Mixture of Human and Machine Intelligence in Digital Manufacturing, 2nd Edition

A special issue of Designs (ISSN 2411-9660). This special issue belongs to the section "Smart Manufacturing System Design".

Deadline for manuscript submissions: 30 June 2026 | Viewed by 8244

Special Issue Editors

Department of Computer Science, School of Science, Loughborough University, Loughborough LE11 3TU, UK
Interests: human-in-the-loop system design; digital intervention; cyber security; artificial intelligence; decision making
Special Issues, Collections and Topics in MDPI journals
Digital and Intelligent Technology Department, Beijing Bohua Xinzhi Technology Co., Ltd., Beijing, China
Interests: artificial intelligent for condition monitoring; fault diagnosis; prognostic health management; especially for deep learning; transfer learning; few-shot learning method and their application for the large industrial environment; reinforcement learning for control and its applications
Special Issues, Collections and Topics in MDPI journals
School of Engineering, Lancaster University, Bailrigg, Lancaster LA1 4YW, UK
Interests: human–robot interaction; assistive robotics; teleoperation; reinforcement learning; control theory and applications
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

This is the second edition of a previous successful Special Issue titled “Mixture of Human and Machine Intelligence in Digital Manufacturing” (https://www.mdpi.com/journal/designs/special_issues/R78POOKP14).

Industry 4.0 and digital manufacturing describe new paradigms for seamless human–machine interface (HMI). A number of definitions of HMI have emphasized the significance of understanding how user interface technologies that enable the development of useful, usable, and aesthetic software and hardware are designed. Modern interaction technology has moved from simple use of computers as tools to the establishment of human relationships with autonomous entities, which gradually emphasizes human factors in various steps of interaction processes (De Visser and Shaw, 2018). In this regard, an organic combination of human and machine would leverage their intelligence in a dynamic fashion and motivate computational intelligence development, providing more opportunities for traditional manufacturing system design. While machines interact with manufacturing systems and make human-relevant decisions, new design patterns in perception, control, and arbitration are required to support the integration of human expertise. In addition, sensory data collection and analysis can further aid digital decision-making.

HMI is regarded as a solution for effective operation of machine systems by humans without potential error. Addressing the developments in HMI in an Industry 4.0 context is not sufficient. The objective of this Special Issue is therefore to provide a forum for academics and industrial practitioners to share their latest achievements, identify critical issues and challenges for advanced designs, and highlight applications of HMI in manufacturing.

Dr. Yang Lu
Dr. Ming Zhang
Dr. Ziwei Wang
Guest Editors

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Keywords

  • human–machine interface
  • human–robot collaboration
  • human-in-the-loop (HITL)
  • human-centered design
  • human factors: intent, gaze, emotion, and sensation
  • ergonomics and muscle activation analysis
  • gamification in manufacturing
  • machine/computer intelligence
  • optimization
  • virtual reality, augmented reality, and mixed reality
  • resilience, sustainability, and digitalization
  • deep learning, machine learning, and artificial intelligence
  • cyber–physical systems
  • trustworthy autonomous systems

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Related Special Issue

Published Papers (3 papers)

