International Conference on AI and Concurrent Engineering (AICE 2025) to be held at IIMT, Bhubaneswar, India, during August 23rd-24th, 2025 invites original papers from the academicians, researchers and industry practitioners from the fields of AI and Concurrent Engineering. The primary goal of AICE 2025 aims at attracting scholars and professionals to present and publish up-to-date ideas and innovation in all areas related to Concurrent Engineering in various industries and in driving the new-generation economy, society, and government at large.

The scope of the conference has been kept wide but is not limited to:
 

Theme 1: Advanced Concurrent Engineering Practices

1. • Integrated multi-disciplinary design methodologies

2. • Simultaneous design, testing, and production planning

3. • Workflow synchronization in distributed engineering teams

4. • Collaborative platforms for parallel product development

5. • Best practices and challenges in scaling concurrent engineering

6. • Real-time collaboration tools for engineering design

7. • Knowledge management in concurrent engineering environments

8. • Process re-engineering for enhanced product development

9. • Digital thread integration across design stages

10. • Case studies on successful concurrent engineering implementations

Theme 2: AI-Driven Design and Optimization in Engineering

1. • AI-enabled tuning of design parameters

2. • Machine learning for multi-objective design optimization

3. • Deep learning-based generative design for concept creation

4. • Reinforcement learning for iterative design improvements

5. • Data-driven simulation for performance forecasting

6. • Predictive analytics for design feasibility assessments

7. • Neural network applications in exploring design trade-offs

8. • AI algorithms for virtual prototyping

9. • Optimization of product configurations through AI

10. • Integration of AI with traditional design methods

Theme 3: Real-Time Monitoring, Adaptive Control, and Predictive Analytics

1. • Real-time sensor data analysis for dynamic design feedback

2. • AI for predictive maintenance and quality assurance

3. • Adaptive control systems in production environments

4. • Feedback loops using digital twin integration

5. • AI-based process anomaly detection and troubleshooting

6. • Machine learning for real-time decision support

7. • Intelligent control systems in concurrent engineering

8. • Cyber-physical integration for responsive manufacturing

9. • Data fusion techniques for real-time process analytics

10. • Automated system monitoring for continuous improvement

Theme 4: Data-Driven Collaboration and Digital Twin Technologies

1. • Integration of heterogeneous design data through AI

2. • Cloud-based simulation and collaborative design tools

3. • Digital twins for real-time design iteration and validation

4. • AI-enhanced synchronization across multi-disciplinary teams

5. • Unified analytics frameworks for product lifecycle management

6. • AI-driven knowledge management in engineering design

7. • Collaborative platforms with integrated decision support

8. • Big data analytics in concurrent engineering workflows

9. • Enhanced communication protocols for digital threads

10. • Virtual and augmented reality for collaborative design reviews

Theme 5: Emerging Trends and Industrial Applications in AI-Integrated Engineering

1. • Case studies on AI integration in concurrent engineering projects

2. • Disruptive innovations in automated design processes

3. • Sustainable and resilient design via AI-driven strategies

4. • Scaling AI solutions for industrial product development

5. • Future directions in integrated digital transformation

6. • Convergence of simulation, prototyping, and production analytics

7. • AI for adaptive manufacturing and smart factory solutions

8. • Robotics and autonomous systems in concurrent engineering

9. • Leveraging Industry 4.0 for enhanced product lifecycle management

10. • Cross-sector applications of AI in modern engineering design