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Designs
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Mixture of Human and Machine Intelligence in Digital Manufacturing
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Mixture of Human and Machine Intelligence in Digital Manufacturing

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

Deadline for manuscript submissions: closed (1 November 2023) | Viewed by 9267

Special Issue Editors

Dr. Yang Lu

E-Mail Website
Guest Editor
School of Science, Technology and Health, York St John University, York YO31 7EX, UK
Interests: human-in-the-loop system design; digital intervention; cyber security; artificial intelligence; decision making
Dr. Ming Zhang

E-Mail Website
Guest Editor
College of Engineering and Physical Sciences, Aston University, Birmingham B4 7ET, UK
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
Dr. Ziwei Wang

E-Mail Website
Guest Editor
Department of Bioengineering, Imperial College London and School of Engineering, Lancaster University, Lancaster, UK
Interests: human–robot interaction; assistive robotics; teleoperation; reinforcement learning; control theory and applications

Special Issue Information

Dear Colleagues,

Industry 4.0 and digital manufacturing describe new paradigms for seamless human–machine interface (HMI). A number of definitions of HMI emphasized the significance of understanding how user interface technologies that aim to develop useful, usable and artistic 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 process (De Visser and Shaw, 2018). As such, organic combination between human and machine would leverage their intelligence in a dynamic way 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 of HMI in an industry 4.0 context is not sufficient. The objective of this Special Issue is to provide a forum for academics and industrial practitioners to share the latest achievements, identify critical issues and challenges for advanced designs and applications of HMI in manufacturing.

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

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Designs is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

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

Published Papers (6 papers)

