10/01/2026 Call for Paper

The AICE-2026 aims to create a premier platform for researchers, industry professionals, and innovators to engage and share cutting edge developments and practical applications in the integration of artificial intelligence with concurrent engineering. This conference seeks to unite established and emerging researchers, academicians, industry experts, and thought leaders from around the globe to exchange ideas, discuss advancements, and explore future trends in AI enhanced product design and development. The objective is to nurture collaboration and drive innovation by encouraging the dissemination of original, unpublished research and real-world applications that address the challenges and opportunities at the intersection of AI and concurrent engineering.

The contributions across a broad range of topics, including but not limited to the following key themes:

Theme 1: Intelligent and Scalable Concurrent Engineering Frameworks

1. • Cross-functional and multi-domain design integration strategies
2. • Parallel engineering of design, validation, and manufacturing processes
3. • Orchestration of distributed and globally networked engineering teams
4. • Collaborative digital ecosystems for synchronous product development
5. • Scaling concurrent engineering in complex industrial systems: lessons learned
6. • Next-generation real-time collaboration environments for engineering design
7. • Knowledge capture, reuse, and intelligence in concurrent workflows
8. • Process transformation and reconfiguration for accelerated development
9. • End-to-end digital thread connectivity across engineering stages
10. • Empirical case studies of large-scale concurrent engineering success
11. • AI-assisted coordination mechanisms in concurrent engineering systems
12. • Human–system interaction models in collaborative engineering platforms

Theme 2: AI-Augmented Design Intelligence and Optimization

1. • Intelligent calibration and tuning of complex design parameters
2. • Machine learning approaches for multi-criteria design optimization
3. • Generative and evolutionary design using deep learning models
4. • Reinforcement learning for adaptive and self-improving design cycles
5. • Data-centric simulation and performance prediction frameworks
6. • Predictive intelligence for early-stage design feasibility evaluation
7. • Neural network models for exploring high-dimensional design trade-offs
8. • Virtual prototyping driven by AI-based inference engines
9. • Configuration optimization and variant management using AI
10. • Hybrid integration of AI-driven and physics-based design methodologies
11. • Explainable AI in engineering design decision-making
12. • AI-assisted sustainability and eco-design optimization

Theme 3: Adaptive Monitoring, Control Intelligence, and Predictive Systems

1. • Streaming sensor analytics for continuous design and process feedback
2. • Predictive maintenance intelligence and AI-enabled quality assurance
3. • Self-adaptive control architectures in advanced manufacturing systems
4. • Closed-loop learning via intelligent digital twin feedback mechanisms
5. • AI-driven anomaly detection and root-cause diagnostics
6. • Machine learning models for real-time engineering decision support
7. • Intelligent control strategies in concurrent engineering environments
8. • Cyber–physical convergence for responsive and resilient production
9. • Multi-source data fusion for real-time operational intelligence
10. • Autonomous system monitoring for continuous performance enhancement
11. • Edge AI and low-latency analytics for time-critical engineering systems
12. • Resilience engineering and fault-tolerant control using AI

Theme 4: Data-Centric Collaboration and Intelligent Digital Twin Ecosystems

1. • AI-enabled integration of heterogeneous engineering data landscapes
2. • Cloud-native simulation environments for collaborative engineering
3. • Intelligent digital twins for continuous design iteration and validation
4. • AI-driven synchronization of multi-disciplinary engineering activities
5. • Unified analytics platforms for full product lifecycle intelligence
6. • Knowledge discovery and reasoning systems for engineering collaboration
7. • Decision-aware collaborative platforms with embedded AI support
8. • Big data architectures for concurrent engineering analytics
9. • Advanced digital thread communication and interoperability protocols
10. • Immersive collaboration using virtual and augmented reality
11. • Self-evolving digital twins with autonomous learning capabilities
12. • Trust, transparency, and governance in digital twin ecosystems

Theme 5: Industrial Transformation and Future Pathways in AI-Integrated Engineering

1. • Industry-driven case studies of AI-enabled concurrent engineering adoption
2. • Breakthrough innovations in autonomous and intelligent design systems
3. • Sustainable, resilient, and circular product development through AI
4. • Industrial-scale deployment strategies for AI-driven engineering solutions
5. • Roadmaps for enterprise-wide digital and AI transformation
6. • Convergence of simulation, prototyping, manufacturing, and analytics
7. • AI-enabled adaptive manufacturing and smart factory frameworks
8. • Robotics, autonomy, and human–machine teaming in engineering systems
9. • Leveraging Industry 4.0 and beyond for lifecycle optimization
10. • Cross-industry applications of AI in next-generation engineering design
11. • Integration of generative AI and large language models in engineering workflows
12. • Transition toward Industry 5.0: human-centric and ethical engineering systems