Haptic-Robotic Systems in Industrial Design, Manufacturing, Assembly, Simulation, and Training

Special Issue Editors


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Guest Editor
Faculty of Engineering, University of Porto, Rua Dr. Roberto Frias, 4200-465 Porto, Portugal
Interests: complex systems modelling; automation and robotics; fractional order systems modelling and control; data analysis and visualization; machine learning
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E-Mail Website
Guest Editor
1. INEGI—Institute of Science and Innovation in Mechanical and Industrial Engineering, 4200-465 Porto, Portugal
2. ISEP, Polytechnic of Porto, Rua Dr. António Bernardino de Almeida, 4249-015 Porto, Portugal
Interests: design of the mechatronic (pneumatic and oil-hydraulics) system; maintenance; robotics; AI and mechanical engineering
Special Issues, Collections and Topics in MDPI journals

E-Mail Website
Guest Editor
1. INEGI–Institute of Science and Innovation in Mechanical and Industrial Engineering, Rua Dr. Roberto Frias, 4200-465 Porto, Portugal
2. MEtRICs Research Center, School of Engineering, University of Minho, Campus of Azurém, 4800-058 Guimarães, Portugal
Interests: automation and industrial control; Industry 4.0; mechatronics; artificial intelligence; industrial, mobile and colaborative robots and industrial network protocols and advanced communication networks
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Haptic–robotic systems are at the forefront of transforming industrial practices by integrating force and tactile feedback with robotic capabilities. Haptic–robotic systems enhance user interaction in various applications, including industrial design, manufacturing processes, assembly tasks, simulations, and training environments. By providing realistic force and touch perceptions, haptic–robotic systems enable precision, efficiency, and improved learning outcomes.

This Special Issue (SI) aims to explore the latest advancements, methodologies, and case studies in this dynamic field. It is expected that the SI will serve as a comprehensive platform for researchers and practitioners to share insights and advancements in the field of haptic–robotic systems, contributing to enhancing the understanding and implementation of these technologies in industrial design, manufacturing, assembly, simulation, and training processes. Contributions reporting original research, reviews, or applications of haptic–robotic systems in industrial design, manufacturing, assembly, simulation, and training are welcome. Topics include, but are not limited to, the following themes:

  • The development of haptic interfaces for enhancing industrial design processes; applications of haptic feedback in robotic assembly tasks to improve accuracy and efficiency;
  • Real-time haptic feedback mechanisms for manufacturing processes and quality control;
  • Adoption of haptic–robotic systems in simulation environments for training skills in various industries;
  • Analyzing the impact of haptic feedback on ergonomics and operator training; integration of machine learning and AI to optimize haptic feedback and robotic control strategies;
  • Real-world successful implementations of haptic–robotic systems across different industrial sectors;
  • Performance evaluation metrics for haptic–robotic systems in design, manufacturing, assembly, and training.

Dr. António Lopes
Dr. Adriano A. Santos
Dr. Filipe Pereira
Guest Editors

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Keywords

  • haptic–robotic systems
  • industrial design
  • manufacturing

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Published Papers (1 paper)

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Research

27 pages, 14022 KiB  
Article
Personalizing Industrial Maintenance Operation Using the Model of Hierarchical Complexity
by Gonçalo Raposo, Nuno Araújo, Marco Parente, António M. Lopes, Adriano Santos, Filipe Pereira, Sofia Leite and António Ramos Silva
J. Manuf. Mater. Process. 2025, 9(4), 132; https://doi.org/10.3390/jmmp9040132 - 15 Apr 2025
Viewed by 287
Abstract
The rapid advancement of Industry 4.0 technologies has transformed industrial maintenance operations, introducing digital work instructions as a critical tool for improving efficiency and reducing errors. However, existing digitalization approaches often fail to account for variations in worker expertise, leading to cognitive overload, [...] Read more.
The rapid advancement of Industry 4.0 technologies has transformed industrial maintenance operations, introducing digital work instructions as a critical tool for improving efficiency and reducing errors. However, existing digitalization approaches often fail to account for variations in worker expertise, leading to cognitive overload, frustrations, and overall inefficiency. This study proposes a novel methodology for dynamically personalizing digital work instructions by structuring task instructions based on complexity levels and worker proficiency. Using the Model of Hierarchical Complexity (MHC) as a framework ensures that operators receive guidance tailored to their cognitive and skill capabilities. The methodology is implemented and evaluated in an industrial maintenance environment, where digital work instructions are adapted based on worker profiles. The results show significant improvements in maintenance operations, including a reduction in task completion time, a decrease in error rates, and enhanced worker engagement. Comparative analysis with conventional static instructions reveals that personalized digital work instructions contribute to a more effective knowledge transfer process, reducing cognitive strain and enhancing procedural adherence. Additionally, integrating predictive maintenance strategies with personalized work instructions could further enhance operational efficiency by enabling proactive decision-making. Addressing potential challenges, such as worker resistance to adaptive technologies and data privacy concerns, will be crucial for widespread implementation. In conclusion, leveraging the Model of Hierarchical Complexity to personalize digital work instructions represents a significant step toward optimizing industrial maintenance workflows. Tailoring instructional content to individual skill levels and cognitive abilities enhances workforce productivity, reduces errors, and contributes to the broader objectives of Industry 4.0. Full article
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