The Role of AI-Driven Predictive Maintenance in Enhancing U.S. Defense Manufacturing Operations
Keywords:
Predictive Maintenance, Defense Manufacturing OperationsAbstract
Introduction Predictive maintenance is used in manufacturing to utilize data analytics, techniques, and tools to ensure the maintenance of the operational conditions of the manufacturing equipment (e.g., CNC machines, metal 3D printers) that produce parts, tools, and fixtures used in the production of critical weapons systems used by the Department of Defense (DoD). Machine maintenance can be costly, and unscheduled machine downtime can negatively impact the ability of a manufacturer to fulfill its contracts at the necessary lead times. Applying predictive maintenance to manufacturing equipment can minimize downtime and extend equipment life, resulting in an improvement in overall equipment effectiveness (OEE).
The importance of predictive maintenance on equipment in a manufacturing environment is quite clear. When advanced manufacturing equipment is offline for service or maintenance, it is not contributing to the manufacturer's output or throughput. Bringing advanced manufacturing equipment back online for production after downtime can be time-consuming when considering power-up cycles, equipment armoring, log-on time, identification of tool offsets and other settings for the work, first-article inspections, and release. Moreover, extrinsic factors such as motion or trailer availability can play into the optimal time to run a part and deliver to the final customer. The impact of a single printer failure is not limited to the lost production of parts but also can delay the planned schedule of an assembly, delivery, or test by multiple days. Consequently, a manufacturer is loath to offline equipment for maintenance. Instead, manufacturers opt to operate equipment despite it exhibiting signs of possible failure.
Downloads
References
Sengottaiyan, Krishnamoorthy, and Manojdeep Singh Jasrotia. "Relocation of Manufacturing Lines-A Structured Approach for Success." International Journal of Science and Research (IJSR) 13.6 (2024): 1176-1181.
Gayam, Swaroop Reddy. "Artificial Intelligence for Natural Language Processing: Techniques for Sentiment Analysis, Language Translation, and Conversational Agents." Journal of Artificial Intelligence Research and Applications 1.1 (2021): 175-216.
Nimmagadda, Venkata Siva Prakash. "Artificial Intelligence for Compliance and Regulatory Reporting in Banking: Advanced Techniques, Models, and Real-World Applications." Journal of Bioinformatics and Artificial Intelligence 1.1 (2021): 151-189.
Putha, Sudharshan. "AI-Driven Natural Language Processing for Voice-Activated Vehicle Control and Infotainment Systems." Journal of Artificial Intelligence Research and Applications 2.1 (2022): 255-295.
Sahu, Mohit Kumar. "Machine Learning Algorithms for Personalized Financial Services and Customer Engagement: Techniques, Models, and Real-World Case Studies." Distributed Learning and Broad Applications in Scientific Research 6 (2020): 272-313.
Kasaraneni, Bhavani Prasad. "Advanced Machine Learning Models for Risk-Based Pricing in Health Insurance: Techniques and Applications." Australian Journal of Machine Learning Research & Applications 1.1 (2021): 170-207.
Kondapaka, Krishna Kanth. "Advanced Artificial Intelligence Models for Predictive Analytics in Insurance: Techniques, Applications, and Real-World Case Studies." Australian Journal of Machine Learning Research & Applications 1.1 (2021): 244-290.
Kasaraneni, Ramana Kumar. "AI-Enhanced Pharmacoeconomics: Evaluating Cost-Effectiveness and Budget Impact of New Pharmaceuticals." Australian Journal of Machine Learning Research & Applications 1.1 (2021): 291-327.
Pattyam, Sandeep Pushyamitra. "AI-Driven Data Science for Environmental Monitoring: Techniques for Data Collection, Analysis, and Predictive Modeling." Australian Journal of Machine Learning Research & Applications 1.1 (2021): 132-169.
Kuna, Siva Sarana. "Reinforcement Learning for Optimizing Insurance Portfolio Management." African Journal of Artificial Intelligence and Sustainable Development 2.2 (2022): 289-334.
Gayam, Swaroop Reddy, Ramswaroop Reddy Yellu, and Praveen Thuniki. "Artificial Intelligence for Real-Time Predictive Analytics: Advanced Algorithms and Applications in Dynamic Data Environments." Distributed Learning and Broad Applications in Scientific Research 7 (2021): 18-37.
Nimmagadda, Venkata Siva Prakash. "Artificial Intelligence for Customer Behavior Analysis in Insurance: Advanced Models, Techniques, and Real-World Applications." Journal of AI in Healthcare and Medicine 2.1 (2022): 227-263.
Putha, Sudharshan. "AI-Driven Personalization in E-Commerce: Enhancing Customer Experience and Sales through Advanced Data Analytics." Journal of Bioinformatics and Artificial Intelligence 1.1 (2021): 225-271.
Sahu, Mohit Kumar. "Machine Learning for Personalized Insurance Products: Advanced Techniques, Models, and Real-World Applications." African Journal of Artificial Intelligence and Sustainable Development 1.1 (2021): 60-99.
Kasaraneni, Bhavani Prasad. "AI-Driven Approaches for Fraud Prevention in Health Insurance: Techniques, Models, and Case Studies." African Journal of Artificial Intelligence and Sustainable Development 1.1 (2021): 136-180.
Kondapaka, Krishna Kanth. "Advanced Artificial Intelligence Techniques for Demand Forecasting in Retail Supply Chains: Models, Applications, and Real-World Case Studies." African Journal of Artificial Intelligence and Sustainable Development 1.1 (2021): 180-218.
Kasaraneni, Ramana Kumar. "AI-Enhanced Portfolio Optimization: Balancing Risk and Return with Machine Learning Models." African Journal of Artificial Intelligence and Sustainable Development 1.1 (2021): 219-265.
Pattyam, Sandeep Pushyamitra. "AI-Driven Financial Market Analysis: Advanced Techniques for Stock Price Prediction, Risk Management, and Automated Trading." African Journal of Artificial Intelligence and Sustainable Development 1.1 (2021): 100-135.
Kuna, Siva Sarana. "The Impact of AI on Actuarial Science in the Insurance Industry." Journal of Artificial Intelligence Research and Applications 2.2 (2022): 451-493.
Nimmagadda, Venkata Siva Prakash. "Artificial Intelligence for Dynamic Pricing in Insurance: Advanced Techniques, Models, and Real-World Application." Hong Kong Journal of AI and Medicine 4.1 (2024): 258-297.
Paul, Debasish, Gunaseelan Namperumal, and Yeswanth Surampudi. "Optimizing LLM Training for Financial Services: Best Practices for Model Accuracy, Risk Management, and Compliance in AI-Powered Financial Applications." Journal of Artificial Intelligence Research and Applications 3.2 (2023): 550-588.
Namperumal, Gunaseelan, Akila Selvaraj, and Yeswanth Surampudi. "Synthetic Data Generation for Credit Scoring Models: Leveraging AI and Machine Learning to Improve Predictive Accuracy and Reduce Bias in Financial Services." Journal of Artificial Intelligence Research 2.1 (2022): 168-204.
Soundarapandiyan, Rajalakshmi, Praveen Sivathapandi, and Yeswanth Surampudi. "Enhancing Algorithmic Trading Strategies with Synthetic Market Data: AI/ML Approaches for Simulating High-Frequency Trading Environments." Journal of Artificial Intelligence Research and Applications 2.1 (2022): 333-373.