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Research

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20 pages, 5041 KB  
Article
The Design Process in the Development of an Online Interface for Personalized Footwear
by Margarida Graça, Nuno Martins and Miguel Terroso
Designs 2026, 10(2), 36; https://doi.org/10.3390/designs10020036 - 19 Mar 2026
Viewed by 420
Abstract
This study is part of the FAIST research project—Agile, Intelligent, Sustainable and Technological Factory, coordinated by the Footwear Technology Centre of Portugal (CTCP), which aims to develop an innovative production process through the creation of a sustainable footwear model fully adapted to the [...] Read more.
This study is part of the FAIST research project—Agile, Intelligent, Sustainable and Technological Factory, coordinated by the Footwear Technology Centre of Portugal (CTCP), which aims to develop an innovative production process through the creation of a sustainable footwear model fully adapted to the user’s foot anatomy and personalized according to individual aesthetic preferences. Within this context, the need emerged to design an online platform with an interface capable of effectively addressing user needs throughout all stages of the personalization process, from the foot scanning to the aesthetic personalization of the model, while ensuring an efficient, intuitive, and pleasant navigation experience. Thus, this work aims to demonstrate how the design process of a footwear personalization platform, across its different phases, can contribute to the revitalization of the Portuguese footwear industry, as well as to describe its effectiveness, with the goal of being potentially adapted and implemented in similar contexts. The adopted methodology was based on the principles of Design Thinking, an approach centered on user needs. The development of the platform involved the creation of personas, the definition of the information architecture, the development of wireframes and workflows, and the execution of usability tests using the System Usability Scale (SUS). The results demonstrate a high success rate, validating the proposed solution with users and confirming the suitability of the applied methodologies. Full article
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31 pages, 3570 KB  
Article
Optimization of the Human–Robot Collaborative Disassembly Process Using a Genetic Algorithm: Application to the Reconditioning of Electric Vehicle Batteries
by Salma Nabli, Gilde Vanel Tchane Djogdom and Martin J.-D. Otis
Designs 2025, 9(5), 122; https://doi.org/10.3390/designs9050122 - 17 Oct 2025
Cited by 1 | Viewed by 3631
Abstract
To achieve a complete circular economy for used electric vehicle batteries, it is essential to implement a disassembly step. Given the significant diversity of battery geometries and designs, a high degree of flexibility is required for automated disassembly processes. The incorporation of human–robot [...] Read more.
To achieve a complete circular economy for used electric vehicle batteries, it is essential to implement a disassembly step. Given the significant diversity of battery geometries and designs, a high degree of flexibility is required for automated disassembly processes. The incorporation of human–robot interaction provides a valuable degree of flexibility in the process workflow. However, human behavior is characterized by unpredictable timing and variable task durations, which add considerable complexity to process planning. Therefore, it is crucial to develop a robust strategy for coordinating human and robotic tasks to manage the scheduling of production activities efficiently. This study proposes a global optimization approach to the scheduling of production activities, which employs a genetic algorithm with the objective of minimizing the total production time while simultaneously reducing the idle time of both the human operator and robot. The proposed approach is concerned with optimizing the sequencing of disassembly tasks, considering both temporal and exclusion constraints, to guarantee that tasks deemed hazardous are not executed in the presence of a human. This approach is based on a two-level adaptation framework developed in RoboDK (Robot Development Kit, v5.4.3.22231, 2022, RoboDK Inc., Montréal, QC Canada). At the first level, offline optimization is performed using a genetic algorithm to determine the optimal task sequencing strategy. This stage anticipates human behavior by proposing disassembly sequences aligned with expected human availability. At the second level, an online reactive adjustment refines the plan in real time, adapting it to actual human interventions and compensating for deviations from initial forecasts. The effectiveness of this global optimization strategy is evaluated against a non-global approach, in which the problem is partitioned into independent subproblems solved separately and then integrated. The results demonstrate the efficacy of the proposed approach in comparison with a non-global approach, particularly in scenarios where humans arrive earlier than anticipated. Full article
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Review

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23 pages, 2572 KB  
Review
The Impact of User Interface and Experience (UI/UX) Design on Visual Ergonomics: A Technical Approach for Reducing Human Error in Industrial Settings
by Anael Vizcarra, Gustavo Quiroz and Jose Cornejo
Designs 2026, 10(1), 8; https://doi.org/10.3390/designs10010008 - 21 Jan 2026
Viewed by 3674
Abstract
User Interface (UI) and User Experience (UX) design play a critical role in shaping human interaction with digital systems, particularly in professional environments where accuracy, safety, and efficiency are essential. Poor visual design increases cognitive load and the likelihood of human error, whereas [...] Read more.
User Interface (UI) and User Experience (UX) design play a critical role in shaping human interaction with digital systems, particularly in professional environments where accuracy, safety, and efficiency are essential. Poor visual design increases cognitive load and the likelihood of human error, whereas ergonomically informed interfaces can substantially improve task performance. This systematic literature review analyzes 20 peer-reviewed studies published between 2020 and 2024 to examine how visual ergonomics embedded in UI/UX design contributes to error reduction across industrial and professional contexts. The reviewed studies report measurable improvements when ergonomic principles are applied, including reductions in operational errors ranging from approximately 30% to 70%, improvements in task completion time between 20% and 60%, and increased user accuracy and satisfaction in safety-critical and high-workload environments. The findings indicate that visual hierarchy, modular layouts, adaptive components, and real-time feedback are consistently associated with improved performance outcomes. Moreover, task complexity, user expertise, and working conditions were identified as moderating factors influencing ergonomic demands. Overall, the review demonstrates that visual ergonomics should be treated not merely as a usability enhancement but as a strategic design approach for minimizing human error and supporting reliable human–machine interaction in complex digital environments. Full article
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