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15 pages, 5393 KiB

Open AccessArticle

An Approach for Predicting the Lifetime of Lead-Free Soldered Electronic Components: Hitachi Rail STS Case Study

by Paolo Renna, Michele Ambrico, Vito Romaniello and Thomas Russino

Designs 2024, 8(4), 74; https://doi.org/10.3390/designs8040074 - 26 Jul 2024

Viewed by 912

Abstract

Throughout much of the 20th century, Sn–Pb solder dominated electronics. However, environmental and health concerns have driven the adoption of lead-free alternatives. Since 2006, legislation such as the European Union’s RoHS Directive has mandated lead-free solder in most electronic devices, prompting extensive research [...] Read more.

Throughout much of the 20th century, Sn–Pb solder dominated electronics. However, environmental and health concerns have driven the adoption of lead-free alternatives. Since 2006, legislation such as the European Union’s RoHS Directive has mandated lead-free solder in most electronic devices, prompting extensive research into high-performance substitutes. Lead-free solders offer advantages such as reduced environmental impact and improved reliability but replacing Sn–Pb presents challenges in areas like melting point and wetting ability. This transition is primarily motivated by a focus on protecting environmental and human health, while ensuring equal or even improved reliability. Research has explored lead-free solder’s mechanical properties, microstructure, wettability, and reliability. However, there is a notable lack of studies on its long-term performance and lifetime influence. To address this gap, mathematical models are used to predict intermetallic bond evolution from process conditions, validated with experimental tests. This study contributes by extending these models to predict bond evolution under typical operating conditions of devices and comparing the predictions with actual intermetallic thickness values found through metallographic sections. Full article

(This article belongs to the Special Issue Mixture of Human and Machine Intelligence in Digital Manufacturing)

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26 pages, 6315 KiB

Open AccessArticle

Scalable Compositional Digital Twin-Based Monitoring System for Production Management: Design and Development in an Experimental Open-Pit Mine

by Nabil El Bazi, Oussama Laayati, Nouhaila Darkaoui, Adila El Maghraoui, Nasr Guennouni, Ahmed Chebak and Mustapha Mabrouki

Designs 2024, 8(3), 40; https://doi.org/10.3390/designs8030040 - 7 May 2024

Viewed by 2293

Abstract

While digital twins (DTs) have recently gained prominence as a viable option for creating reliable asset representations, many existing frameworks and architectures in the literature involve the integration of different technologies and paradigms, including the Internet of Things (IoTs), data modeling, and machine [...] Read more.

While digital twins (DTs) have recently gained prominence as a viable option for creating reliable asset representations, many existing frameworks and architectures in the literature involve the integration of different technologies and paradigms, including the Internet of Things (IoTs), data modeling, and machine learning (ML). This complexity requires the orchestration of these different technologies, often resulting in subsystems and composition frameworks that are difficult to seamlessly align. In this paper, we present a scalable compositional framework designed for the development of a DT-based production management system (PMS) with advanced production monitoring capabilities. The conducted approach used to design the compositional framework utilizes the Factory Design and Improvement (FDI) methodology. Furthermore, the validation of our proposed framework is illustrated through a case study conducted in a phosphate screening station within the context of the mining industry. Full article

(This article belongs to the Special Issue Mixture of Human and Machine Intelligence in Digital Manufacturing)

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15 pages, 1532 KiB

Open AccessArticle

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 1054

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

Open AccessArticle

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 - 8 Mar 2024

Cited by 2 | Viewed by 1550

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

Open AccessArticle

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 2056

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

Open AccessArticle

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 - 7 Aug 2023

Cited by 9 | Viewed by 6197

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

Open AccessEditor’s ChoiceArticle

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

Cited by 1 | Viewed by 1592

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

Jump to: Research

15 pages, 493 KiB

Open AccessReview

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

Cited by 3 | Viewed by 2621

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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