Dr. Michelangelo Mortello | Monitoring | Excellence in Research Award

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Dr. Michelangelo Mortello | Monitoring | Excellence in Research Award

Dr. Michelangelo Mortello | Monitoring | Italian Welding Institute | Italy

Dr. Michelangelo Mortello is a highly accomplished researcher and materials engineer at the Istituto Italiano della Saldatura (IIS) – Ente Morale, Genoa, Italy, whose work stands at the intersection of welding technology, materials science, and sensing innovation. With a strong academic foundation culminating in a Ph.D. in Materials Engineering, Dr. Mortello has developed profound expertise in laser-based manufacturing, hybrid welding systems, hydrogen embrittlement mitigation, and subsurface sensing applications for industrial metals. His education and continuous professional engagement have equipped him with exceptional research insight into advanced joining processes, metallurgy, and additive manufacturing. Throughout his career, Dr. Mortello has accumulated an extensive body of work, publishing more than 29 scientific papers with over 800 citations and an h-index of 16, reflecting the global influence of his research contributions. His professional experience spans collaborative roles in advanced laboratories and European research initiatives, where he has contributed to the development of real-time sensing frameworks for process monitoring, structural diagnostics, and non-destructive evaluation. At IIS, he has led and contributed to numerous research projects focused on laser–arc hybrid welding, hydrogen embrittlement studies, and the structural evaluation of pipelines for repurposing energy systems. Dr. Mortello’s research interests encompass a wide spectrum of interdisciplinary domains, including additive manufacturing, laser processing, smart sensing technologies, and the integration of artificial intelligence in material diagnostics. His scientific pursuits aim to enhance the reliability, sustainability, and precision of industrial materials through predictive modeling and sensor-enabled process control. Equipped with advanced research skills such as finite element modeling (FEM), computational simulation, microstructural characterization, and corrosion testing, he consistently contributes innovative solutions to address the challenges of modern manufacturing.

Professional Profile: ORCID | Scopus | Google Scholar

Selected Publications 

  1. Mortello, M., & Casalino, G. (2021). Transfer mode effects on Ti6Al4V wall building in wire laser additive manufacturing. Manufacturing Letters. — Citations: 65

  2. Casalino, G., & Mortello, M. (2021). Laser-arc combined welding of AA5754 alloy. Materials Letters. — Citations: 58

  3. Contuzzi, N., Mortello, M., & Casalino, G. (2021). On the laser scarfing of epoxy resin matrix composite with copper reinforcement. Manufacturing Letters. — Citations: 47

  4. Casalino, G., Leo, P., Mortello, M., Perulli, P., & Varone, A. (2017). Effects of laser offset and hybrid welding on microstructure and IMC in Fe–Al dissimilar welding. Metals. — Citations: 83

  5. Casalino, G., Guglielmi, P., Lorusso, V. D., Mortello, M., Peyre, P., & Sorgente, D. (2017). Laser offset welding of AZ31B magnesium alloy to 316 stainless steel. Journal of Materials Processing Technology. — Citations: 112

Prof. Dr. Len Gelman | Monitoring | Best Researcher Award

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Prof. Dr. Len Gelman | Monitoring | Best Researcher Award 

Prof. Dr. Len Gelman, The University of Huddersfield, United Kingdom

Professor Len Gelman is a distinguished academic and researcher in the fields of Signal Processing, Condition Monitoring, and Maintenance. He holds a PhD and Doctor of Science (Habilitation) degrees and is a Fellow of several prestigious institutions, including the British Institute of Non-Destructive Testing (BINDT), IAENG, IDE, and HEA. Since 2017, Professor Gelman has served as the Professor and Chair in Signal Processing and Condition Monitoring/Maintenance at the University of Huddersfield, where he is also the Director of the Maintenance Centre for Efficiency and Performance Engineering. Prior to this, he was a Professor at Cranfield University (2002-2017), where he established a leading research programme in vibro-acoustical condition monitoring. Professor Gelman has received numerous accolades, including the UK Rolls-Royce Innovation Award (2019), the COMADIT Prize (2017), and the Best Paper Award at the International Condition Monitoring/Maintenance Conference (2016 and 2013). With extensive experience in both academia and industry, he has developed pioneering technologies for damage detection in turbines and aircraft engines, with significant contributions to Rolls-Royce, Dresser-Rand, and Scottish Southern Energy. Professor Gelman has built strategic international partnerships with top universities and research centres across the globe, including institutions in China, Korea, the USA, and Europe. He has supervised numerous postdoctoral fellows and researchers and is renowned for his leadership in vibro-acoustical condition monitoring, a field in which he has secured over £7.3M in research grants.

