Quality Engineering

Scope & Guideline

Exploring the frontiers of quality assurance and management.

Introduction

Delve into the academic richness of Quality Engineering with our guidelines, detailing its aims and scope. Our resource identifies emerging and trending topics paving the way for new academic progress. We also provide insights into declining or waning topics, helping you stay informed about changing research landscapes. Evaluate highly cited topics and recent publications within these guidelines to align your work with influential scholarly trends.
LanguageEnglish
ISSN0898-2112
PublisherTAYLOR & FRANCIS INC
Support Open AccessNo
CountryUnited States
TypeJournal
Convergefrom 1970 to 1972, 1974, from 1988 to 2024
AbbreviationQUAL ENG / Qual. Eng.
Frequency4 issues/year
Time To First Decision-
Time To Acceptance-
Acceptance Rate-
Home Page-
Address530 WALNUT STREET, STE 850, PHILADELPHIA, PA 19106

Aims and Scopes

The journal 'Quality Engineering' focuses on advancing knowledge and methodological practices in the field of quality assurance and engineering. It encompasses a wide array of research areas that are critical for improving quality in various industrial applications.
  1. Statistical Process Control (SPC) and Quality Control Techniques:
    Research emphasizing the development and optimization of statistical methods for monitoring and controlling manufacturing processes to ensure product quality.
  2. Reliability Engineering and Maintenance Optimization:
    Studies that explore methodologies for predicting and enhancing the reliability of systems, including maintenance strategies and failure analysis.
  3. Experimental Design and Analysis:
    Innovative approaches to experimental design, including response surface methodology and design of experiments (DOE), aimed at optimizing processes and product quality.
  4. Machine Learning and Artificial Intelligence Applications:
    Application of advanced computational techniques, such as deep learning and reinforcement learning, to enhance quality control and process optimization in manufacturing.
  5. Quality 4.0 and Industry 4.0 Integration:
    Exploration of the intersection between quality management and modern industrial technologies, including digital twins and smart manufacturing.
  6. Risk Assessment and Decision-Making in Quality Engineering:
    Research focused on methodologies for assessing risks and making informed decisions in quality engineering contexts, often incorporating uncertainty and variability.
The journal 'Quality Engineering' has seen a shift towards several emerging themes that reflect contemporary challenges and advancements in the field. The following points outline these trending areas.
  1. Integration of AI and Machine Learning in Quality Engineering:
    There is a growing trend towards utilizing artificial intelligence and machine learning techniques for predictive maintenance, quality monitoring, and process optimization, indicating a significant shift in methodological approaches.
  2. Quality 4.0 and Digital Transformation:
    Research focusing on Quality 4.0 encompasses the integration of digital technologies in quality management, reflecting the industry's movement towards smart manufacturing and the Internet of Things (IoT).
  3. Sustainability and Environmental Quality Management:
    Emerging themes include the incorporation of sustainability considerations into quality engineering practices, highlighting the importance of environmental impacts in production processes.
  4. Advanced Statistical Methods and Big Data Analytics:
    The trend towards utilizing big data analytics and advanced statistical methodologies for quality improvement reflects the industry's need to manage and analyze large volumes of data effectively.
  5. Uncertainty Quantification and Risk Management:
    An increasing focus on uncertainty quantification in engineering processes and risk management practices is evident, as industries seek to better understand and mitigate risks associated with quality.

Declining or Waning

As the field of quality engineering evolves, certain themes appear to be less frequently addressed in recent publications. This section highlights those declining or waning scopes.
  1. Traditional Six Sigma Methodologies:
    While Six Sigma remains a foundational concept in quality management, its prominence in recent literature has diminished, potentially overshadowed by newer methodologies such as Quality 4.0.
  2. Basic Control Chart Techniques:
    Basic statistical process control techniques are becoming less common as more advanced and complex methods are developed, indicating a shift towards innovative monitoring approaches.
  3. Conventional Reliability Testing Methods:
    There is a noticeable decline in studies focusing solely on traditional reliability testing methods, as researchers are increasingly integrating predictive analytics and machine learning into reliability assessments.
  4. Standard Quality Auditing Practices:
    The frequency of studies solely dedicated to conventional auditing practices has decreased, possibly due to a shift towards more integrated and automated quality management systems.

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