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Sponsored AI Networking Cookbook: Practical recipes for AI-assisted network automation and development AI Networking Cookbook: Practical recipes for AI-assisted network automation and development

Author

Mohammed Asif Khan

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2 papers indexed here

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Open access 2018

AI-Based Dynamic Reconfiguration in Modular Smart Manufacturing Cells

The evolution of Industry 4.0 has brought forth an increasing demand for flexibility, adaptability, and intelligence in manufacturing systems. Modular smart manufacturing cells, with their inherent reconfigurability, are becoming essential components in modern production environments. This paper presents an AI-based framework for dynamic reconfiguration of such cells, enabling rapid adaptation to changing production demands, equipment failures, and optimization goals. Leveraging machine learning and real-time data analytics, the proposed system autonomously identifies optimal reconfiguration strategies with minimal human intervention. A case study is presented to validate the framework, demonstrating significant improvements in operational efficiency and system responsiveness. The results highlight the transformative potential of AI in achieving truly autonomous and resilient manufacturing systems.

Mohammed Asif Khan, Shalini Gupta · 0 citations
Open access 2020

Generative AI Models for On-Demand Design in Lights-Out Manufacturing Facilities

The rise of lights-out manufacturing—facilities operating autonomously without human intervention—has redefined the landscape of industrial automation. However, these systems often rely on pre-designed parts and rigid workflows, limiting their adaptability. This paper explores the integration of generative AI models into lights-out manufacturing environments to enable on-demand, real-time product design. By leveraging the capabilities of neural networks such as Generative Adversarial Networks (GANs) and diffusion-based models, manufacturing systems can autonomously create, evaluate, and iterate product designs without human input. We propose an architectural framework for integrating AI-driven design generation with digital twin-based production pipelines, highlight key use cases such as rapid prototyping and design optimization, and assess the technical challenges associated with quality control, validation, and data integrity. Our analysis suggests that generative AI can dramatically enhance the responsiveness and efficiency of fully automated facilities, marking a significant step toward fully autonomous product lifecycles.

Mohammed Asif Khan, Shalini Gupta · 0 citations

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