Professional Profile:

SCOPUS

GOOGLE SCHOLAR

Summary of Suitability for Best Researcher Award

Professor Len Gelman is an outstanding researcher whose extensive contributions to signal processing, condition monitoring, and maintenance engineering position him as a leading figure in his field, making him an ideal candidate for the Best Researcher Award. His innovative work has consistently benefited both industry and society, earning him significant recognition and awards.

Education 🎓

  • BSc (Hons), MSc (Hons) in Signal Processing and Condition Monitoring/Maintenance

  • PhD, Doctor of Science (Habilitation) in Vibro-Acoustical Monitoring/Maintenance

Work Experience 💼

  • 2017-present
    Professor and Chair in Signal Processing and Condition Monitoring/Maintenance
    Director of the Maintenance Centre for Efficiency and Performance Engineering
    University of Huddersfield, UK

  • 2002-2017
    Professor and Chair in Vibro-Acoustical Monitoring/Maintenance
    Cranfield University, UK

Achievements 🏆

  • Led research in condition monitoring and maintenance for complex systems.

  • Built the novel “Vibro-acoustical condition monitoring of complex mechanical systems” research program at Cranfield University.

  • Recruited over 90 MSc students from various international universities for MSc studies at Cranfield.

  • Successfully gained £7.3M in research grants for research projects involving leading companies like Rolls-Royce, Caterpillar, and Shell.

  • Established strategic international partnerships with world-class universities and research centres around the globe. Monitoring

Awards and Honors 🥇

  • UK Rolls-Royce Innovation Award (2019)

  • COMADIT Prize for significant contributions to condition monitoring/maintenance (2017)

  • Rolls-Royce Engineering Award for Innovation (2012)

  • EC Fellowship Award (2015) – European Social Fund-Human Capital Operational Programme

  • Oxford Academic Health Science Network Award (2014)

  • Best Paper Award at CM/MFPT 2016 and CM/MFPT 2013

  • William Sweet Smith Prize from the UK Institution of Mechanical Engineers (2010)

  • USA Navy Award for helicopter fault diagnosis methodologies (1998)

  • Acoustical Society of America Award (1998)

Professional Recognition 🌟

  • Chairman of several international committees, including:

    • International Institute of Acoustics and Vibration (USA) (2014-2016)

    • International Society for Condition Monitoring/Maintenance (2011-2017)

    • European Federation of NDT (2014-present)

  • Editorial Board Member for renowned journals:

    • “Insight” NDT and Condition Monitoring

    • “Electronics” (MDPI)

    • “Energies” (MDPI)

    • “Prognostics and Health Management”

    • IEEE Fellow (Recognized as a leading professional in the field)

Publication Top Notes:

Novel Investigation of Influence of Torsional Load on Unbalance Fault Indicators for Induction Motors

Vibration analysis of rotating porous functionally graded material beams using exact formulation

Novel instantaneous wavelet bicoherence for vibration fault detection in gear systems

Novel prediction of diagnosis effectiveness for adaptation of the spectral kurtosis technology to varying operating conditions

Vibration health monitoring of rolling bearings under variable speed conditions by novel demodulation technique

Novel fault identification for electromechanical systems via spectral technique and electrical data processing

Novel method for vibration sensor-based instantaneous defect frequency estimation for rolling bearings under non-stationary conditions

Novel higher-order spectral cross-correlation technologies for vibration sensor-based diagnosis of gearboxes

Novel vibration structural health monitoring technology for deep foundation piles by non-stationary higher order frequency response function