial volatile dry matter. Heat evolution averaged 18.3 MJ/kg decomposed (range 15.4-22.0 MJ/kg). Oxygen usage followed a pattern similar to that of heat production reaching a maximum in the 55-63�C range. Peak O2 usage was about 9 x 10-7 kg O2/kg composts, or in volume terms, 2.9 x 10-6 m3 Air/kg comp0st.s. During temperature feedback control, O2 levels were maintained at about 19%. The enclosed ECC system permitted mass balance data to be collected for various components. Trials demonstrated that temperature and O2 could be closely controlled, resulting in-good compost uniformity.

Hyperspectral drill-core imaging for ore characterization

2020 ◽  
Author(s):  
Laura Tusa ◽  
Mahdi Khodadadzadeh ◽  
Margret Fuchs ◽  
Richard Gloaguen ◽  
Jens Gutzmer
Keyword(s):  
Machine Learning ◽  
Remote Sensing ◽  
High Resolution ◽  
Hyperspectral Imaging ◽  
Mineral Exploration ◽  
Electromagnetic Spectrum ◽  
Drill Core ◽  
Ore Characterization ◽  
Ore Types ◽  
Drill Cores

<p>Mineral exploration campaigns represent an essential step in the discovery and evaluation of ore deposits required to fulfil the global demand for raw materials. Thousands of meters of drill-cores are extracted in order to characterize a specific exploration target. Hyperspectral imaging is recently being explored in the mining industry as a tool to complement traditional logging techniques and to provide a rapid and non-invasive analytical method for mineralogical characterization. The method relies on the fact that minerals have different spectral responses in specific portions of the electromagnetic spectrum. Sensors covering the visible to near-infrared (VNIR) and short-wave infrared (SWIR) are commonly used to identify and estimate the relative abundance of minerals such as phyllosilicates, amphiboles, carbonates, iron oxides and hydroxides as well as sulphates (Clark, 1999). The distribution of these mineral phases can frequently be used as a proxy for the distribution of ore minerals such as sulphides. Typical core imaging systems can acquire hyperspectral data from a whole drill-core tray in a matter of seconds. Available sensors record data in several hundreds of contiguous spectral bands at spatial resolutions around 1 mm/pixel.</p><p>​​In this work, we apply a local high-resolution mineralogical analysis, such as SEM-MLA (Kern et al., 2018), for a precise and exhaustive mineral mapping of some selected small samples. We then upscale these mineralogical data acquired from thin sections to drill-core scale by integrating hyperspectral imaging and machine learning techniques. Our proposed method is composed of two main steps. In the first step, after initially co-registering the hyperspectral and high-resolution mineralogical data and making a training set, a machine learning model is trained. In the second step, we apply the learned model to obtain mineral abundance and association maps over entire drill-cores.</p><p>​​The mapping is further used for the calculation of other mineralogical parameters essential to exploration and further mining stages such as modal mineralogy, mineral association, alteration indices, metal grade estimates and hardness. The proposed methodological framework is illustrated on samples collected from a porphyry type deposit, but the procedure is easily adaptable to other ore types. Therefore, this approach can be integrated in the standard core-logging routine, complementing the on-site geologists and can serve as background for the geometallurgical analysis of numerous ore types.  </p><p>​​</p><p>​​Clark, R. N., 1999, “Spectroscopy of rocks and minerals, and principles of spectroscopy,” in Remote sensing for the earth sciences: Manual of remote sensing, vol. 3, John Wiley & Sons, Inc, pp. 3–58.</p><p>​​Gandhi, S. M. and Sarkar, B. C., 2016, “Drilling,” in Essentials of Mineral Exploration and Evaluation, pp. 199–234.</p><p>​​Kern, M., Möckel, R., Krause, J., Teichmann, J., Gutzmer, J., 2018. Calculating the deportment of a fine-grained and compositionally complex Sn skarn with a modified approach for automated mineralogy. Miner. Eng. 116, 213–225.</p>

Model-based Skin Pigment Cartography by High-Resolution Hyperspectral Imaging

2017 ◽  
Vol 2017 (8) ◽  
pp. 108-114
Author(s):  
Pierre Seroul ◽  
Mathieu Hébert ◽  
Marie Cherel ◽  
Romain Vernet ◽  
Raphael Clerc ◽  
...  
Keyword(s):  
High Resolution ◽  
Hyperspectral Imaging ◽  
Model Based ◽  
Skin Pigment

scholarly journals Strain relaxation in InGaN/GaN micro-pillars evidenced by high resolution cathodoluminescence hyperspectral imaging

2012 ◽  
Vol 112 (1) ◽  
pp. 013107 ◽  
Author(s):  
E. Y. Xie ◽  
Z. Z. Chen ◽  
P. R. Edwards ◽  
Z. Gong ◽  
N. Y. Liu ◽  
...  
Keyword(s):  
High Resolution ◽  
Hyperspectral Imaging ◽  
Strain Relaxation ◽  
Micro Pillars
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