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Knowledge-Data Driven for Cyber-Physical Production systems in the Aerospace Industry: Current Issues and Emergent Technologies

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IFAC-PapersOnLine 2024年第19期58卷 1162-1167页

作者： Murillo Skrzek Anderson L. Szejka Fernando Mas Maria Jose Escalona Industrial and Systems Engineering Graduate Program (PPGEPS) Pontifical Catholic University of Parana (PUCPR) Curitiba Brazil University of Seville (US) Seville Spain CT Engineering Group Spain

The aerospace manufacturing industry exhibits significant complexity in all its tasks. For instance, the aircraft’s main body comprises several components with multiple dimensions, geometries, and materials. This kind of manufacturing system is specialised in creating aerospace parts characterised by advanced technology, limited production quantities, and a high degree of customisation. The aerospace products and the corresponding manufacturing systems have extensive life cycles spanning decades and are repurposed to accommodate product variations. Any disturbance during the project progression has the potential to result in escalated expenses and time investments, leaving economic and environmental drivers. In this way, Cyber-Physical Production systems (CPPS) are emerging to reduce misinterpretation and mistakes across all stages of the manufacturing process. Therefore, the main aim of this paper is to discuss the current issues and emergent technologies across the literature review to address the following Research Issue 1 (RI1): What are the current issues and emergent technologies in CPPS and KDD for the Aerospace Industry? Research Issue 2 (RI2): What is the gap in CPPS and KDD for the aerospace industry? This initial literature review is concentrated on the Knowledge data-driven (KDD) to aid in developing CPPS for Aerospace Sheet Metal (ASM) parts manufacturing and examining the use of CPPS in the aerospace sector. Finally, this research contributes to the research community with an initial overview of research trends in the domain of KDD for CPPS in the aerospace industry and finds the main research gaps in this area.

关键词： Knowledge data-driven Cyber-Physical Production System Aerospace Industry Content Analysis

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Going in Circles: Key Aspects for Circular Economy Contributions to Agro-industrial Cooperatives

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Circular Economy and Sustainability 2023年第2期3卷 861-880页

作者： Barros, Murillo Vetroni de Jesus, Rômulo Henrique Gomes Ribeiro, Bruno Silva Piekarski, Cassiano Moro Sustainable Production Systems Laboratory (LESP) Graduate Program in Industrial Engineering (PPGEP) Universidade Tecnológica Federal Do Paraná (UTFPR) Paraná Ponta Grossa Brazil Department of Industrial Engineering Universidade Estadual do Paraná (UNESPAR) Paraná Paranaguá Brazil Sustainable Production Systems Laboratory (LESP) Universidade Tecnológica Federal Do Paraná (UTFPR) Paraná Ponta Grossa Brazil

The global agribusiness context faces at the same time challenges of feeding a growing global population that is used to safe and nutritious food, opportunities based on innovation, high technology and efficiency in the agri-food production systems from field to table. Given the context, this article aims to present the main key aspects of the circular economy to agro-industrial cooperatives. These contributions were named key aspects, such as circular and symbiotic practices, competitive advantages, innovation, cooperation and barriers and opportunities. The field presents a problem that few literature in fact discusses circular economy approaches in the context of agro-industrial cooperatives. As a methodological procedure, a literature review was carried out in three databases to obtain relevant documents and thus analyse and discuss some characteristics based on circular economy practices applied in agro-industrial cooperatives. As a result, a diagnosis of the current scenario that agro sector organizations are facing in terms of the main rural activities and sustainable practices that relate to the circular economy;measures taken to generate competitive advantage;innovation behaviour;and how linear and circular business models are being applied in the agroindustry. A framework is presented to show potential routes strategies for closing the cycle in an agro-industrial cooperative. The opportunities are based on the implementation of high technology in the field, the use of bioenergy and the development of new circular business models throughout the agro-value chain. The study has contributions to rural properties and managers of cooperatives in terms of waste reduction, innovation generation and increases in activities and processes based on circular economy. © 2022, The Author(s), under exclusive licence to Springer Nature Switzerland AG.

关键词： Agro-industrial business Circular economy Circularity Cooperation Farm

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Controller tuning with multi-objective optimization: benchmark definitions for SISO linear systems: A PI controller case ⁎

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IFAC-PapersOnLine 2024年第7期58卷 126-131页

作者： Gilberto Reynoso-Meza Ricardo Massao Kagami Industrial and Systems Engineering Graduate Program (PPGEPS) Pontifical Catholic University of Paraná (PUCPR) Control Systems Optimization Laboratory (LOSC) Pontifícia Universidade Católica do Paraná (PUCPR)

Multi-objective optimization techniques are practical techniques for controller tuning purposes. A wide variety of papers use such an approach or propose new algorithms to approximate the Pareto front. Nevertheless, despite the expressive volume of works dealing with it, there is no standard benchmark testbed that could be used as a baseline comparison among different techniques. This work addresses such a gap, with a first step proposing a linear-SISO testbed with guidelines and rules and providing a basic example for a PI controller. Such a benchmark is expected to promote further research on the topic.

关键词： Multi-objective optimization Parametric optimization Evolutionary Algorithms

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Discovery of rare association rules in the distribution of lawsuits in the federal justice system of Southern Brazil

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International Journal of Business Intelligence and Data Mining 2021年第2期18卷 127-154页

作者： dos Santos Gruginskie, Lúcia Adriana Roehe Vaccaro, Guilherme Luís Chiwiacowsky, Leonardo Dagnino Blesz, Attila Elod Graduate Program in Production Engineering and Systems Unisinos Sao Leopoldo RS Brazil Program in Industrial Engineering UCS-Universidade de Caxias do Sul Bento Gonçalves City RS Brazil

In the context of data mining, infrequent association rules may be beneficial for analysing rare or extreme cases with very low support values and high confidence. In researching risky situations or allocating specific resources, such rules may have a much greater impact than rules with high support value. The objective of this study is to obtain association rules from the database of lawsuits filed in the Federal Court of Southern Brazil in 2016, including both frequent and rare rules. By finding these rules, especially rare ones, the information collected can assist in the decision-making process, in this case, such as training clerks or establishing specialised courts. Copyright © 2021 Inderscience Enterprises Ltd.

关键词： Association rules

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In the Search for the Balance Between Real and Synthetic Images in Multi-Class Detection systems

In the Search for the Balance Between Real and Synthetic Ima...

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IEEE Conference on industrial Electronics and Applications (ICIEA)

作者： Vitória Biz Cecchetti Marcelo Rudek Roberto Z. Freire Industrial and Systems Engineering Pontifical Catholic University of Parana (PUCPR - PPGEPS) Curitiba Brazil Industrial and Systems Engineering Graduate Program (PPGEPS) Universidade Tecnoló gica Federal do Paraná (UTFPR) Curitiba Brazil

ISBN: (数字)9798350360868

ISBN: (纸本)9798350360875

Intelligent systems focused on traffic management have been in evidence in recent years, and applications related to vehicle detection and tracking, speed estimation, and traffic flow identification have become an interesting research topic. For the previously mentioned tasks, a large number of data has to be gathered to train deep learning algorithms, but collecting that data can be a time and resource-consuming task. Therefore, the use of synthetic data has become a viable option that helps to minimize data acquisition problems, but when misused, it can negatively impact the model's quality. This paper presents a systematic literature review based on the use of synthetic images to train object detection models in urban scenarios, aiming at identifying the ideal ratio between real and synthetic images that can benefit those models and the best methods to produce synthetic images. This study identified that there is no consensus on the number of synthetic images that can help to generate a more accurate model, due to the low number of papers addressing this relationship, however, it was noted that the use of generative adversarial networks (GANs) can create synthetic images that are more similar to real images, bringing benefits for training detection models, although without identifying how the use of images generated by this method can help in the relationship between synthetic and real.

关键词： Training Analytical models Accuracy Vehicle detection Object detection Generative adversarial networks Data models

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Generative assistant for digital twin simulations 12

Generative assistant for digital twin simulations

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12th CIRP Global Web Conference, CIRPe 2024

作者： Boareto, Pedro Antonio De Freitas Rocha Loures, Eduardo Portela Santos, Eduardo Alves Deschamps, Fernando Industrial and Systems Engineering Graduate Program Pontifical Catholic University of Paraná 1155 Imaculada Conceição St. Paraná Curitiba80215-901 Brazil Federal University of Paraná 632 Prefeito Lothário Meissner Av. Paraná Curitiba80210-170 Brazil

One of the key emerging technologies in Industry 4.0 is the Digital Twin (DT). Although it promises increased efficiency, productivity, and innovation, its adoption faces challenges such as high investment costs and the need for workforce requalification. Generative Artificial Intelligence (GAI) emerges as a promising solution, offering capabilities to accelerate development processes and reduce costs. This study aims to leverage GAI to enhance the development of DT and support decision-making in industrial environments by proposing a Generative Assistant for Digital Twin Simulations (GADTS). This proposal generates operational models quickly, offers greater customization, and facilitates the creation of efficient scenario simulations in natural language. The proposal was tested with artificial data. As a result, the development of highly personalized DT simulations with Key Performance Indicators (KPIs) was entirely abstracted into natural language requests. © 2025 Elsevier B.V.. All rights reserved.

关键词： Generative adversarial networks

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Towards an Integration of Augmented Reality in industrial Assembly Processes: Issues and Perspectives

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Procedia Computer Science 2025年 253卷 1063-1072页

作者： Bruno J. Souza Anderson L. Szejka Roberto Z. Freire Industrial and Systems Engineering Graduate Program Pontifical Catholic University of Parana (PUCPR) Curitiba 80215-901 Brazil Federal University of Technology – Parana (UTFPR) Curitiba 80050-540 Brazil

The evolution of manufacturing has led to Industry 5.0, where human creativity is integrated with advanced technology to achieve efficient and sustainable production. Given the high volume and diversity of assembly tasks, operator assistance systems have become essential tools in the industry, leveraging augmented reality (AR) and other new technologies. This literature review specifically examines 19 papers on AR applications within the context of industrial assembly tasks. The review focuses on identifying the types of AR technologies utilized, the methodologies for implementing AR systems in assembly, and the primary limitations and challenges encountered. The findings indicate a growing field with promising applications in assembly, training, and maintenance, predominantly using AR glasses. Furthermore, a gap is identified indicating the absence of a standardized framework for AR-based assembly assistance systems. Future research should aim at creating a foundational framework for AR system implementation, thereby accelerating the adoption of innovative technologies in the industry.

