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24 pages, 582 KiB

Open AccessArticle

Fault-Resilient Manufacturing Scheduling with Deep Learning and Constraint Solvers

by Hyuk Lee

Appl. Sci. 2025, 15(4), 1771; https://doi.org/10.3390/app15041771 - 10 Feb 2025

Viewed by 660

As edge computing environments become increasingly dynamic, the need for efficient job scheduling and proactive fault prevention is becoming paramount. In such environments, minimizing machine downtime and maintaining productivity are critical challenges. In this paper, we propose an integrated approach to scheduling optimization [...] Read more.

As edge computing environments become increasingly dynamic, the need for efficient job scheduling and proactive fault prevention is becoming paramount. In such environments, minimizing machine downtime and maintaining productivity are critical challenges. In this paper, we propose an integrated approach to scheduling optimization that combines deep learning-based fault prediction with Satisfiability Modulo Theories (SMT)-based scheduling techniques. The proposed system predicts fault probabilities for machines in real time by leveraging operational state features such as temperature, vibration, tool wear, and operating hours. These fault predictions are then used as inputs to the SMT solver, which dynamically optimizes job scheduling. The system ensures task completion within deadlines while minimizing fault risks and optimizing resource utilization. To achieve this, the deep learning model continuously updates fault probabilities through a rolling prediction mechanism, allowing the scheduling system to proactively adapt to changing machine conditions. The SMT solver incorporates these predictions into its optimization process, ensuring that the schedule dynamically reflects the latest system state. The proposed method has been evaluated in simulated production line scenarios, demonstrating significant reductions in machine faults, improved scheduling efficiency, and enhanced overall system reliability. By integrating predictive maintenance with optimization techniques, this research contributes to the development of robust and adaptive scheduling systems for dynamic production environments. Full article

(This article belongs to the Special Issue Smart Manufacturing and Industry 4.0, 2nd Edition)

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15 pages, 4457 KiB

Open AccessArticle

The Real-Time Prediction of Cracks and Wrinkles in Sheet Metal Forming According to Changes in Shape and Position of Drawbeads Based on a Digital Twin

by Sarang Yi, Daeil Hyun and Seokmoo Hong

Appl. Sci. 2025, 15(2), 700; https://doi.org/10.3390/app15020700 - 12 Jan 2025

Cited by 1 | Viewed by 1305

Abstract

In the automotive industry, extensive research has been conducted to eliminate factors negatively impacting product quality, such as wrinkles, cracks, and thickness distribution in components. The application of drawbeads often relies on the experience of field workers, leading to considerable trial and error [...] Read more.

In the automotive industry, extensive research has been conducted to eliminate factors negatively impacting product quality, such as wrinkles, cracks, and thickness distribution in components. The application of drawbeads often relies on the experience of field workers, leading to considerable trial and error before stabilizing the production process. Therefore, to efficiently transform these inefficiencies related to time and cost, there is a need for real-time predictive technology for forming quality based on the position of drawbeads and the bead force. This study proposes a method for predicting formability in real-time, based on a digital twin framework that considers the position of drawbeads and holder force. A digital twin was developed to predict the sheet metal forming process using Support Vector Machine, Random Forest, Gradient Boosting Machine, and Artificial Neural Networks. The machine learning models were trained using finite element analysis data corresponding to the position and bead force of drawbeads, enabling the real-time prediction of wrinkles and crack occurrences. The accuracy of the machine learning models was demonstrated, achieving 100% accuracy in determining crack occurrence, with a mean squared error (MSE) of 0.141 for wrinkle prediction and 0.038 for crack prediction, thereby ensuring the accuracy of the forming prediction model based on drawbead applications. Based on these predictive models, a user-friendly GUI has been developed, which is expected to reduce design time and costs while facilitating real-time predictions of forming quality, such as wrinkles and cracks, on-site. Full article

(This article belongs to the Special Issue Smart Manufacturing and Industry 4.0, 2nd Edition)

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25 pages, 4748 KiB

Open AccessArticle

Data and Knowledge-Driven Bridge Digital Twin Modeling for Smart Operation and Maintenance

by Zhe Sun, Bin Liang, Shengyao Liu and Zhansheng Liu

Appl. Sci. 2025, 15(1), 231; https://doi.org/10.3390/app15010231 - 30 Dec 2024

Viewed by 1036

Abstract

The rapid expansion of civil infrastructure in China underscores the critical need for advanced solutions to ensure the structural health of aging bridges. This study introduces a novel data and knowledge-driven digital twin modeling (DK-DTM) framework designed to enhance the safe and efficient [...] Read more.

