๐ Last week at #EuroBrake2024, we were at the forefront of cutting-edge discussions on #AI and #MachineLearning in product development. Dr. Alexander Plunkett shared insightful initiatives and visions that are setting the pace for how AI can enhance innovation and #sustainability in automotive engineering. Want to be a part of #TheTennecoDifference? #TeamTenneco Braking is looking for passionate #DataScientists and #DataEngineers to join our dynamic #Engineering Data & Analytics team. Visit our careers page: tenneco.com/jobs to learn more about our open positions and how you can contribute to pioneering solutions that will support our drive to becoming the most trusted partner to the transportation industry. #TennecoBraking #Innovation #Sustainability #EngineeringExcellence #JoinUs #CareerOpportunities
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The sun sets on another productive day, collaborating and sharing expertise with automotive professionals on their AI journey. DataProphet's data scientists have partnered with an automotive stud welding facility to produce incredible results. ๐๐ Months of dedicated teamwork, meticulously linking data to quality outcomes, have paved the way for a transformation in automotive manufacturing. This journey is a testament to human collaboration and expertise. Explore the core technology behind this people-driven innovation. #Teamwork #AIInAutomotive ๐๐ค Link in the comments!
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๐๐ฉ๐ญ๐ข๐ฆ๐ข๐ณ๐ข๐ง๐ ๐ฉ๐ซ๐จ๐๐๐ฌ๐ฌ๐๐ฌ ๐จ๐ง ๐ญ๐ก๐ ๐ฌ๐ก๐จ๐ฉ ๐๐ฅ๐จ๐จ๐ซ ๐ก๐๐ฏ๐ ๐๐๐๐ง ๐ ๐๐จ๐ง๐ฌ๐ญ๐๐ง๐ญ ๐๐จ๐ซ ๐๐ฎ๐ญ๐จ๐ฆ๐๐ค๐๐ซ๐ฌ ๐จ๐ฏ๐๐ซ ๐ญ๐ก๐ ๐ฒ๐๐๐ซ๐ฌ ๐๐ฎ๐ญ ๐ก๐๐ฏ๐ ๐ญ๐ก๐๐ข๐ซ ๐ญ๐๐๐ก๐ง๐จ๐ฅ๐จ๐ ๐ข๐๐ฌ ๐๐ง๐ ๐ฌ๐ญ๐ซ๐๐ญ๐๐ ๐ข๐๐ฌ ๐ฉ๐ฅ๐๐ญ๐๐๐ฎ๐๐? Bill Graca, Senior Director of Automotive at Capgemini Invent North America, urges auto manufacturers to rethink, challenge long-established processes, and leverage ๐๐ซ๐ญ๐ข๐๐ข๐๐ข๐๐ฅ ๐ข๐ง๐ญ๐๐ฅ๐ฅ๐ข๐ ๐๐ง๐๐ and ๐ฆ๐๐๐ก๐ข๐ง๐ ๐ฅ๐๐๐ซ๐ง๐ข๐ง๐ to drive more ๐ฉ๐ซ๐จ๐๐ฎ๐๐ญ๐ข๐ฏ๐ข๐ญ๐ฒ, ๐๐๐๐ข๐๐ข๐๐ง๐๐ฒ and ๐จ๐ฏ๐๐ซ๐๐ฅ๐ฅ ๐๐ช๐ฎ๐ข๐ฉ๐ฆ๐๐ง๐ญ ๐๐๐๐๐๐ญ๐ข๐ฏ๐๐ง๐๐ฌ๐ฌ.ย ๐ Read more via IndustryWeek: โก๏ธ https://bit.ly/3Lr3d98 ๐ #GetTheFutureYouWant #Automotive #Manufacturing #ArtificialIntelligence #MachineLearning #Gigafactory
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๐ Academic researchers and technology companies are turning their attention toward deep learning as a useful tool for automotive manufacturers faced with a variety of visual inspection requirements that traditional tools have struggled to handle. Traditional machine vision systems are used for quality and end of line inspection, traceability of parts, gauging and measurement, presence/ absence checking, metrology and porosity inspection. However, these tools come with longstanding problems, including training time needed, cost, interoperability, maintenance, and handling complex use cases. Learn More: https://hubs.ly/Q02v93zL0 #shopfloorcoffee #skilledtrades #workforcedevelopment #manufacturing #industrial #metalfabrication #manufacturer #machining #manufacturers #manufacturingindustry #industrialmanufacturing #automotive
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โAI at Boschโ https://lnkd.in/e9nz-8dq Bosch is a German multinational technology and engineering company that manufactures products across various industries, including aerospace, automotive, energy and utilities, healthcare and others. In this article, we take a closer break down two of Boschโs timely #AI use cases: - Automated visual quality inspection - Real-time recommendation engine #AI #artificialintelligence #technology #engineering #usecases
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Founder | Managing Partner | Entrepreneur | B.Tech | EMBA | IPMA level B | Emerging Technologies | Corporate strategies | Leadership Expert | Global Coach, Mentor & Trainer | Innovation
How AI is impacting Automotive and Manufacturingย Industries
How AI is impacting Automotive and Manufacturingย Industries
https://clustiv.com
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Data & Analytics Management: BI, DWH, Analytics, ML, Data Quality, Data Architecture, MDM, Governance, IOT, Metadata, Document Management, Security, GDPR, Integration, Monetisation, Digital Transformation
