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

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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Effect of Sn addition on electrochemical activity of ribbons of Mg-Al-Ca anode material for rechargeable magnesium batteries fabricated by rapid solidification

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Keikinzoku/Journal of Japan Institute of Light Metals 2025年第1期75卷 9-13页

作者： Fukuta, Yoshitaka Kikura, Kensei Tsukeda, Tadayoshi Aida, Tetsuo Tabata, Hironobu Kurihara, Hideki Suzuki, Mayumi Program of Materials Science and Engineering Studies in Science and Engineering Graduate School of Science and Engineering University of Toyama 3190 Gofuku Toyama-shi Toyama930-8555 Japan Graduate School of Science and Engineering University of Toyama Toyama-shi Toyama Japan Faculty of Sustainable Design Academic Assembly University of Toyama Toyama-shi Toyama Japan CHUETSU METAL WORKS CO. LTD. Nakaniikawagun Toyama Japan Saitama Industrial technology Center Kawaguchi-shi Saitama Japan Department of Mechanical Systems Engineering Faculty of Engineering Toyama Prefectural University Imizu-shi Toyama Japan

Magnesium-based batteries are potential candidates for next-generation rechargeable batteries due to the divalent nature of magnesium cations and the natural abundance of magnesium resources. In this study, the electrochemical activity of Mg-6mass%Al-3mass%Ca anodes combined with Sn was investigated. Thin ribbons of the anode specimens were prepared by a single-roll rapid solidification method. The results showed that the electrochemical activity increased upon adding 1 mass% Sn, while it decreased for 3 mass% Sn. Based on the results of an X-ray diffraction analysis, the addition of Sn led to the formation of Mg2Sn and Ca2Sn phases, which increased the lattice distortion. Scanning electron microscopy observations indicated that these secondary phases were homogeneously distributed for 1 mass% Sn addition, while they were coarsely distributed for 3 mass% Sn. © 2025 The Japan Institute of Light Metals.

关键词： X ray diffraction analysis

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Deep Learning-Based Residual Useful Lifetime Prediction for Assets With Uncertain Failure Modes

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Journal of Computing and Information Science in engineering 2025年第4期25卷 041002页

作者： Su, Yuqi Fang, Xiaolei Operations Research Graduate Program North Carolina State University 915 Partners Way Raleigh NC 27607 Edward P. Fitts Department of Industrial and Systems Engineering North Carolina State University 915 Partners Way Raleigh NC 27607

industrial prognostics focuses on utilizing degradation signals to forecast and continually update the residual useful life of complex engineering systems. However, existing prognostic models for systems with multiple failure modes face several challenges in real-world applications, including overlapping degradation signals from multiple components, the presence of unlabeled historical data, and the similarity of signals across different failure modes. To tackle these issues, this research introduces two prognostic models that integrate the mixture (log)-location-scale distribution with deep learning. This integration facilitates the modeling of overlapping degradation signals, eliminates the need for explicit failure mode identification, and utilizes deep learning to capture complex nonlinear relationships between degradation signals and residual useful lifetimes. Numerical studies validate the superior performance of these proposed models compared to existing methods.

关键词： Failure mechanisms mixture (log)-location-scale distribution Failure Weibull distribution multiple failure modes Signals Service life (Equipment) prognostics data analytics Deep learning machine learning for engineering applications engineering systems and industry applications industrial Internet of things big data and analytics

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Cascaded evolutionary multiobjective identification based on correlation function statistical tests for improving velocity analyzes in swimming

Cascaded evolutionary multiobjective identification based on...