The rapid expansion of civil infrastructure in China underscores the critical need for advanced solutions to ensure the structural health of aging bridges. This study introduces a novel data and knowledge-driven digital twin modeling (DK-DTM) framework designed to enhance the safe and efficient operation and maintenance (O&M) of bridges. Such a system should be capable of (1) monitoring structural dynamics in real time, (2) capturing spatiotemporal details and changes (e.g., defects and deformations), (3) analyzing structure deterioration patterns, (4) predicting structure failure risks, and (5) generating optimal maintenance and repair actions for ensuring structural safety. Previous studies have developed advanced sensing techniques and robust artificial intelligence algorithms for capturing and analyzing bridge health conditions. However, most existing techniques and algorithms heavily rely on high-quality data, which are difficult to obtain during bridge O&M. This raises the critical question of how to incorporate expert knowledge together with data-driven tools to establish a trustworthy DT for bridge O&M. This study presents the DK-DTM framework, which uniquely integrates multi-source data collection, spatiotemporal modeling, and expert knowledge reasoning. By combining these components, the framework supports smart structural health assessments of bridges, enabling comprehensive monitoring, prediction, and decision-making for efficient maintenance. The spatial and temporal models provide real-time data, while the expert knowledge model functions as an automated evaluation tool for structural health assessment. The results demonstrate that the proposed DK-DTM framework significantly enhances the accuracy and efficiency of O&M processes for aging bridges, addressing key gaps in existing digital twin methodologies. Full article

(This article belongs to the Special Issue Smart Manufacturing and Industry 4.0, 2nd Edition)

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23 pages, 16504 KiB

Open AccessArticle

Skin Imaging: A Digital Twin for Geometric Deviations on Manufactured Surfaces

by Elnaz Ghanbary Kalajahi, Mehran Mahboubkhah and Ahmad Barari

Appl. Sci. 2023, 13(23), 12971; https://doi.org/10.3390/app132312971 - 4 Dec 2023

Viewed by 1674

Abstract

Closed-loop manufacturing is crucial in Industry 4.0, since it provides an online detection–correction cycle to optimize the production line by using the live data provided from the product being manufactured. By integrating the inspection system and manufacturing processes, the production line achieves a [...] Read more.

Closed-loop manufacturing is crucial in Industry 4.0, since it provides an online detection–correction cycle to optimize the production line by using the live data provided from the product being manufactured. By integrating the inspection system and manufacturing processes, the production line achieves a new level of accuracy and savings on costs. This is far more crucial than only inspecting the finished product as an accepted or rejected part. Modeling the actual surface of the workpiece in production, including the manufacturing errors, enables the potential to process the provided live data and give feedback to production planning. Recently introduced “skin imaging” methodology can generate 2D images as a comprehensive digital twin for geometric deviations on any scanned 3D surface including analytical geometries and sculptured surfaces. Skin-Image has been addressed as a novel methodology for continuous representation of unorganized discrete 3D points, by which the geometric deviation on the surface is shown using image intensity. Skin-Image can be readily used in online surface inspection for automatic and precise 3D defect segmentation and characterization. It also facilitates search-guided sampling strategies. This paper presents the implementation of skin imaging for primary engineering surfaces. The results, supported by several industrial case studies, show high efficiency of skin imaging in providing models of the real manufactured surfaces. Full article

(This article belongs to the Special Issue Smart Manufacturing and Industry 4.0, 2nd Edition)

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34 pages, 14174 KiB

Open AccessArticle

Cloud-Based Architecture for Production Information Exchange in European Micro-Factory Context

by Fábio M-Oliveira, André Dionísio Rocha, Duarte Alemão, Nelson Freitas, Rayko Toshev, Jani Södergård, Nikolaos Tsoniotis, Charalampos Argyriou, Alexios Papacharalampopoulos, Panagiotis Stavropoulos, Pietro Perlo and José Barata

Appl. Sci. 2023, 13(18), 10223; https://doi.org/10.3390/app131810223 - 12 Sep 2023

Cited by 3 | Viewed by 2087

Abstract

In a constantly changing world, information stands as one of the most valuable assets for a manufacturing site. However, exchanging information is not a straightforward process among factories, and concerns regarding the trustability and validation of transactions between various stakeholders have emerged within [...] Read more.

In a constantly changing world, information stands as one of the most valuable assets for a manufacturing site. However, exchanging information is not a straightforward process among factories, and concerns regarding the trustability and validation of transactions between various stakeholders have emerged within the context of micro-factories. This work presents an architecture designed to enable information exchange among heterogeneous stakeholders, taking advantage of the cloud infrastructure. It was designed to enable the use of several tools, connected through a middleware system deployed on the cloud. To demonstrate the potential of this architecture, a platform was instantiated, and two use cases—designed to accurately represent real manufacturing sites—were implemented. Full article

(This article belongs to the Special Issue Smart Manufacturing and Industry 4.0, 2nd Edition)

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19 pages, 667 KiB

Open AccessArticle

Dimensional Tolerances in Mechanical Assemblies: A Cost-Based Optimization Approach

by Eduardo Umaras, Ahmad Barari, Oswaldo Horikawa and Marcos Sales Guerra Tsuzuki

Appl. Sci. 2023, 13(16), 9202; https://doi.org/10.3390/app13169202 - 13 Aug 2023

Cited by 2 | Viewed by 2822

Abstract

There is a widely accepted consensus that component manufacturing precision is directly correlated with improved functional performance. However, this increase in precision comes at the expense of higher manufacturing costs, resulting in a trade-off between quality and affordability. In light of this opposing [...] Read more.