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Research

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15 pages, 1532 KiB  
Article
Predicting Quality of Modified Product Attributes to Achieve Customer Satisfaction
by Andrzej Pacana and Dominika Siwiec
Designs 2024, 8(2), 36; https://doi.org/10.3390/designs8020036 - 20 Apr 2024
Viewed by 220
Abstract
In the era of the competitive environment, the improvement in current products is ensured through activities aimed at increasing a product’s quality level and, consequently, reducing the amount of waste. The dynamically changing production environment and sudden changes in customer expectations force us [...] Read more.
In the era of the competitive environment, the improvement in current products is ensured through activities aimed at increasing a product’s quality level and, consequently, reducing the amount of waste. The dynamically changing production environment and sudden changes in customer expectations force us to take precise and well-thought-out development steps. Furthermore, it is important to anticipate favourable product changes to prepare for market changes over time. This is still an open problem. The aim of this study was to develop a method to predict the quality of potential product prototypes resulting from the proposed modifications of the product features. This methodology takes into account current customer expectations. The method was created based on the principles of creating Quality Function Deployment (QFD) in the context of taking into account current and future customer expectations regarding product features. This is a new approach to analysing product quality within the principles of the traditional QFD method. The originality of the study is the technique used in the method to estimate the expected values of product features and their importance (weights), taking into account current customer expectations. Its originality is also manifested in drawing conclusions supporting the decision-making process of product improvement, because it involves ensuring the pro-quality modification of selected features of current products in the order that is most advantageous from the customer’s point of view. The use of the proposed method allows for the analysis of the impact of modifying the current value of a product feature. The method is illustrated with an example of a vacuum cleaner for home use. However, the proposed method can be applied to the design of any product to predict products that will meet customer expectations. Full article
(This article belongs to the Special Issue Mixture of Human and Machine Intelligence in Digital Manufacturing)
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22 pages, 3165 KiB  
Article
Implementation of Simulation Modeling of Single and High-Volume Machine-Building Productions
by Nadezhda Savelyeva, Tatyana Nikonova, Gulnara Zhetessova, Khrustaleva Irina, Vassiliy Yurchenko, Olegas Černašėjus, Olga Zharkevich, Essim Dandybaev, Andrey Berg, Sergey Vassenkin and Murat Baimuldin
Designs 2024, 8(2), 24; https://doi.org/10.3390/designs8020024 - 08 Mar 2024
Viewed by 857
Abstract
The authors of this article analyze the existing methods and models of technological preparation of machine-building industries. The structure of a three-level simulation model with network-centric control, the structures of individual elements of the simulation model, and the process of simulation modeling are [...] Read more.
The authors of this article analyze the existing methods and models of technological preparation of machine-building industries. The structure of a three-level simulation model with network-centric control, the structures of individual elements of the simulation model, and the process of simulation modeling are described. The criteria for choosing a rational option for the processing technological route have been determined. During this research, a simulation program was implemented in C++. It allows you to select the optimal scenario for the operation of a production site based on two criteria: time and cost. The volume of implementation is about 2 × 103 lines of code. A diagram of the modeling algorithm for the implemented program and a description of the classes and their interactions are given in the article. The developed simulation model was tested at a machine-building enterprise using the example of the “Pusher” part, manufactured under single-unit production conditions. The technological equipment used for the manufacture of this part was formed in the form of input data of the simulation model. The results of simulation modeling for the selected part are described. For each variant of the technological processing route, the values of variable costs and the duration of the production cycle were determined. Full article
(This article belongs to the Special Issue Mixture of Human and Machine Intelligence in Digital Manufacturing)
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25 pages, 1638 KiB  
Article
Evaluating the Ranking of Performance Variables in Flexible Manufacturing System through the Best-Worst Method
by Anthony Bagherian, Gulshan Chauhan, Arun Lal Srivastav and Rajiv Kumar Sharma
Designs 2024, 8(1), 12; https://doi.org/10.3390/designs8010012 - 22 Jan 2024
Viewed by 1390
Abstract
Flexible Manufacturing Systems (FMSs) provide a competitive edge in the ever-evolving manufacturing landscape, offering the agility to swiftly adapt to changing customer demands and product lifecycles. Nevertheless, the complex and interconnected nature of FMSs presents a distinct challenge: the evaluation and prioritization of [...] Read more.
Flexible Manufacturing Systems (FMSs) provide a competitive edge in the ever-evolving manufacturing landscape, offering the agility to swiftly adapt to changing customer demands and product lifecycles. Nevertheless, the complex and interconnected nature of FMSs presents a distinct challenge: the evaluation and prioritization of performance variables. This study clarifies a conspicuous research gap by introducing a pioneering approach to evaluating and ranking FMS performance variables. The Best-Worst Method (BWM), a multicriteria decision-making (MCDM) approach, is employed to tackle this challenge. Notably, the BWM excels at resolving intricate issues with limited pairwise comparisons, making it an innovative tool in this context. To implement the BWM, a comprehensive survey of FMS experts from the German manufacturing industry was conducted. The survey, which contained 34 key performance variables identified through an exhaustive literature review and bibliometric analysis, invited experts to assess the variables by comparing the best and worst in terms of their significance to overall FMS performance. The outcomes of the BWM analysis not only offer insights into the factors affecting FMS performance but, more importantly, convey a nuanced ranking of these factors. The findings reveal a distinct hierarchy: the “Quality (Q)” factor emerges as the most critical, followed by “Productivity (P)” and “Flexibility (F)”. In terms of contributions, this study pioneers a novel and comprehensive approach to evaluating and ranking FMS performance variables. It bridges an evident research gap and contributes to the existing literature by offering practical insights that can guide manufacturing companies in identifying and prioritizing the most crucial performance variables for enhancing their FMS competitiveness. Our research acknowledges the potential introduction of biases through expert opinion, delineating the need for further exploration and comparative analyses in diverse industrial contexts. The outcomes of this study bear the potential for cross-industry applicability, laying the groundwork for future investigations in the domain of performance evaluation in manufacturing systems. Full article
(This article belongs to the Special Issue Mixture of Human and Machine Intelligence in Digital Manufacturing)
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23 pages, 1790 KiB  
Article
Predictive Maintenance in Industry 4.0 for the SMEs: A Decision Support System Case Study Using Open-Source Software
by Mirjana Pejić Bach, Amir Topalović, Živko Krstić and Arian Ivec
Designs 2023, 7(4), 98; https://doi.org/10.3390/designs7040098 - 07 Aug 2023
Cited by 1 | Viewed by 3879
Abstract
Predictive maintenance is one of the most important topics within the Industry 4.0 paradigm. We present a prototype decision support system (DSS) that collects and processes data from many sensors and uses machine learning and artificial intelligence algorithms to report deviations from the [...] Read more.
Predictive maintenance is one of the most important topics within the Industry 4.0 paradigm. We present a prototype decision support system (DSS) that collects and processes data from many sensors and uses machine learning and artificial intelligence algorithms to report deviations from the optimal process in a timely manner and correct them to the correct parameters directly or indirectly through operator intervention or self-correction. We propose to develop the DSS using open-source R packages because using open-source software such as R for predictive maintenance is beneficial for small and medium enterprises (SMEs) as it provides an affordable, adaptable, flexible, and tunable solution. We validate the DSS through a case study to show its application to SMEs that need to maintain industrial equipment in real time by leveraging IoT technologies and predictive maintenance of industrial cooling systems. The dataset used was simulated based on the information on the indicators measured as well as their ranges collected by in-depth interviews. The results show that the software provides predictions and actionable insights using collaborative filtering. Feedback is collected from SMEs in the manufacturing sector as potential system users. Positive feedback emphasized the advantages of employing open-source predictive maintenance tools, such as R, for SMEs, including cost savings, increased accuracy, community assistance, and program customization. However, SMEs have overwhelmingly voiced comments and concerns regarding the use of open-source R in their infrastructure development and daily operations. Full article
(This article belongs to the Special Issue Mixture of Human and Machine Intelligence in Digital Manufacturing)
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23 pages, 11598 KiB  
Article
From Meaning to Expression: A Dual Approach to Coupling
by Lukas Van Campenhout, Ward Vancoppenolle and Ivo Dewit
Designs 2023, 7(3), 69; https://doi.org/10.3390/designs7030069 - 23 May 2023
Viewed by 1208
Abstract
Coupling is a key concept in the field of embodied interaction with digital products and systems, describing how digital phenomena relate to the physical world. In this paper, we present a Research through Design process in which the concept of coupling is explored [...] Read more.
Coupling is a key concept in the field of embodied interaction with digital products and systems, describing how digital phenomena relate to the physical world. In this paper, we present a Research through Design process in which the concept of coupling is explored and deepened. The use case that we employed to conduct our research is an industrial workplace proposed by Audi Brussels and Kuka. Our aim was to enrich this workplace with projection, or Spatial Augmented Reality, while focusing on operator interaction. We went through three successive design iterations, each of which resulted in a demonstrator. We present each of the three demonstrators, focusing on how they propelled our understanding of coupling. We establish a framework in which coupling between different events, be they physical or digital, emerges on four different aspects: time, location, direction, and expression. We bring the first three aspects together under one heading—coupling of meaning—and relate it to ease of use and pragmatic usability. We uncover the characteristics of the fourth aspect—coupling of expression—and link it to the psychological wellbeing of the operator in the workplace. We conclude this paper by highlighting its contribution to the embodied interaction research agenda. Full article
(This article belongs to the Special Issue Mixture of Human and Machine Intelligence in Digital Manufacturing)
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Review