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2014 IEEE Symposium on Computational Intelligence in Multi-Criteria Decision-Making, MCDM 2014

作者： Ayala, Helon Vicente Hultmann Da Cruz, Luciano Ferreira Freire, Roberto Zanetti Coelho, Leandro Dos Santos Industrial and Systems Engineering Graduate Program PUCPR Curitiba Brazil Industrial and Systems Engineering Graduate Program Department of Electrical Engineering UFPR Curitiba Brazil

ISBN: (纸本)9781479944682

By using biomechanical analyses applied to sports many researchers are providing important information to coaches and athletes in order to reach better performance in a shorter time. In swimming, these kinds of analyses are being used to evaluate, to detect and to improve the skills of high level athletes. Recently, evolutionary computing theories have been adopted to support swim velocity profile identification. Based on velocity profiles recognition, it is possible to identify distinct characteristics and classify swimmers according to their abilities. In this way, this work presents an application of Radial Basis Function Neural Network (RBF-NN) associated to a proposed cascaded evolutionary procedure composed by a genetic and Multiobjective Differential Evolution (MODE) algorithms as optimization method for searching the best fitness within a set of parameters to configure the RBF-NN. The main goal and novelty of the proposed approach is to enable, through the adoption of cascaded multiobjective optimization, the use of correlation based tests in order to select both the model lagged inputs and the associated parameters in a supervised fashion. Finally, the real data of a Brazilian elite female swimmer in crawl and breaststroke styles obtained into a 25 meters swimming pool have been identified by the proposed method. The soundness of the approach is illustrated with the adherence to the model validity tests and the values of the multiple correlation coefficients between 0.95 and 0.93 for two tests for both breaststroke and crawl strokes, respectively. © 2014 IEEE.

关键词： Multiobjective optimization

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Methodology for optimizing electrical grounding grids in stratified soils using advanced calculation techniques and evolutionary algorithms

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Swarm and Evolutionary Computation 2025年 95卷

作者： Silva, Carlos Leandro Borges da Pires, Thyago Gumeratto Silva Filho, Antonio Marcelino Bulhões, Junio Santos Belo, Orlando M. Oliveira Rodrigues, Clóves Gonçalves Coimbra, Antonio Paulo Calixto, Wesley Pacheco Electrical Mechanical & Computer Engineering School Federal University of Goias Goiania Goias PC 74.605-220 Brazil Institute of Systems and Robotics Coimbra University CoimbraPC 3030-290 Portugal Department of Informatics School of Engineering University of Minho BragaPC 4710-057 Portugal Electrical Power Systems Study Group Federal Institute of Tocantins Palmas TocantinsPC 77.021-090 Brazil Graduate Program in Production and Systems Engineering Pontifical Catholic University of Goias Goiania Goias PC 74605-010 Brazil Mato Grosso Brazil Technology Research and Development Center Federal Institute of Goias Goiania Goias PC 74.055-110 Brazil

This paper presents a practical methodology for optimizing the geometry of electrical grounding grids at industrial frequencies of 50Hz and 60Hz, integrating advanced calculation techniques and evolutionary algorithms to improve the safety and operational performance of electrical grounding systems. The proposed approach is particularly beneficial for industrial automation and control systems, where effective grounding is necessary to maintain system reliability and prevent downtime. This methodology employs mathematical modeling and computational tools to optimize grid parameters, ensuring compliance with safety standards while reducing operational costs, thus contributing to the overall efficiency of automated systems in industrial environments. The study reports a reduction of up to 66% in the number of vertical rods and 40% in horizontal conductors compared to traditional methods. These results indicate that the proposed methodology can significantly reduce material usage and costs while maintaining electrical safety in accordance with regulatory standards, making it applicable to a wide range of industrial settings, including substations and automated facilities. © 2025 The Authors

关键词： Electric grounding

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Discrete Differential Evolution with Local Search to Solve the Traveling Salesman Problem: Fundamentals and Case Studies

Discrete Differential Evolution with Local Search to Solve t...

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7th IEEE International Conference on Cybernetic Intelligent systems

作者： Sauer, Joao Guilherme Coelho, Leandro dos Santos Industrial and Systems Engineering Graduate Program Pontifical Catholic University of Parana (PPGEPS/PUCPR) Curitiba Parana Brazil

ISBN: (纸本)9781424429141

Traveling Salesman Problem (TSP) is one of the most difficult problems in the Combinatorial Optimization area. The goal of TSP is to find one path that can travel between all the nodes (instances) of the graph just once (Hamiltonian tour) in the smallest tour, that is, smallest Euclidian distance. In this context, many other meta-heuristics techniques based on evolutionary algorithms have been propose in literature to solve TSP problems. Differential Evolution (DE) is a relatively new simple evolutionary algorithm, which is an effective adaptive approach to global optimization over continuous search spaces. The design principles of DE are simplicity and efficiency. The most distinct feature of DE is that it mutates vectors by adding weighted. random vector differentials to them. Since its invention. DE has been applied with high success on many numerical optimization problems outperforming other more popular meta-heuristics such as the genetic algorithms. Recently. some researchers extended with success the application of DE to complex combinatorial optimization problems with discrete decision variables such as the traveling salesman problem, the machine layout problem, the now-shop scheduling problem. In this paper, the following discrete DE approaches for the TSP are proposed and evaluated: i) DE approach without local search, ii) DE with local search based on Lin-Kernighan-Heulsgaun (LKH) method, and iii) DE with local search based on Variable Neighborhood Search (VNS) and together with LKH method. Numerical study is carried out using the TSPLIB of test TSP problems. In this context, the computational results are compared with the other results in the recent TSP literature. The obtained results show that LKH method is the best method to reach optimal results for TSPLIB benchmarks, but for largest problems, the ED+VNS improve the quality of obtained results.

关键词： Optimization Traveling salesman problem Evolutionary Algorithm Differential Evolution Variable Neighbor Search Local Search Lin-Kernighan-Heulsgaun

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Resolution of one-dimensional bin packing problems using augmented neural networks and minimum bin slack

Resolution of one-dimensional bin packing problems using aug...

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作者： De Almeida, Ricardo Steiner, Maria Teresinha Arns Industrial and Systems Engineering Post-Graduate Program Pontifical Catholic University of Paraná - PUCPR Curitiba Brazil

The objective of this work is to compare the augmented neural network (AugNN) metaheuristic to minimum bin slack (MBS) heuristic to solve combinatorial optimisation problems, specifically, in this case, the one-dimensional bin packing problem (1D-BPP), a class of cutting and packing problems (CPP). CPP are easily found among various industry sectors and its proper treatment can improve the use of stocks in cutting problems or optimise physical space in packing problems. In order to optimise AugNN parameters, a design of experiment (DOE) was applied in order to guide a statistical analysis of different configurations of AugNN. The tests, developed in many benchmark problems found in the literature, showed that MBS heuristic was, in general superior, both in terms of the solution quality, which is about 70% better, and computational time, which is about 90% less. Copyright © 2016 Inderscience Enterprises Ltd.

关键词： Design of experiments

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Different approaches on how to improve braking performance in M3 vehicles to meet secondary brake requirements from ECE R13 regulation through low cost solutions 14

Different approaches on how to improve braking performance i...

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14th SAE Brasil International Brake and Motion Control Colloquium and Exhibit, BRASILBRAKE 2019

作者： Da Cruz, Luciano Ferreira Rudek, Claudemir Freire, Roberto Zanetti Volvo Bus Corporation Volvo Trucks Technology Industrial and Systems Engineering Graduate Program PUCPR Brazil

The braking system of any road vehicle is subject to extensive legislative standards and requirements in many regions around the world. In buses, which belong to M3 category, one of the most critical demands is occupancy rate of passengers that leads to affect Gross Vehicle Weight (GVW). More and more, customers are pushing the manufactures to increase the GVW of buses to allow more passengers inside and consequently increase profitability of the operation. By increasing GVW braking system has to present better performance to fulfill requirements. ECE R13 regulation establishes 2.5m/s2 as minimum Mean Fully Developed Deceleration (MFDD) and 64,4meters as maximum Stopping Distance (SD) acceptable to secondary brake performance. In a 4x2 vehicle with GVW of 19.5 tons fitted with disc brakes these requirements are actually fulfilled, but by adding 600 kg it became out of requirements. Based on the parameters which might affect braking performance, three different approaches were studied, tested and disposed in a decision matrix to choose most feasible in terms of performance, costs, manufacturing and time to implementation: a new protecting valve setup, increasing residual pressure at port 21 and 22;a new pneumatic scheme using a Double Check Valve (DCV) to share residual pressure from non- failed circuit to failed circuit;and finally an exchange of 24" service brake chambers by a 27" in both axles, being those solutions preselected as feasible solutions in terms of less impacts to brake system. Tests were performed considering failure at front (FA) and rear (RA) axles. By taking baseline results with MFDD 5.49% and 1.63%, and SD 3.09% and 4.72% out of requirements for FA and RA failures respectively, test results for proposed solutions showed in a ranking: DCV solution as the best performance, which had improved MFDD by 50.56% and 51.92%, and SD had decreased by 67.51% and 59.44%;27" service chambers as second best solution, which had improved MFDD by 17.80% and 16.23

关键词： Braking performance

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Towards an integrated MCDM and BSC method to support the digital transformation strategy in railway companies 28

Towards an integrated MCDM and BSC method to support the dig...

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28th ISTE International Conference on Transdisciplinary engineering, TE 2021

作者： Zanon, Gabriel Nogueira Szejka, Anderson Luis De Freitas Rocha Loures, Eduardo Industrial and Systems Engineering Graduate Program - PPGEPS Pontifical Catholic University of Paraná - PUCPR Curitiba Brazil

ISBN: (纸本)9781643682082

Digital transformation is now widely discussed and applied in different enterprises and under various organizational aspects. Traditional industries recognize the need to innovate and digitalize their business processes and models. Among these industries, the railway sector presents a vast space for digital transformation. The railway has several operational, regulatory, regional and technological specificities that must be taken into account in the digitalization processes. Given the multiple aspects involved and the transdisciplinarity, the digital transformation strategy and roadmap of companies must be defined in a way that fits it to the corporate strategy, allowing digital transformation program to be implemented effectively, prioritizing internal sectors with the greatest strategic impact, generating greater productivity, business models improvement, security and, above all, competitiveness for the sector. This research aims to propose a model for the definition of the digital transformation strategy in the railway companies using the Balanced Scorecard (BSC) as a tool for strategic organizational analysis and multi-criteria decision-making (MCDM) to define the drivers for digital transformation in the railway. Finally, to demonstrate the proposal, a case study will be carried out at a Brazilian railway company. © 2021 The authors and IOS Press.

关键词： Decision making

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Multi-Objective Model Selection for Unmanned Aerial Vehicles Automatic Target Recognition systems

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IFAC-PapersOnLine 2017年第1期50卷 11607-11612页

作者： Alves Ribeiro V.H. Reynoso-Meza G. dos Santos Coelho L. Industrial and Systems Engineering Graduate Program (PPGEPS) Pontifical Catholic University of Parana (PUCPR) Curitiba Brazil Department of Electrical Engineering (PPGEE) Federal University of Parana (UFPR) Polytechnic Center Curitiba Parana Brazil

Within unmanned aerial vehicles on-going research topics, automatic target recognition is acquiring relevance. This is due to the easiness with which it is possible to acquire such devices. In order to do this, signal processing and classification techniques could be adopted. Also, in order to improve the classification ratio, optimization techniques could be used. This subject is object of study for many researchers, but the authors worry about the lack of information while using single-objective optimization techniques. The proposed work comprises the application of multi-objective optimization design in order to create an automatic target recognition system to discriminate different types of unmanned aerial vehicles. In order to accomplish this, a K-band radar system is used to send and receive electromagnetic signals, which are then processed with feature extraction techniques, and finally, applied on an artificial neural network system. In order to improve the system's classification ratio, the classifier is defined as a multi-objective problem, and evolutionary multi-objective optimization techniques are applied. Finally, in order to select the best possible trade-off, the level diagrams multi-criteria decision making methodology is used to compare different solutions. © 2017

关键词： Artificial neural networks Classifiers Electromagnetic signals Genetic algorithms Multi-objective optimization Optimization Signal processing

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