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İçerik Rainer Groh – Aerospace Engineer and Researcher and Rainer Groh – Aerospace Engineer tarafından sağlanmıştır. Bölümler, grafikler ve podcast açıklamaları dahil tüm podcast içeriği doğrudan Rainer Groh – Aerospace Engineer and Researcher and Rainer Groh – Aerospace Engineer veya podcast platform ortağı tarafından yüklenir ve sağlanır. Birinin telif hakkıyla korunan çalışmanızı izniniz olmadan kullandığını düşünüyorsanız burada https://tr.player.fm/legal özetlenen süreci takip edebilirsiniz.
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Podcast Ep. #37 – Anomalous is Using AI to Make Inspection More Accurate
MP3•Bölüm sayfası
Manage episode 260439963 series 1757905
İçerik Rainer Groh – Aerospace Engineer and Researcher and Rainer Groh – Aerospace Engineer tarafından sağlanmıştır. Bölümler, grafikler ve podcast açıklamaları dahil tüm podcast içeriği doğrudan Rainer Groh – Aerospace Engineer and Researcher and Rainer Groh – Aerospace Engineer veya podcast platform ortağı tarafından yüklenir ve sağlanır. Birinin telif hakkıyla korunan çalışmanızı izniniz olmadan kullandığını düşünüyorsanız burada https://tr.player.fm/legal özetlenen süreci takip edebilirsiniz.
Euan Wielewski is the co-founder and CEO at Anomalous Technologies, a start-up based in Edinburgh, Scotland using the latest machine learning and artificial intelligence (AI) technologies to enable better quality control of manufactured components. As aerospace engineers we know that quality control and inspection of flight-critical hardware is essential to guarantee safe operation of aircraft. Human visual inspection is a subjective and analogue process, which means that flight hardware is typically double and triple-checked to high levels of accuracy. This is where the AI tools developed by Anomalous Technologies are invaluable. By making inspection digital, operational traceability is immediately enhanced. Furthermore, digitisation allows the human inspection process to be accompanied by statistical tools that reduce the error rate of missed defects and improve inspection efficiency. Anomalous are already working with global aerospace companies such as Rolls Royce and Boeing, and the company is currently a member of the first cohort of the ATI Boeing Accelerator. In this episode of the podcast, Euan and I talk about: his broad background in aerospace engineering the challenges of human inspection in the aerospace industry how data-centric methods such as AI tools can help improve inspection accuracy and efficiency and how Anomalous is using their analytic tools to help out in the current Coronavirus pandemic. This episode of the Aerospace Engineering Podcast is brought to you by my patrons on Patreon. Patreon is a way for me to receive regular donations from listeners whenever I release a new episode, and with the help of these generous donors I have been able to pay for much of the expenses, hosting and travels costs that accrue in the production of this podcast. If you would like to support the podcast as a patron, then head over to my Patreon page. There are multiple levels of support, but anything from $1 an episode is highly appreciated. Thank you for your support! Selected Links from the Episode Anomalous webpage Anomalous wins recognition in Tokyo Anomalous profile in ADVANCE ATI Boeing Accelerator portfolio Boeing press release on ATI Accelerator cohort
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50 bölüm
MP3•Bölüm sayfası
Manage episode 260439963 series 1757905
İçerik Rainer Groh – Aerospace Engineer and Researcher and Rainer Groh – Aerospace Engineer tarafından sağlanmıştır. Bölümler, grafikler ve podcast açıklamaları dahil tüm podcast içeriği doğrudan Rainer Groh – Aerospace Engineer and Researcher and Rainer Groh – Aerospace Engineer veya podcast platform ortağı tarafından yüklenir ve sağlanır. Birinin telif hakkıyla korunan çalışmanızı izniniz olmadan kullandığını düşünüyorsanız burada https://tr.player.fm/legal özetlenen süreci takip edebilirsiniz.
Euan Wielewski is the co-founder and CEO at Anomalous Technologies, a start-up based in Edinburgh, Scotland using the latest machine learning and artificial intelligence (AI) technologies to enable better quality control of manufactured components. As aerospace engineers we know that quality control and inspection of flight-critical hardware is essential to guarantee safe operation of aircraft. Human visual inspection is a subjective and analogue process, which means that flight hardware is typically double and triple-checked to high levels of accuracy. This is where the AI tools developed by Anomalous Technologies are invaluable. By making inspection digital, operational traceability is immediately enhanced. Furthermore, digitisation allows the human inspection process to be accompanied by statistical tools that reduce the error rate of missed defects and improve inspection efficiency. Anomalous are already working with global aerospace companies such as Rolls Royce and Boeing, and the company is currently a member of the first cohort of the ATI Boeing Accelerator. In this episode of the podcast, Euan and I talk about: his broad background in aerospace engineering the challenges of human inspection in the aerospace industry how data-centric methods such as AI tools can help improve inspection accuracy and efficiency and how Anomalous is using their analytic tools to help out in the current Coronavirus pandemic. This episode of the Aerospace Engineering Podcast is brought to you by my patrons on Patreon. Patreon is a way for me to receive regular donations from listeners whenever I release a new episode, and with the help of these generous donors I have been able to pay for much of the expenses, hosting and travels costs that accrue in the production of this podcast. If you would like to support the podcast as a patron, then head over to my Patreon page. There are multiple levels of support, but anything from $1 an episode is highly appreciated. Thank you for your support! Selected Links from the Episode Anomalous webpage Anomalous wins recognition in Tokyo Anomalous profile in ADVANCE ATI Boeing Accelerator portfolio Boeing press release on ATI Accelerator cohort
…
continue reading
50 bölüm
Alla avsnitt
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