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15 pages, 493 KiB  
Review
A Review of Game Theory Models to Support Production Planning, Scheduling, Cloud Manufacturing and Sustainable Production Systems
by Paolo Renna
Designs 2024, 8(2), 26; https://doi.org/10.3390/designs8020026 - 15 Mar 2024
Viewed by 946
Abstract
Cyber-physical systems, cloud computing, the Internet of Things, and big data play significant roles in shaping digital and automated landscape manufacturing. However, to fully realize the potential of these technologies and achieve tangible benefits, such as reduced manufacturing lead times, improved product quality, [...] Read more.
Cyber-physical systems, cloud computing, the Internet of Things, and big data play significant roles in shaping digital and automated landscape manufacturing. However, to fully realize the potential of these technologies and achieve tangible benefits, such as reduced manufacturing lead times, improved product quality, and enhanced organizational performance, new decision support models need development. Game theory offers a promising approach to address multi-objective problems and streamline decision-making processes, thereby reducing computational time. This paper aims to provide a comprehensive and up-to-date systematic review of the literature on the application of game theory models in various areas of digital manufacturing, including production and capacity planning, scheduling, sustainable production systems, and cloud manufacturing. This review identifies key research themes that have been explored and examines the main research gaps that exist within these domains. Furthermore, this paper outlines potential future research directions to inspire both researchers and practitioners to further explore and develop game theory models that can effectively support the digital transformation of manufacturing systems. Full article
(This article belongs to the Special Issue Mixture of Human and Machine Intelligence in Digital Manufacturing)
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