Abstract

With the increasing number of digital technologies in production, more and more detailed product data can be collected, measured, and analyzed. Coupled, this forms the basis for future data services during the product life cycle. In this way, suppliers, OEMs, and vehicle users achieve a new quality and transparency of their respective internal value chains and enable completely new improvements. However, many of the current product data services only serve specific services or partial aspects of a vehicle with a digital twin but do not enable an overall continuous digital profile. One reason is the multitude of different systems in the automotive data landscape and along the entire product line, from product design to the end of life.

In addition, the vulnerability of the data increases with each touchpoint in the value chain and thus in the field use. Missing transparency and trust of data are also major challenges for companies and users beyond the vulnerability of blockchain-based in-vehicle data use and thus also hamper the potential of a new era of collaboration and cooperation structures in the use of new digital technologies in production. This research aims to conceptualize a design approach for continuous and reliable data for the overall digital product profile along the entire product life cycle. In doing so, a design and validation approach for an overall digital product profile based on the integrated automation of blockchain technology and machine learning is developed and the individual areas of action are demonstrated in a concrete demonstrator. Virtual product twins in combination with structural digital twins should enable the new quality of end-to-end traceability and defect prediction as part of the overall digital product profile for automotive OEM data services, supplier services, and customers.

Keywords

  • Keywords: active power filtering
  • artificial neuronal network
  • flying capacitor inverter
  • indirect matrix converter
  • fuzzy PI controller.

