Maintaining tools is a crucial process in several industries including aerospace for extending their lifespan and ensuring they operate efficiently in the manufacturing sector. Manual maintenance is often slow and susceptible to errors, highlighting the importance of automating and optimizing the process to achieve the best possible performance and results from the machinery. An Intel® Student Ambassador developed an AI-powered Computer Vision based predictive tool maintenance solution called the ‘Tool Detective’ using oneAPI tools and libraries that aims at checking wear and tear of the metal cutting tools at every machine cycle. At the UXL Foundation’s oneAPI DevSummit held in Oct’24, he delivered an insightful technical talk about:
- Basics of the oneAPI programming model,
- How the oneAPI paradigm helps achieve heterogeneous, cross-vendor, parallel computing approach,
- The concept of predictive tool maintenance, and
- How oneAPI is useful in industry applications such as predictive tool maintenance.
This blog will give you highlights of the DevSummit session.
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About the Intel® Student Ambassador
Yuri Winche Acherman has a background in Aerospace Engineering and Data Science and has conducted research with NASA's Innovative Advanced Concepts and the MIT GeneSys Group. As an Intel Student Ambassador, he co-developed a project on Tool Detection, which evolved into a startup supported by Microsoft Founders Hub and Intel Liftoff Program for AI startups. He holds a master's degree in mechanical engineering from RWTH Aachen University in Germany.
About the Tech Talk in Brief
At the DevSummit session, Yuri introduced the basics of oneAPI, explaining its fundamental concepts and frameworks. He emphasized the importance of oneAPI in providing a unified programming model that supports multiple architectures, including CPUs, GPUs, FPGAs, and other accelerators from diverse vendors. This model helps developers avoid the complexity of writing code for different platforms by using SYCL*, an object-oriented parallel programming language. Yuri also discussed the UXL Foundation, an initiative under the umbrella of Linux Foundation*, which promotes open, accelerated computing and cross-platform performance. The foundation aims to simplify the development of high-performance applications by fostering collaboration among technology leaders and developers and advancing acceleration technologies.
He further delved into the concept of predictive maintenance, highlighting its significance in minimizing downtime, reducing costs, and extending equipment lifespan. He outlined four fundamental steps for implementing predictive maintenance:
- Establishing baselines,
- Installing IoT devices,
- Connecting devices to software, and
- Using insights for proactive maintenance.
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He presented a specific use case, the Tool Detective, which monitors metal cutting tool wear. Traditional methods of assessing tool wear are time-consuming and rely on visual inspections, whereas predictive maintenance using oneAPI enhances efficiency and reduces costs. He underscored the practical applications of oneAPI in improving industrial processes and maintenance strategies.
To demonstrate how oneAPI enhances the implementation of Tool detective, Yuri gave a hands-on coding demonstration. He walked the attendees through the process of importing and organizing data, using the Intel® Extension for Scikit-learn*, and leveraging the Intel® oneAPI Data Analytics Library (oneDAL) for computations. The example focused on digit recognition using SVC (Support Vector Classification). Yuri also explained how oneAPI optimizes algorithms for both CPUs and GPUs, enhancing development efficiency and performance. He also highlighted the benefits of cross-architecture development, which allows code to run on different XPU architectures, optimizing performance and flexibility for various scenarios.
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Overall, the session emphasized the advantages of oneAPI in simplifying development across multiple architectures and enhancing industrial applications like predictive maintenance. It provided a comprehensive overview of oneAPI's capabilities and practical insights into its implementation.
What’s Next?
We encourage you to explore our AI tools and framework optimizations and other toolkits powered by oneAPI for high-performance computing. You can also experiment with our optimized AI software on the latest Intel hardware on Intel® Tiber™ AI Cloud platform.
Useful Resources
Technical Software Product Marketing Engineer, Intel
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