AI EDAM-ARTIFICIAL INTELLIGENCE FOR ENGINEERING DESIGN ANALYSIS AND MANUFACTURING

Scope & Guideline

Bridging Engineering and AI for a Smarter Tomorrow

Introduction

Immerse yourself in the scholarly insights of AI EDAM-ARTIFICIAL INTELLIGENCE FOR ENGINEERING DESIGN ANALYSIS AND MANUFACTURING with our comprehensive guidelines detailing its aims and scope. This page is your resource for understanding the journal's thematic priorities. Stay abreast of trending topics currently drawing significant attention and explore declining topics for a full picture of evolving interests. Our selection of highly cited topics and recent high-impact papers is curated within these guidelines to enhance your research impact.
LanguageEnglish
ISSN0890-0604
PublisherCAMBRIDGE UNIV PRESS
Support Open AccessNo
CountryUnited Kingdom
TypeJournal
Convergefrom 1987 to 2024
AbbreviationAI EDAM / AI EDAM-Artif. Intell. Eng. Des. Anal. Manuf.
Frequency1 issue/year
Time To First Decision-
Time To Acceptance-
Acceptance Rate-
Home Page-
AddressEDINBURGH BLDG, SHAFTESBURY RD, CB2 8RU CAMBRIDGE, ENGLAND

Aims and Scopes

The journal AI EDAM focuses on the intersection of artificial intelligence and engineering design, analysis, and manufacturing. It aims to advance the understanding and application of AI methodologies in engineering contexts, enhancing efficiency, creativity, and effectiveness in design and manufacturing processes.
  1. Artificial Intelligence in Engineering Design:
    The journal emphasizes the integration of AI techniques, including machine learning and reinforcement learning, to enhance various stages of engineering design, from conceptualization to detailed design and manufacturing.
  2. Human-Robot Collaboration:
    Research on human-robot interaction, particularly in collaborative tasks such as disassembly and assembly, is a key focus area, aiming to improve efficiency and safety in engineering processes.
  3. User Experience and Cognitive Factors:
    The journal publishes studies that explore the relationship between user experience, cognitive processes, and design, contributing to the development of user-centered design methodologies.
  4. Data-Driven Design and Manufacturing:
    There is a consistent emphasis on the use of data-driven approaches, including ontology-based methods and machine learning algorithms, to optimize design processes and improve manufacturing outcomes.
  5. Sustainable and Intelligent Manufacturing:
    Research related to sustainability in manufacturing, including predictive maintenance and intelligent systems for resource allocation, highlights the journal's commitment to addressing contemporary challenges in the engineering field.
The journal has seen a rise in publications addressing innovative themes that reflect the current trends in engineering design and manufacturing, particularly those that leverage advanced technologies and interdisciplinary approaches.
  1. Augmented Reality in Design:
    Recent papers highlight the use of augmented reality in collaborative design processes, suggesting its growing importance for enhancing interaction and efficiency in engineering tasks.
  2. Machine Learning Applications:
    There is an increasing trend in applying machine learning techniques across various engineering domains, particularly for predictive analysis and optimization in manufacturing processes.
  3. Interdisciplinary Approaches:
    Emerging research emphasizes the intersection of cognitive science and engineering design, exploring how psychological factors influence creativity and decision-making in design projects.
  4. Ontology and Semantic Methods:
    The application of ontological frameworks to improve design processes and knowledge management is gaining traction, indicating a shift towards more structured and systematic approaches in engineering design.
  5. Sustainability and Eco-Design:
    A rising interest in sustainable design practices and methodologies that incorporate eco-friendly considerations into the engineering process is evident, reflecting broader societal trends towards sustainability.

Declining or Waning

While the journal has consistently focused on various themes, certain areas of research have shown a decline in prominence over recent years. This may indicate shifts in research priorities or advancements in methodologies that render some topics less critical.
  1. Traditional Manufacturing Techniques:
    Research focusing solely on conventional manufacturing processes without the integration of AI or data-driven methodologies has decreased, reflecting a shift towards more advanced, intelligent systems.
  2. Basic Design Methods:
    Papers centered on traditional design methodologies lacking AI integration are becoming less common, as there is a growing preference for innovative approaches that leverage modern technologies.
  3. General Literature Reviews:
    While literature reviews remain important, there appears to be a decline in publications that do not offer novel insights or methodologies, suggesting a trend towards more application-focused research.

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