Working in a #start-up provides interesting challenges when applying modern approaches to #dataanalysis due to minimal amounts of data. Knowledge in the #BEV sector is scarce as it evolves, so a lot of traditional #ICE assumptions cannot be made and leaders need to let the data lead them. This applies to order pipeline, supply chain, inventory management and ultimately engineering and the analysis is very often NOT #usecase driven it is #exporatory_data_analysis. Machine Learning #ML supports the ability to review #telematics data in a number of ways for a particular vehicle or #signls across vehicles as we go through design verification to pilot vehicles. The aspects of supervised and unsupervised learning enable us to establish real relationships between signals relating to a driver and say the performance of a battery e.g. accelerator, brake, speed, payload, and SOC overlaid with topology data. Aspects of #statistical_process_control and #linear_regression in particular support our understanding of behaviour, vehicle safety and vehicle performance. Similar techniques are also applicable for understanding the relationship of a vehicle to charging infrastructure. Despite these opportunities, so many companies are still not recognising the need to get data & analytics fully operational with budget to support success from day 1, often delaying deployment of capability into a business. At the end of what has been a fantastic journey at Volta Trucks with a lot of great people it is time to move on. Many thanks everyone on this journey, wishing you all the best and for those remaining I wish you good luck in keeping the dream alive. Dave Cleaves Asif Hameed Satish K G Maxime LAMBOLEY Pradeep K .Paul Boardman Intrepid Control Systems Fernando C. Martinelli Mark Garnett
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The Impact of Automated Manufacturing on the Labour Market When machines replace humans in any form the fear of job loss becomes inevitable. The automated manufacturing has caused a fear for people in the manufacturing sector. Disadvantages of Automated Manufacturing: 1. High Initial Costs 2. Job Displacement 3. Maintenance Challenges 4. Technological Obsolescence 5. Dependency on Skilled Personnel Read More: https://lnkd.in/digcWFvf #automation #technology #engineering #innovation #robotics #tech #industry #manufacturing #smarthome #homeautomation #ai #artificialintelligence #business #mrbusinessmagazine
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Co-Founder at Renumics ๐ | ML Engineer ๐ค | Writing about AI in engineering and manufacturing and interactive ML data visualization
A recent paper from Mercedes-Benz AG and Leiden University enhances #automotive #testing with efficient and precise #anomaly #detection: "MA-VAE: Multi-head Attention-based Variational Autoencoder Approach for Anomaly Detection in Multivariate Time-series Applied to Automotive Endurance Powertrain Testing" Lucas Correia et al. propose an automatic anomaly detection method to address the inefficiencies of manual data evaluation in automotive endurance powertrain testing. The MA-VAE model uses a variational autoencoder enhanced with multi-head attention to identify anomalies in multivariate time-series data. Challenges addressed - Automotive endurance powertrain testing generates vast, complex, and temporal data, making manual evaluation impractical and inefficient. Key challenges include: - The large volume and diverse nature of data. - The necessity for timely anomaly detection to minimize downtime and prevent damage. - The impracticality of manual inspections for each measurement, leading to delays. ๐ Approach Model Architecture: - Encoder: Utilizes bidirectional long short-term memory (#BiLSTM) layers to map time-series windows into temporal latent distributions. - Multi-head Attention (MA): Enhances the latent matrix before passing it to the decoder. - Decoder: Reconstructs input data from the context matrix. Anomaly Detection Mechanism: - The model is trained on unlabelled data to learn normal system behavior. - Anomalies are detected by comparing the log probability of reconstructed data against a validation set threshold. Avoidance of Bypass Phenomenon: - The attention mechanism ensures the model does not