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Introduction

 

Artificial intelligence (AI) has become a major field in computer science, with applications ranging from expert systems to evolutionary algorithms and deep learning. These AI and machine learning (ML) systems can be comprised of several different techniques, each suited for specific tasks. The evolution of AI from mimicking expert human decisions to utilizing heuristics and deep learning has significantly impacted various industries, including manufacturing​​.

 

Machine Learning Paradigms and Techniques

 

Machine learning paradigms include supervised learning, unsupervised learning, and reinforcement learning, each with its own set of applications and techniques. Supervised learning is particularly useful for tasks like image, voice, and object recognition, where large labeled datasets are available. Unsupervised learning, on the other hand, discovers hidden patterns in data without human intervention and is used for tasks such as clustering and anomaly detection. Reinforcement learning, which does not require a pre-existing training dataset, is utilized in robotics and optimization, learning from feedback to determine an optimal path to a goal​​.

Machine learning techniques, such as neural networks, decision trees, support vector machines, clustering algorithms, generative adversarial networks (GANs), and scientific machine learning (SciML), are employed across various learning paradigms. Neural networks, for example, are powerful models used in supervised, unsupervised, and reinforcement learning, while decision trees are primarily used in supervised learning for tasks involving classification and regression​​.

Representative AI/ML applications in the manufacturing industry. Figure Source: “A review of artificial intelligence applications in manufacturing operations” in Journal of Advanced Manufacturing and Processing · May 2023 by Siby Jose Plathottam and Chukwunwike O Iloeje

 

AI/ML Applications in Manufacturing

 

AI/ML applications in manufacturing can be categorized into operations, design, and automation:

 

1. Operations: This includes predictive maintenance, quality assurance, energy consumption forecasting, and supply chain management. Predictive maintenance uses AI/ML to analyze sensor data and anticipate equipment failures, thereby reducing downtime and financial losses. Quality assurance employs models like CNNs to detect product imperfections, enhancing customer satisfaction. Energy consumption forecasting helps in reducing environmental impact and improving sustainability. Supply chain management leverages predictive analytics and real-time data analysis to optimize inventory levels and production planning​​.
2. Design: AI/ML aids in process and product design through techniques like generative design and SciML. Generative design uses AI to explore a wide variety of design options based on user-provided requirements, while SciML combines conventional ML models with known physical laws to perform high-performance simulations, speeding up the design process​​.
3. Automation and Human-Machine Interaction: Incorporating AI into industrial robots allows for a more efficient cooperation between human workers and robots, adapting to variable human behavior while maintaining safety. AI/ML can also improve worker and equipment safety through intelligent access control systems and mitigate cybersecurity risks​​.

Common categories for various aspects of machine learning, grouped into paradigms, techniques, tasks, and relevant manufacturing industry applications. Figure Source: “A review of artificial intelligence applications in manufacturing operations” in Journal of Advanced Manufacturing and Processing · May 2023 by Siby Jose Plathottam and Chukwunwike O Iloeje

 

Challenges in Implementing AI/ML in Manufacturing

 

The integration of AI/ML into manufacturing faces several challenges, including data acquisition, energy consumption, security and privacy concerns, implementation difficulties, and decision validation. Acquiring large amounts of data for training models can be challenging due to the proprietary nature of manufacturing equipment. Energy consumption during training runs and inference steps can be significant, impacting the environment. Security and privacy concerns arise when accessing data on servers located within plant control rooms. Implementing AI solutions can be difficult due to the need for a foundation of infrastructure and personnel. Decision validation is crucial as the lack of interpretability of AI/ML model outputs makes it difficult to use for planning​​.

 

Trends and Opportunities in AI/ML for Manufacturing

 

Current trends suggest that AI/ML-based solutions supplement human labor rather than provide complete automation. Opportunities for AI/ML in manufacturing include the development of high-quality synthetic data, improving the energy efficiency of AI/ML hardware accelerators, enhancing the computing capabilities of edge computing hardware, and building trust in AI/ML decisions through explainable AI and concepts like “humble AI”​​.

In conclusion, AI and ML are transforming the manufacturing industry by optimizing operations, aiding in design, and enhancing automation. However, challenges such as data acquisition, energy consumption, and security need to be addressed to fully realize the potential of these technologies. As the industry continues to evolve, the integration of AI/ML into manufacturing processes will play a crucial role in driving innovation and efficiency.

 

References

 

1. Siby Jose Plathottam, Chukwunwike O Iloeje, “A review of artificial intelligence applications in manufacturing operations” in Journal of Advanced Manufacturing and Processing · May 2023
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6. P. Brosset, A. L. Thieullent, S. Patsko, P. Ravix, “Scaling AI in Manufacturing Operations: A Practitioners’ Perspective,” Capgemini Research Institute, Paris, 2019, https://www.capgemini.com/wp-content/uploads/2019/12/AI-in-manufacturing-operations.pdf.
7. S. Fahle, C. Prinz, B. Kuhlenkötter, Proc. CIRP, 2020, 93, 413.
8. R. Cioffi, M. Travaglioni, G. Piscitelli, A. Petrillo, F. De Felice, Sustainability, 2020, 12(2), 492.
9. A. Rizzoli, “7 Out-of-the-Box Applications of AI in Manufacturing,” V7 Labs Blog, 2022, https://www.v7labs.com/blog/ai-in-manufacturing.
10. Plutoshift, “Breaking Ground on Implementing AI: Instituting Strategic AI Programs — From Promise to Productivity,” Plutoshift, Palo Alto, 2019, https://plutoshift.com/wp-content/uploads/2022/02/plutoshift-breaking-ground-on-implementing-ai.pdf.
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12. V. Kanade, “Narrow AI vs. General AI vs. Super AI: Key Comparisons,” SpiceWorks, 2022, https://www.spiceworks.com/tech/artificial-intelligence/articles/narrow-general-super-ai-difference/.
13. “Machine learning,” Wikipedia, Machine learning, 2022, https://en.wikipedia.org/w/index.php?title=Machine_learning&oldid=1084622324.
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15. IBM Cloud Education, “What is Supervised Learning?” IBM Cloud Education, 2021, https://www.ibm.com/cloud/learn/supervised-learning.
16. javapoint, “Unsupervised Machine Learning — Javatpoint,” javapoint, 2022, https://www.javatpoint.com/unsupervised-machine-learning.
17. IBM Cloud Team, “Supervised vs. Unsupervised Learning: What’s the Difference?” IBM Cloud Team, 2021, https://www.ibm.com/cloud/blog/supervised-vs-unsupervised-learning.
18. V. François-Lavet, P. Henderson, R. Islam, M. G. Bellemare, J. Pineau, Found Trends Mach. Learn. Mach. Learn., 2018, 11(3–4), 219.
19. S. J. Plathottam, B. Richey, G. Curry, J. Cresko, C. O. Iloeje, J. Adv. Manuf. Process., 2021, 3(2), e10079.
20. A. Mirhoseini, A. Goldie, M. Yazgan, J. Jiang, E. Songhori, S. Wang, Y.-J. Lee, E. Johnson, O. Pathak, S. Bae, A. Nazi, J. Pak, A. Tong, K. Srinivasa, W. Hang, E. Tuncer, A. Babu, Q. V. Le, J. Laudon

Read more info:- https://medium.com/@mmp3071/transform-or-be-left-behind-the-ai-and-ml-in-manufacturing-revolution-awaits-153053b0364d

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