JOURNAL OF INTELLIGENT MANUFACTURING

Scope & Guideline

Pioneering Knowledge in Intelligent Manufacturing and AI

Introduction

Welcome to your portal for understanding JOURNAL OF INTELLIGENT MANUFACTURING, featuring guidelines for its aims and scope. Our guidelines cover trending and emerging topics, identifying the forefront of research. Additionally, we track declining topics, offering insights into areas experiencing reduced scholarly attention. Key highlights include highly cited topics and recently published papers, curated within these guidelines to assist you in navigating influential academic dialogues.
LanguageEnglish
ISSN0956-5515
PublisherSPRINGER
Support Open AccessNo
CountryNetherlands
TypeJournal
Convergefrom 1990 to 2024
AbbreviationJ INTELL MANUF / J. Intell. Manuf.
Frequency8 issues/year
Time To First Decision-
Time To Acceptance-
Acceptance Rate-
Home Page-
AddressVAN GODEWIJCKSTRAAT 30, 3311 GZ DORDRECHT, NETHERLANDS

Aims and Scopes

The Journal of Intelligent Manufacturing focuses on the intersection of advanced manufacturing technologies and artificial intelligence, emphasizing the role of machine learning, digital twins, and smart manufacturing systems in enhancing industrial processes.
  1. Artificial Intelligence and Machine Learning in Manufacturing:
    The journal publishes research on the application of AI and machine learning techniques to optimize manufacturing processes, improve predictive maintenance, and enhance quality control.
  2. Digital Twin Technology:
    A significant focus is placed on the development and application of digital twin technologies, which facilitate real-time monitoring and simulation of manufacturing processes.
  3. Human-Robot Collaboration:
    Research in this area explores the integration of collaborative robots (cobots) in manufacturing environments, enhancing safety and productivity through intelligent systems.
  4. Quality Control and Defect Detection:
    The journal features studies on advanced methods for quality assurance, including the use of machine vision and deep learning for defect detection in various manufacturing processes.
  5. Sustainable Manufacturing Practices:
    There is a growing emphasis on sustainability, with research addressing energy efficiency, waste reduction, and the environmental impact of manufacturing processes.
  6. Supply Chain Optimization:
    The journal covers methodologies for optimizing supply chain management through intelligent systems, including the use of AI for demand forecasting and resource allocation.
Recent publications in the Journal of Intelligent Manufacturing reveal several emerging themes that are gaining traction among researchers. These trends indicate a shift towards more innovative and integrated approaches within the field.
  1. Explainable AI in Manufacturing:
    There is a growing interest in developing explainable AI systems that enhance transparency and trust in automated decision-making processes within manufacturing.
  2. Integration of IoT and Smart Systems:
    The convergence of the Internet of Things (IoT) with manufacturing processes is becoming increasingly prominent, enabling real-time data collection and analysis for smarter operations.
  3. Resilient and Adaptive Manufacturing Systems:
    Research is trending towards creating manufacturing systems that can adapt to changes in demand and supply chain disruptions, emphasizing flexibility and resilience.
  4. Data-Driven Process Optimization:
    There is an increasing focus on leveraging big data analytics to optimize manufacturing processes, improve efficiency, and reduce costs through data-driven insights.
  5. Sustainable and Circular Manufacturing:
    Emerging themes include sustainable practices and circular economy principles in manufacturing, addressing environmental concerns and resource efficiency.

Declining or Waning

While the Journal of Intelligent Manufacturing continues to evolve, certain research themes are becoming less prominent. This section highlights those areas that appear to be waning in focus.
  1. Traditional Manufacturing Techniques:
    Research on conventional manufacturing methods is declining as the focus shifts towards integrating advanced technologies like AI and automation.
  2. Simple Automation Solutions:
    The emphasis on basic automation solutions is waning in favor of more sophisticated, intelligent systems that leverage machine learning and data analytics.
  3. Hardware-Centric Approaches:
    There is a noticeable decline in research centered solely on hardware improvements, with a greater focus now on software-driven solutions and smart systems.
  4. Static Quality Control Methods:
    Static methods of quality control are losing traction as dynamic, real-time monitoring solutions gain prominence, reflecting a shift towards more proactive quality management.

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