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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. 📈📈 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 🚗🤝 Link in the comments!

𝐎𝐩𝐭𝐢𝐦𝐢𝐳𝐢𝐧𝐠 𝐩𝐫𝐨𝐜𝐞𝐬𝐬𝐞𝐬 𝐨𝐧 𝐭𝐡𝐞 𝐬𝐡𝐨𝐩 𝐟𝐥𝐨𝐨𝐫 𝐡𝐚𝐯𝐞 𝐛𝐞𝐞𝐧 𝐚 𝐜𝐨𝐧𝐬𝐭𝐚𝐧𝐭 𝐟𝐨𝐫 𝐚𝐮𝐭𝐨𝐦𝐚𝐤𝐞𝐫𝐬 𝐨𝐯𝐞𝐫 𝐭𝐡𝐞 𝐲𝐞𝐚𝐫𝐬 𝐛𝐮𝐭 𝐡𝐚𝐯𝐞 𝐭𝐡𝐞𝐢𝐫 𝐭𝐞𝐜𝐡𝐧𝐨𝐥𝐨𝐠𝐢𝐞𝐬 𝐚𝐧𝐝 𝐬𝐭𝐫𝐚𝐭𝐞𝐠𝐢𝐞𝐬 𝐩𝐥𝐚𝐭𝐞𝐚𝐮𝐞𝐝? Bill Graca, Senior Director of Automotive at Capgemini Invent North America, urges auto manufacturers to rethink, challenge long-established processes, and leverage 𝐚𝐫𝐭𝐢𝐟𝐢𝐜𝐢𝐚𝐥 𝐢𝐧𝐭𝐞𝐥𝐥𝐢𝐠𝐞𝐧𝐜𝐞 and 𝐦𝐚𝐜𝐡𝐢𝐧𝐞 𝐥𝐞𝐚𝐫𝐧𝐢𝐧𝐠 to drive more 𝐩𝐫𝐨𝐝𝐮𝐜𝐭𝐢𝐯𝐢𝐭𝐲, 𝐞𝐟𝐟𝐢𝐜𝐢𝐞𝐧𝐜𝐲 and 𝐨𝐯𝐞𝐫𝐚𝐥𝐥 𝐞𝐪𝐮𝐢𝐩𝐦𝐞𝐧𝐭 𝐞𝐟𝐟𝐞𝐜𝐭𝐢𝐯𝐞𝐧𝐞𝐬𝐬. 🚗 Read more via IndustryWeek: ➡️ https://bit.ly/3Lr3d98 🔍 #GetTheFutureYouWant #Automotive #Manufacturing #ArtificialIntelligence #MachineLearning #Gigafactory

👉 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

“AI at Bosch” 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 Bosch’s timely #AI use cases: - Automated visual quality inspection - Real-time recommendation engine #AI #artificialintelligence #technology #engineering #usecases

How AI is impacting Automotive and Manufacturing Industries

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

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

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. 🔍 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. 📊 Results -> Anomaly Detection Performance: F-score 0.79 -> MA-VAE outperformed several other VAE-based anomaly detection models (F-score ≤0.53) 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. 📖 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 Bäck, Anna Kononova License: CC BY 4.0

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

#MachineLearning to Reduce Battery Testing 📉🔋 🚀 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. 🔌 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. ⚙️ 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. 🌎 Read the full article on Electric & Hybrid Vehicle Technology International: https://lnkd.in/eKKxjiTR 🔗 Connect with me for daily posts on science & technology! #machinelearning #ai #automotive #artificialintelligence #electricvehicle