There is a widely accepted consensus that component manufacturing precision is directly correlated with improved functional performance. However, this increase in precision comes at the expense of higher manufacturing costs, resulting in a trade-off between quality and affordability. In light of this opposing behavior, low-cost products typically exhibit lower quality, whereas high-quality products tend to be more expensive. This study introduces a novel approach for optimizing the dimensional tolerances of mechanical assembly components, taking into account both their manufacturing requirements and the associated costs of non-quality. Furthermore, the method considers the functional constraints imposed by interrelated tolerance chains within the product. Instead of relying on an exact mathematical solution, the proposed solution employs a heuristic approach through a simple and flexible algorithm. This enables practical implementation, as different cost-tolerance functions can be selected based on specific requirements. To provide a comprehensive evaluation of the proposed method, a concise review of the relevant literature in the field was conducted, allowing a comparison with other state-of-the-art approaches. Full article

(This article belongs to the Special Issue Smart Manufacturing and Industry 4.0, 2nd Edition)

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19 pages, 2989 KiB

Open AccessArticle

Project Portfolio Planning Taking into Account the Effect of Loss of Competences of Project Team Members

by Grzegorz Bocewicz, Eryk Szwarc, Amila Thibbotuwawa and Zbigniew Banaszak

Appl. Sci. 2023, 13(12), 7165; https://doi.org/10.3390/app13127165 - 15 Jun 2023

Viewed by 1111

Abstract

This paper deals with a declarative model of the performance of employees conducting variably repetitive tasks based on the assumption of aging competences. An analytical model is used to consider refreshing the competences of the team’s multi-skilled members and shaping the structure of [...] Read more.

This paper deals with a declarative model of the performance of employees conducting variably repetitive tasks based on the assumption of aging competences. An analytical model is used to consider refreshing the competences of the team’s multi-skilled members and shaping the structure of staff’s competences to maximize their mutual substitutability in processes typical for a multi-item lot-size production. Its impact on maintaining the skill level of employees is important in cases of an unplanned event, e.g., caused by employee absenteeism and/or a change in the priorities of orders carried out, disrupting the task of software companies. The developed model implemented in the constraint programming environment enables the formulation of decision-making versions of both the problem of analysis (seeking an answer to the question to discover whether there is a solution that meets the given expectations) and synthesis (seeking an answer to the question, assuming there is a solution that meets the given expectations). The potential of the proposed reference model-based approach is illustrated with examples. Full article

(This article belongs to the Special Issue Smart Manufacturing and Industry 4.0, 2nd Edition)

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19 pages, 8374 KiB

Open AccessArticle

Retrieval of Injection Molding Industrial Knowledge Graph Based on Transformer and BERT

by Zhe-Wei Zhou, Wen-Ren Jong, Yu-Hung Ting, Shia-Chung Chen and Ming-Chien Chiu

Appl. Sci. 2023, 13(11), 6687; https://doi.org/10.3390/app13116687 - 31 May 2023

Cited by 1 | Viewed by 1708

Abstract

Knowledge graphs play an important role in the field of knowledge management by providing a simple and clear way of expressing complex data relationships. Injection molding is a highly knowledge-intensive technology, and in our previous research, we have used knowledge graphs to manage [...] Read more.

Knowledge graphs play an important role in the field of knowledge management by providing a simple and clear way of expressing complex data relationships. Injection molding is a highly knowledge-intensive technology, and in our previous research, we have used knowledge graphs to manage and express relevant knowledge, gradually establishing an injection molding industrial knowledge graph. However, the current way of retrieving knowledge graphs is still mainly through programming, which results in many difficulties for users without programming backgrounds when it comes to searching a graph. This study will utilize the previously established injection molding industrial knowledge graph and employ a BERT (Bidirectional Encoder Representations from Transformers) fine-tuning model to analyze the semantics of user questions. A knowledge graph will be retrieved through a search engine built on the Transformer Encoder, which can reason based on the structure of the graph to find relevant knowledge that satisfies a user’s questions. The experimental results show that both the BERT fine-tuned model and the search engine achieve an excellent performance. This approach can help engineers who do not have a knowledge graph background to retrieve information from the graph by inputting natural language queries, thereby improving the usability of the graph. Full article

(This article belongs to the Special Issue Smart Manufacturing and Industry 4.0, 2nd Edition)

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