References

  1. 1. Vankayalapati, R. K. (2020). AI-Driven Decision Support Systems: The Role Of High-Speed Storage And Cloud Integration In Business Insights. Available at SSRN 5103815.
  2. 2. Sondinti, L. R. K., & Yasmeen, Z. (2022). Analyzing Behavioral Trends in Credit Card Fraud Patterns: Leveraging Federated Learning and Privacy-Preserving Artificial Intelligence Frameworks.
  3. 3. Kannan, S. (2022). The Role Of AI And Machine Learning In Financial Services: A Neural Networkbased Framework For Predictive Analytics And Customercentric Innovations. Migration Letters, 19(6), 985-1000.
  4. 4. Harish Kumar Sriram. (2022). AI-Driven Optimization of Intelligent Supply Chains and Payment Systems: Enhancing Security, Tax Compliance, and Audit Efficiency in Financial Operations. Mathematical Statistician and Engineering Applications, 71(4), 16729–16748. Retrieved from https://philstat.org/index.php/MSEA/article/view/2966
  5. 5. Chava, K. (2022). Redefining Pharmaceutical Distribution With AI-Infused Neural Networks: Generative AI Applications In Predictive Compliance And Operational Efficiency. Migration Letters, 19(S8), 1905-1917.
  6. 6. Maintenance Algorithms For Intelligent Network Systems: Leveraging Neural Networks To Predict And Optimize Performance In Dynamic Environments. Migration Letters, 19, 1949-1964.
  7. 7. Chakilam, C. (2022). Generative AI-Driven Frameworks for Streamlining Patient Education and Treatment Logistics in Complex Healthcare Ecosystems. Kurdish Studies. Green Publication. Kurdish Studies. Green Publication. https://doi. org/10.53555/ks. v10i2, 3719.
  8. 8. Nuka, S. T. (2022). The Role of AI Driven Clinical Research in Medical Device Development: A Data Driven Approach to Regulatory Compliance and Quality Assurance. Global Journal of Medical Case Reports, 2(1), 1275.
  9. 9. Burugulla, J. K. R. (2022). The Role of Cloud Computing in Revolutionizing Business Banking Services: A Case Study on American Express’s Digital Financial Ecosystem. Kurdish Studies. Green Publication. https://doi. org/10.53555/ks. v10i2, 3720.
  10. 10. Pamisetty, A. (2022). Enhancing Cloud native Applications WITH Ai AND Ml: A Multicloud Strategy FOR Secure AND Scalable Business Operations. Migration Letters, 19(6), 1268-1284.
  11. 11. Anil Lokesh Gadi. (2022). Transforming Automotive Sales And Marketing: The Impact Of Data Engineering And Machine Learning On Consumer Behavior. Migration Letters, 19(S8), 2009–2024. Retrieved from https://migrationletters.com/index.php/ml/article/view/11852
  12. 12. Someshwar Mashetty. (2022). Enhancing Financial Data Security And Business Resiliency In Housing Finance: Implementing AI-Powered Data Analytics, Deep Learning, And Cloud-Based Neural Networks For Cybersecurity And Risk Management. Migration Letters, 19(6), 1302–1818. Retrieved from https://migrationletters.com/index.php/ml/article/view/11741
  13. 13. Pandiri, L., & Chitta, S. (2022). Leveraging AI and Big Data for Real-Time Risk Profiling and Claims Processing: A Case Study on Usage-Based Auto Insurance. In Kurdish Studies. Green Publication. https://doi.org/10.53555/ks.v10i2.3760
  14. 14. Recharla, M., & Chitta, S. (2022). Cloud-Based Data Integration and Machine Learning Applications in Biopharmaceutical Supply Chain Optimization.
  15. 15. Nandan, B. P., & Chitta, S. (2022). Advanced Optical Proximity Correction (OPC) Techniques in Computational Lithography: Addressing the Challenges of Pattern Fidelity and Edge Placement Error. Global Journal of Medical Case Reports, 2(1), 58–75. Retrieved from https://www.scipublications.com/journal/index.php/gjmcr/article/view/1292
  16. 16. Srinivasarao Paleti. (2022). Adaptive AI In Banking Compliance: Leveraging Agentic AI For Real-Time KYC Verification, Anti-Money Laundering (AML) Detection, And Regulatory Intelligence. Migration Letters, 19(6), 1253–1267.
  17. 17. Pallav Kumar Kaulwar. (2022). Data-Engineered Intelligence: An AI-Driven Framework for Scalable and Compliant Tax Consulting Ecosystems. Kurdish Studies, 10(2), 774–788. https://doi.org/10.53555/ks.v10i2.3796
  18. 18. Koppolu, H. K. R. (2022). Advancing Customer Experience Personalization with AI-Driven Data Engineering: Leveraging Deep Learning for Real-Time Customer Interaction. Kurdish Studies. Green Publication. https://doi. org/10.53555/ks. v10i2, 3736.
  19. 19. Dodda, A. (2022). Strategic Financial Intelligence: Using Machine Learning to Inform Partnership Driven Growth in Global Payment Networks. International Journal of Scientific Research and Modern Technology, 1(12), 10–25. https://doi.org/10.38124/ijsrmt.v1i12.436
  20. 20. Jeevani Singireddy,. (2022). Leveraging Artificial Intelligence and Machine Learning for Enhancing Automated Financial Advisory Systems: A Study on AIDriven Personalized Financial Planning and Credit Monitoring. Mathematical Statistician and Engineering Applications, 71(4), 16711–16728. Retrieved from https://philstat.org/index.php/MSEA/article/view/2964
  21. 21. Challa, S. R. (2022). Optimizing Retirement Planning Strategies: A Comparative Analysis of Traditional, Roth, and Rollover IRAs in LongTerm Wealth Management. Universal Journal of Finance and Economics, 2(1), 1276.
  22. 22. Lakkarasu, P., & Kalisetty, S. Hybrid Cloud and AI Integration for Scalable Data Engineering: Innovations in Enterprise AI Infrastructure
  23. 23. Ganti, V. K. A. T., & Valiki, S. (2022). Leveraging Neural Networks for Real-Time Blood Analysis in Critical Care Units. KURDISH. Green Publication. https://doi. org/10.53555/ks. v10i2, 3642.
  24. 24. Kothapalli Sondinti, L. R., & Syed, S. (2022). The Impact of Instant Credit Card Issuance and Personalized Financial Solutions on Enhancing Customer Experience in the Digital Banking Era. Universal Journal of Finance and Economics, 1(1), 1223. Retrieved from https://www.scipublications.com/journal/index.php/ujfe/article/view/1223