ignore the latent path between encoder and decoder, preventing bypassing important information. Reverse-window Process: - Maps individual windows back to continuous time series, enabling the model to handle variable-length sequences. ๐ Results -> Anomaly Detection Performance: F-score 0.79 -> MA-VAE outperformed several other VAE-based anomaly detection models (F-score โค0.53) Conclusion The MA-VAE model offers a significant improvement in automatic anomaly detection for automotive endurance powertrain testing by integrating multi-head attention with a variational autoencoder. ๐ Paper at arXiv: https://lnkd.in/eQAX4CVv MA-VAE: Multi-head Attention-based Variational Autoencoder Approach for Anomaly Detection in Multivariate Time-series Applied to Automotive Endurance Powertrain Testing Lucas Correia, Jan-Christoph Goos, Philipp Dr. Klein , Thomas Bรคck, Anna Kononova License: CC BY 4.0
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Automotive Industry Platform I Driving Automotive Excellence: Strategy | Intelligence | Digital Transformation | COE Domain SME II Ex Skoda Volkswagen AUDI II Ex MINDA Group
The future of automotive will be driven by software .......... Nearly one-in-four automotive companies believe they already operate as a software company today. Close to fifty percent believe they will be a software company in the next three-to-five years. Automotive leaders anticipate that generative AI will play a growing role in how code is created within their organizations .....76% -of auto organizations believe software is critical to their future product and service strategies. Automotive leaders predict that 37% - of code will be created with assistance from generative AI in the next three years Question for you ---- What you Believe ?? 1. Our company will be a software company in the next 3-5 years 2. Our company is already a software company today Share your thoughts..... #SDT #Automotive #FutureofAutomotive
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๐ Machine Learning for Battery & Fuel Cells | ๐ป Founder of Monolith | ๐ Top 10 Innovator in Germany | ๐ Rising Star in Automotive |
#MachineLearning to Reduce Battery Testing ๐๐ ๐ As an engineering leader, I understand the pressing need to keep pace with the dynamic landscape of battery electric vehicle (BEV) technology. In the 2023 State of AI in Engineering study by Forrester Consulting, it's evident that staying competitive requires new strategies for expediting complex product ideation and launch. My expertise lies in harnessing the power of machine learning to reshape the design and validation of cutting-edge BEV systems. ๐ One of the foremost challenges faced by automotive engineers today is ensuring exceptional performance, durability, and safety of BEV batteries. With the surging demand for improved range, charging efficiency, and reliability, I specialize in utilizing machine learning and AI to address these challenges head-on. Traditional testing approaches involve exhaustive trials, resulting in either time-intensive processes or risking critical performance parameters. Through my work, I've unlocked the potential of machine learning to intelligently optimize testing processes, enhancing efficiency by up to 73% while maintaining the highest standards of quality and safety. โ๏ธ With years of experience and collaboration with global OEMs, I've witnessed the transformative potential of machine learning in action. My recent work on the Next Test Recommender (NTR) system at Monolith showcases how AI-driven test plan optimization can significantly streamline R&D efforts. By identifying optimal testing sequences and reducing the number of tests needed, we've accelerated product development, enabling faster integration of advanced battery technologies into the electric vehicle ecosystem. Let's connect to explore how we can shape the future of electric mobility together and create a more sustainable and efficient automotive landscape. ๐ Read the full article on Electric & Hybrid Vehicle Technology International: https://lnkd.in/eKKxjiTR ๐ Connect with me for daily posts on science & technology! #machinelearning #ai #automotive #artificialintelligence #electricvehicle
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