TY - CONF A1 - Krishna, S. K. P. A1 - Puthiyaveetil, N. A1 - Kidangan, R. A1 - Unnikrishnakurup, Sreedhar A1 - Ziegler, Mathias A1 - Myrach, Philipp A1 - Balasubramaniam, Krishnan A1 - Purushothaman, B. T1 - Raw data based image processing algorithm for fast detection of surface breaking cracks N2 - The aim of this work is to illustrate the contribution of signal processing techniques in the field of Non-Destructive Evaluation. A component’s life evaluation is inevitably related to the presence of flaws in it. The detection and characterization of cracks prior to damage is a technologically and economically significant task and is of very importance when it comes to safety-relevant measures. The Laser Thermography is the most effective and advanced thermography method for Non-Destructive Evaluation. High capability for the detection of surface cracks and for the characterization of the geometry of artificial surface flaws in metallic samples of laser thermography is particularly encouraging. This is one of the non- contacting, fast and real time detection method. The presence of a vertical surface breaking crack will disturb the thermal footprint. The data processing method plays vital role in fast detection of the surface and sub-surface cracks. Currently in laser thermographic inspection lacks a compromising data processing algorithm which is necessary for the fast crack detection and also the analysis of data is done as part of post processing. In this work we introduced a raw data based image processing algorithm which results precise, better and fast crack detection. The algorithm we developed gives better results in both experimental and modeling data. By applying this algorithm we carried out a detailed investigation Variation of thermal contrast with crack parameters like depth and width. The algorithm we developed is applied for various surface temperature data from the 2D scanning model and also validated credibility of algorithm with experimental data. T2 - QNDE conference 2016 - Review of progress in quantitative nondestructive evaluation CY - Atlanta, GA, USA DA - 18.07.2016 KW - Crack Detection KW - Laserthermografie KW - Thermografie KW - Risserkennung PY - 2017 SN - 978-0-7354-1474-7 DO - https://doi.org/10.1063/1.4974723 SN - 0094-243X VL - 1806 IS - 1 SP - UNSP 140008, 1 EP - 9 PB - AIP Publishing CY - New York AN - OPUS4-39387 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Puthiyaveettil, N. A1 - Krishna, S. A1 - Kidangan, R. A1 - Unnikrishnakurup, Sreedhar A1 - Krishnamurthy, C. V. A1 - Ziegler, Mathias A1 - Myrach, Philipp A1 - Balasubramaniam, Krishnan T1 - In-line laser thermography for crack detection at elevated temperature: A Numerical modeling study N2 - The detection and characterization of cracks prior to damage is a technologically and economically highly significant task and is of very importance when it comes to safety-relevant structures. The evaluation of a components life is closely related to the presence of cracks in it. Laser thermography has already high capability for the detection of surface cracks and for the characterization of the geometry of artificial surface flaws in metallic samples. Crack detection in metallic samples at high temperature is highly significant in present manufacturing scenario. During the casting process of billets, surface cracks form, due to the suboptimal cooling rates. These cracks reduce value of the billet and must be removed using machining process after cooling. This secondary process increases cost of manufacturing. In this work we developed a heat transfer model for laser thermography to study the thermal contrast variation with increase in surface temperature using finite element method (FEM). Here we are mainly concentrating the capability of the scanning laser thermography in crack detection which are in elevated temperature and numerical modeling study of thermal contrast variation of crack with respect increase in metal surface temperature. This study is important to prove the capability of laser thermography for crack detection in elevated temperature. Since we are using High power CW Laser to local heating of the metal surface which can give relatively high thermal contrast even at elevated temperature compare to other heating source. Here we are modeled and simulated 2D laser scanning across a surface breaking crack and developed an algorithm to produce the vicinity of crack. The algorithm we developed applied for various surface temperature data. And validated the credibility of the algorithm with experimental data. T2 - 13th Quantitative Infrared Thermography Conference 2016 CY - Gdansk, Poland DA - 04.07.2016 KW - Thermal contrast KW - Laser thermography KW - Thermography KW - Surface cracks KW - Elevated temperatures KW - FEM PY - 2016 UR - http://www.ndt.net/article/qirt2016/papers/092.pdf DO - https://doi.org/10.21611/qirt.2016.092 VL - 2016 SP - 588 EP - 596 PB - QIRT 2016 Proceedings AN - OPUS4-39105 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - JOUR A1 - Joshi, Ranjit A1 - Friedrich, Jörg Florian A1 - Krishna-Subramanian, S. T1 - Surface modification of ultra-high molecular weight polyethylene membranes using underwater plasma polymerization N2 - Ultra-high molecular weight polyethylene membranes were modified and subsequently polymer coated using the underwater plasma produced by glow discharge electrolysis. This plasma pretreatment generated various O-functional groups among them OH groups have dominated. This modified inner (pore) surface of membranes showed complete wetting and strong adhesion to a hydrogel copolymerized by glow discharge electrolysis also. The deposited hydrogel consists of plasma polymerized acrylic acid crosslinked by copolymerization with the bifunctional N,N'-methylenebis(acrylamide). Tuning the hydrogel hydrophilicity and bio-compatibility poly(ethylene glycol) was chemically inserted into the copolymer. Such saturated polymer could only be inserted on a non-classic way by (partial) fragmentation and recombination thus demonstrating the exotic properties of the underwater plasma. The modification of membrane was achieved by squeezing the reactive plasma solution into the pores by plasma-induced shock waves and supported by intense stirring. The deposited copolymer hydrogel has filled all pores also in the inner of membrane as shown by scanning electron microscopy of cross-sections. The copolymer shows the characteristic units of acrylic acid and ethylene glycol as demonstrated by infrared spectroscopy. A minimum loss in carboxylic groups of acrylic acid during the plasma polymerization process was confirmed by X-ray photoelectron spectroscopy. Additional cell adhesion tests on copolymer coated polyethylene using IEC-6 cells demonstrated the bio-compatibility of the plasma-deposited hydrogel. KW - Glow discharge electrolysis (GDE) KW - UHMWPE membrane KW - Pore modification KW - Adhesion KW - Copolymer hydrogel PY - 2013 DO - https://doi.org/10.1007/s11090-013-9476-2 SN - 0272-4324 VL - 33 IS - 5 SP - 921 EP - 940 PB - Plenum Publ. Corp. CY - New York, NY, USA AN - OPUS4-29335 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - JOUR A1 - Kunji Purayil, Sruthi Krishna A1 - Aroliveetil, S. A1 - Chaturvedi, A. A1 - Balasubramaniam, K. T1 - Simulation-assisted multimodal deep learning (Sim-MDL) fusion models for the evaluation of thermal barrier coatings using infrared thermography and Terahertz imaging N2 - Thermal Barrier Coatings (TBCs) are critical for high-temperature applications, such as gas turbines and aerospace engines, protecting metallic substrates from extreme thermal stress and degradation. Accurate evaluation of TBCs is essential to improve operational efficiency, optimize predictive maintenance strategies, and extend component life. Conventional non-destructive evaluation (NDE) techniques such as infrared thermography (IRT) and terahertz (THz) imaging have been widely used for TBC inspection with limitations when used independently, including sensitivity to surface conditions, limited penetration depth mainly in multi-layer coatings. This study proposes a novel framework called simulation-assisted multimodal deep learning (Sim-MDL) that combines IRT and THz data for a comprehensive evaluation of TBCs. To generalize the study to varying thermophysical properties of TBCs, the study uses simulation-generated data along with experimental data for training deep learning models. Two deep learning frameworks based on a 1D convolutional neural networks (CNN) and a long short-term memory (LSTM) with attention were developed for the multimodal feature fusion. The IR-THz fused frameworks enable simultaneous prediction of key TBC topcoat properties including thermal conductivity, heat capacity, topcoat thickness and refractive index. Experiments were conducted on four newly coated samples topcoat thicknesses ranging from 24 to 120 μm. An attention-based LSTM model trained on both simulation and experimental data shows high prediction accuracy with MAPE values ranging from 2.06% to 4.43% for thermal conductivity, 2.05% to 3.57% for heat capacity, 11.53% to 1.75% for topcoat thickness, and 0.27% to 1.05% for refractive index, respectively, for the topcoat layers of four samples. The proposed Sim-MDL framework outperformed single-modality and conventional parameter estimation methods in accuracy and robustness, highlighting the potential of multimodal data for automated analysis of TBC in industrial settings. KW - Multimodal fusion KW - Deep Learning KW - Infrared Thermography KW - Terahertz KW - Thermal Barrier Coatings (TBC) KW - Non-destructive Evaluation (NDE) PY - 2025 UR - https://nbn-resolving.org/urn:nbn:de:kobv:b43-651747 DO - https://doi.org/10.1038/s41598-025-31783-8 SN - 2045-2322 VL - 16 SP - 1 EP - 33 PB - Springer Science and Business Media LLC AN - OPUS4-65174 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER -