Artificial Intelligence for Engineering Design - Impact Factor

AI EDAM-Artificial Intelligence for Engineering Design Analysis and Manufacturing - Cambridge University Press | 2024 Impact Factor:2.3 | Cite Score:4.9 | Q2

AI EDAM-Artificial Intelligence for Engineering Design Analysis and Manufacturing Journal

Impact Factor and Journal Rank of AI EDAM-Artificial Intelligence for Engineering Design Analysis and Manufacturing

About: AI EDAM - Artificial Intelligence for Engineering Design, Analysis and Manufacturing publishes original articles that explore the significant theories and applications of AI in engineering. The journal aims to advance research and development in AI as applied to engineering disciplines, covering planning, design, analysis, simulation, manufacturing, assembly, process planning, scheduling, optimization, and more.
Objectives
The primary objectives of the journal include:
Advancing AI Theory and Applications: To publish original research that contributes to the theoretical foundations and practical applications of AI in engineering.
Interdisciplinary Approach: To foster interdisciplinary research by integrating AI with various branches and phases of engineering, including design, analysis, manufacturing, and assembly.
State-of-the-Art Research: To showcase the latest advancements in AI methodologies such as qualitative reasoning, spatial reasoning, simulation, optimization, and cognitive modeling as applied to engineering challenges.
Key Topics Covered
AI EDAM covers a broad spectrum of topics related to AI in engineering, including:
Analysis and Evaluation: AI techniques for analyzing and evaluating engineering systems and processes.
Selection and Configuration: AI-driven methods for selecting and configuring components or systems.
Design and Optimization: AI applications in design optimization and numerical analysis.
Manufacturing and Assembly: AI approaches to enhance manufacturing processes, assembly operations, and process planning.
Concurrent Engineering: AI-supported methodologies for concurrent engineering to streamline product development cycles.
Multi-Agent Systems: Applications of AI in distributed systems and multi-agent collaboration.
Cognitive Modeling and Learning: AI models and algorithms for cognitive tasks, learning, and creativity in engineering contexts.
Impact and Significance
AI EDAM plays a crucial role in:
Advancing Engineering Practices: By promoting the integration of AI technologies to improve efficiency, accuracy, and innovation in engineering design and manufacturing.
Knowledge Dissemination: By providing a platform for researchers and practitioners to share cutting-edge research and practical applications of AI.
Industry Relevance: By addressing real-world engineering challenges through AI-driven solutions, impacting sectors such as automotive, aerospace, manufacturing, and more.
Education and Training: By contributing to the education of future engineers and AI practitioners through insights into AI applications in engineering.
Global Collaboration: By fostering international collaboration among researchers and industry professionals to accelerate technological advancements in AI and engineering.

Journal Home: Journal Homepage

Editor-in-Chief: Professor Amaresh Chakrabarti

Scope: AI EDAM - Artificial Intelligence for Engineering Design, Analysis and Manufacturing is a peer-reviewed journal that focuses on the application of artificial intelligence (AI) techniques in various aspects of engineering design, analysis, and manufacturing processes. The journal covers a wide range of topics within AI and engineering, including:
AI Applications in Design: Integration of AI techniques such as machine learning, neural networks, and expert systems in engineering design processes.
AI for Analysis: Use of AI methodologies for analysis tasks including simulation, optimization, and decision support in engineering.
AI in Manufacturing: Applications of AI in manufacturing processes, including process optimization, quality control, and automation.
Knowledge-Based Systems: Development and implementation of knowledge-based systems and AI tools for engineering applications.
Intelligent Decision Support Systems: Design and development of intelligent systems to assist engineers in decision-making across various engineering domains.
Computer-Aided Design (CAD) and AI: Integration of AI techniques in computer-aided design tools for enhancing design efficiency and creativity.
Applications in Robotics and Automation: AI applications in robotics, automation, and control systems for improving manufacturing processes and systems.