  25. 25. Annapareddy, V. N. (2022). Innovative Aidriven Strategies For Seamless Integration Of Electric Vehicle Charging With Residential Solar Systems. Migration Letters, 19(6), 1221-1236.
  26. 26. Sriram, H. K. (2022). AI Neural Networks In Credit Risk Assessment: Redefining Consumer Credit Monitoring And Fraud Protection Through Generative AI Techniques. Migration Letters, 19(6), 1017-1032.
  27. 27. Komaragiri, V. B., & Edward, A. (2022). AI-Driven Vulnerability Management and Automated Threat Mitigation. International Journal of Scientific Research and Management (IJSRM), 10(10), 981-998.
  28. 28. Chakilam, C. (2022). Integrating Generative AI Models And Machine Learning Algorithms For Optimizing Clinical Trial Matching And Accessibility In Precision Medicine. Migration Letters, 19, 1918-1933.
  29. 29. Malempati, M. (2022). Machine Learning and Generative Neural Networks in Adaptive Risk Management: Pioneering Secure Financial Frameworks. Kurdish Studies. Green Publication. https://doi. org/10.53555/ks. v10i2, 3718.
  30. 30. Challa, K. (2022). Generative AI-Powered Solutions for Sustainable Financial Ecosystems: A Neural Network Approach to Driving Social and Environmental Impact. Mathematical Statistician and Engineering.
  31. 31. Anil Lokesh Gadi. (2022). Connected Financial Services in the Automotive Industry: AI-Powered Risk Assessment and Fraud Prevention. Journal of International Crisis and Risk Communication Research , 11–28. Retrieved from https://jicrcr.com/index.php/jicrcr/article/view/2965
  32. 32. Srinivasarao Paleti. (2022). Fusion Bank: Integrating AI-Driven Financial Innovations with Risk-Aware Data Engineering in Modern Banking. Mathematical Statistician and Engineering Applications, 71(4), 16785–16800.
  33. 33. Pallav Kumar Kaulwar. (2022). Securing The Neural Ledger: Deep Learning Approaches For Fraud Detection And Data Integrity In Tax Advisory Systems. Migration Letters, 19(S8), 1987–2008. Retrieved from https://migrationletters.com/index.php/ml/article/view/11851
  34. 34. Dodda, A., Lakkarasu, P., Singireddy, J., Challa, K., & Pamisetty, V. (2022). Optimizing Digital Finance and Regulatory Systems Through Intelligent Automation, Secure Data Architectures, and Advanced Analytical Technologies.
  35. 35. Operationalizing Intelligence: A Unified Approach to MLOps and Scalable AI Workflows in Hybrid Cloud Environments. (2022). International Journal of Engineering and Computer Science, 11(12), 25691-25710. https://doi.org/10.18535/ijecs.v11i12.4743
  36. 36. Vankayalapati, R. K., & Pandugula, C. (2022). AI-Powered Self-Healing Cloud Infrastructures: A Paradigm For Autonomous Fault Recovery. Migration Letters, 19(6), 1173-1187.
  37. 37. Kalisetty, S., Vankayalapati, R. K., Reddy, L., Sondinti, K., & Valiki, S. (2022). AI-Native Cloud Platforms: Redefining Scalability and Flexibility in Artificial Intelligence Workflows. Linguistic and Philosophical Investigations, 21(1), 1-15.
  38. 38. Sriram, H. K. (2022). Integrating generative AI into financial reporting systems for automated insights and decision support. Universal Journal of Finance and Economics, 2(1), 115–131. Retrieved from https://www.scipublications.com/journal/index.php/ujfe/article/view/1299
  39. 39. Malempati, M. (2022). AI Neural Network Architectures For Personalized Payment Systems: Exploring Machine Learning’s Role In Real-Time Consumer Insights. Migration Letters, 19(S8), 1934-1948.
  40. 40. Vamsee Pamisetty, Lahari Pandiri, Sneha Singireddy, Venkata Narasareddy Annapareddy, Harish Kumar Sriram. (2022). Leveraging AI, Machine Learning, And Big Data For Enhancing Tax Compliance, Fraud Detection, And Predictive Analytics In Government Financial Management. Migration Letters, 19(S5), 1770–1784. Retrieved from https://migrationletters.com/index.php/ml/article/view/11808
  41. 41. Kishore Challa, Jai Kiran Reddy Burugulla, Lahari Pandiri, Vamsee Pamisetty, Srinivasarao Paleti. (2022). Optimizing Digital Payment Ecosystems: Ai-Enabled Risk Management, Regulatory Compliance, And Innovation In Financial Services. Migration Letters, 19(S5), 1748–1769. Retrieved from https://migrationletters.com/index.php/ml/article/view/11807
  42. 42. Botlagunta Preethish Nadan. (2022). Emerging Technologies in Smart Computing, Sustainable Energy, and Next-Generation Mobility: Enhancing Digital Infrastructure, Secure Networks, and Intelligent Manufacturing. Mathematical Statistician and Engineering Applications, 71(4), 16749–16773. Retrieved from https://philstat.org/index.php/MSEA/article/view/2967
  43. 43. Kaulwar, P. K. (2022). The Role of Digital Transformation in Financial Audit and Assurance: Leveraging AI and Blockchain for Enhanced Transparency and Accuracy. Mathematical Statistician and Engineering Applications, 71 (4), 16679–16695.
  44. 44. Karaka, L. M. (2021). Optimising Product Enhancements Strategic Approaches to Managing Complexity. Available at SSRN 5147875.
  45. 45. Katnapally, N., Murthy, L., & Sakuru, M. (2021). Automating Cyber Threat Response Using Agentic AI and Reinforcement Learning Techniques. J. Electrical Systems, 17(4), 138-148.
  46. 46. Boppana, S. B., Moore, C. S., Bodepudi, V., Jha, K. M., Maka, S. R., & Sadaram, G. (2021). AI And ML Applications In Big Data Analytics: Transforming ERP Security Models For Modern Enterprises.
  47. 47. hinta, P. C. R., & Karaka, L. M.(2020). AGENTIC AI AND REINFORCEMENT LEARNING: TOWARDS MORE AUTONOMOUS AND ADAPTIVE AI SYSTEMS.
  48. 48. Velaga, V. (2022). Enhancing Supply Chain Efficiency and Performance Through ERP Optimization Strategies.