关键词： Industry 5.0 augmented reality assembly assistance

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Multi-Objective Grey Wolf Optimizer for the Tuning Process of the FOPID Controller

Multi-Objective Grey Wolf Optimizer for the Tuning Process o...

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IEEE/IAS International Conference on Industry Applications (INDUSCON)

作者： Allan Christian Krainski Ferrari Gideon Villar Leandro Leandro Dos Santos Coelho Gilberto Reynoso Meza Eletrical Engineering Department Federal University of Paraná UFPR Curitiba Brazil Electrical Engineering Graduate Program (PPGEE) Federal University of Paraná (UFPR) Industrial and Systems Engineering Graduate Program (PPGEPS) Pontifical Catholic University of Paraná (PUCPR) Industrial and Systems Engineering Graduate Program (PPGEPS) Pontifical Catholic University of Paraná Curitiba Brazil

This article implements the multi-objective grey wolf optimizer (MOGWO) in the tuning process of the gains of the Fractional-Order Proportional-Integral-Derivative (FOPID) controller applied in the control of a boiler system. For a sequence of 100 runs, this application had compared and analyzed with other implementation that uses MOGWO to optimize the gains of the classical Proportional-Integral-Derivative (PID) controller. In the computational simulation, the value hypervolume metric had used to analyze the performance of the controllers. In the results, the implementation of the FOPID showed superior to PID, where the comparison had validated by a hypothesis test. Despite the higher computational cost concerning the tuning process of the PID controller, this study proved that the FOPID controller can be advantageous for industrial applications.

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Generative a bistant for digital twin simulations 1

Generative a bistant for digital twin simulations

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1st International Conference on Digital Sovereignty, ICDS 2024

作者： Boareto, Pedro Antonio De Freitas Rocha Loures, Eduardo Portela Santos, Eduardo Alves Deschamps, Fernando Industrial and Systems Engineering Graduate Program Pontifical Catholic University of Paraná 1155 Imaculada Conceição St. Curitiba Paraná80215-901 Brazil Federal University of Paraná 632 Prefeito Lothário Mei bner Av. Curitiba Paraná80210-170 Brazil

One of the key emerging technologies in Industry 4.0 is the Digital Twin (DT). Although it promises increased efficiency, productivity, and innovation, its adoption faces challenges such as high investment costs and the need for workforce requalification. Generative Artificial Intelligence (GAI) emerges as a promising solution, offering capabilities to accelerate development proce bes and reduce costs. This study aims to leverage GAI to enhance the development of DT and support decision-making in industrial environments by proposing a Generative A bistant for Digital Twin Simulations (GADTS). This proposal generates operational models quickly, offers greater customization, and facilitates the creation of efficient scenario simulations in natural language. The proposal was tested with artificial data. As a result, the development of highly personalized DT simulations with Key Performance Indicators (KPIs) was entirely abstracted into natural language requests. © 2025 Elsevier B.V.. All rights reserved.

关键词： Generative adversarial networks

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Preferences in Construction Workers Selection to a Project Team: A Conjoint Analysis Approach 24

Preferences in Construction Workers Selection to a Project T...

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6th International Conference on Management Science and industrial engineering, MSIE 2024

作者： Bonete, Lorenz Ian B. Prasetyo, Yogi Tri Ahmed, Nagib Ismail Cahigas, Maela Madel L. Nadlifatin, Reny Ayuwati, Irene Dyah School of Industrial Engineering and Engineering Management Mapúa University Manila Philippines School of Graduate Studies Mapúa University Manila Philippines International Bachelor Program in Engineering Yuan Ze University Chung-Li Taiwan Department of Industrial Engineering and Management Yuan Ze University Chung-Li Taiwan Department of Information Systems Institut Teknologi Sepuluh Nopember Kampus ITS Sukolilo Surabaya60111 Indonesia University of Surabaya Surabaya60284 Indonesia

ISBN: (纸本)9798400717130

Construction teams are responsible for building complex projects within the allotted budget, time and expected quality. Thus, team members must be able to work with each other to avoid any complications during the construction process. As such, team supervisors must select from a pool of applicants or from their current roster of workers, which would be composed of different personalities and capabilities. This paper aims to identify the preferences of team supervisors when building up their team using conjoin analysis. The study found out that supervisors would like to have workers that respects and values their work and can work harmoniously with others. This study is beneficial for the human resource department of construction companies when recruiting workers, for the management when trying to develop their current personnel and for the workers who are trying to improve themselves. © 2024 ACM.

关键词： Supervisory personnel

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