Industrial and Manufacturing Engineering, Automotive Engineering, Mechanical Engineering, Control and Systems Engineering
23
Scopus Publications
Scopus Publications
Machine Learning Predictive Model Applied to Detect Spaghetti Flaws in 3D Printing Guilherme Corsi Miranda da Silva, Isabella Nocito Santorsa, Erik Gustavo Del Conte, Ugo Ibusuki Journal of Advanced Manufacturing Systems, 2026 The Industry 4.0 concept aims at greater flexibility in manufacturing, mass customization, higher quality, and improved productivity. These concepts can be achieved through three-dimensional (3D) printing. This type of process is still subject to several flaws, so developing a warning system for this event is fundamental. The same printer can produce different objects, making development of a general alarm system difficult as previous researchers focused on data collection of vibration or temperature of 3D printers. This work aims to create a computer vision model capable of detecting Spaghetti flaws in fused deposition modeling (FDM) 3D printing. More than 3,000 images of printing processes with this kind of failure were collected and labeled, and more than 10 models based on the YOLOV5 architecture, employing a single convolutional neural network (CNN) that divides the image into a grid, were trained using deep learning models. The best-performing model was tested against a test dataset classifying completely new images. The model’s performance achieved approximately 73% accuracy.
Development of Embedded Electronics with Artificial Intelligence in an Implement for Forklifts Bruno Bueno Furquim, Italo Meneguello Pivetta, Ugo Ibusuki SAE Technical Papers, 2025 <div class="section abstract"><div class="htmlview paragraph">In this article we will discuss the development and implementation of a computer vision system to be used in decision-making and control of an electro-hydraulic mechanism in order to guarantee correct functioning and efficiency during the logistics project. To achieve this, we have brought together a team of engineering students with knowledge in the area of Artificial Intelligence, Front End and mechanical, electrical and hydraulic devices. The project consists of installing a system on a forklift that moves packaged household appliances that can identify and differentiate the different types of products moved in factories and distribution centers. Therefore, the objective will be to process this identification and control an electro-hydraulic pressure control valve (normally controlled in PWM) so that it releases only the hydraulic pressure configured for each type of packaging/product, and thus correctly squeezing (compressing) the specific volume, without damaging it due to excessive force, and without little force to the point of allowing the load to fall.</div></div>
Digital Transformation & Sustainability: Eliminating Paper Printing Through Data Digitalization Dioclécio Silveira Assis, Ailton Conde Jussani, Ugo Ibusuki SAE Technical Papers, 2025 <div class="section abstract"><div class="htmlview paragraph">This study explores how digital transformation in the automotive sector contributes to sustainability, particularly through the elimination of paper-based processes. Accelerated by the COVID-19 pandemic, the integration of digital technologies has optimized operations and reduced costs across the industry. The research focused on the impact of data digitalization on eliminating printed reports and documents, aligned with Sustainable Development Goals (SDGs) 8, 12, and 15.</div><div class="htmlview paragraph">A mixed-methods approach was used, combining qualitative and quantitative techniques. Data were collected via questionnaires and descriptive research conducted with employees of a multinational automotive company in São Paulo. Findings revealed that the transition to digital solutions eliminated the company’s average of 842 monthly printouts, improved process efficiency by 12%, and reduced data loss and rework.</div><div class="htmlview paragraph">Key initiatives included automating performance indicators, adopting electronic signatures, and promoting virtual meetings—measures that streamlined workflows and strengthened competitiveness. The majority of employees surveyed reported increased efficiency and fewer operational errors after digitalization. Furthermore, 90% acknowledged a positive impact on sustainability, citing significant reductions in paper consumption.</div><div class="htmlview paragraph">Benefits highlighted include task automation, improved communication, and faster decision-making. However, challenges such as the need for continuous training and technical support were also noted. Overall, the study concludes that digital transformation is vital for enhancing corporate sustainability and operational performance and recommends ongoing investment in employee training and digital infrastructure.</div></div>
Integration of Data Analytics and Data Mining for Machine Failure Mitigation and Decision Support in Metal–Mechanical Industry Sidnei Alves de Araujo, Silas Luiz Bomfim, Dimitria T. Boukouvalas, Sergio Ricardo Lourenço, Ugo Ibusuki, et al. Logistics, 2025 Background: The growing complexity of production processes in the metal–mechanical industry demands ever more effective strategies for managing machine and equipment maintenance, as unexpected failures can incur high operational costs and compromise productivity by interrupting workflows and delaying deliveries. However, few studies have combined end-to-end data analytics and data mining methods to proactively predict and mitigate such failures. This study aims to develop and validate a comprehensive framework combining data analytics and data mining to prevent machine failures and support decision-making in a metal–mechanical manufacturing environment. Methods: First, exploratory data analytics were performed on the sensor and logistics data to identify significant relationships and trends between variables. Next, a preprocessing pipeline including data cleaning, data transformation, feature selection, and resampling was applied. Finally, a decision tree model was trained to identify conditions prone to failures, enabling not only predictions but also the explicit representation of knowledge in the form of decision rules. Results: The outstanding performance of the decision tree (82.1% accuracy and a Kappa index of 78.5%), which was modeled from preprocessed data and the insights produced by data analytics, demonstrates its ability to generate reliable rules for predicting failures to support decision-making. The implementation of the proposed framework enables the optimization of predictive maintenance strategies, effectively reducing unplanned downtimes and enhancing the reliability of production processes in the metal–mechanical industry.
Deep Learning-Based Instance Segmentation for Enhanced Navigation of Agricultural Vehicles Renato De Avila Lopes, Marcus Vinicius Leal de Carvalho, Edson Kitani, Francisco De Assis Zampirolli, Leopoldo Yoshioka, et al. Revista De Informatica Teorica E Aplicada, 2025 This paper presents the development of a computer vision application based on the YOLOv8 network, designed to assist the navigation of autonomous vehicles on rural roads, particularly those found in sugarcane fields. The application employs instance segmentation to differentiate between navigable and non-navigable areas and detect obstacles such as pedestrians, vehicles, and other potential hazards. This information is used to generate an occupancy map that helps the navigation planner identify the safest and most efficient routes. The system was trained on a dataset containing 1.018 images, and the results demonstrate that instance segmentation significantly enhances the precision and safety of autonomous navigation in complex rural environments. The proposed approach is compatible with the ROS2 framework, using its structure for data integration and enabling real-time decision making.
Analysis of the Measurement of a Sphere with a 3D Scanner Layse Alves Savordelli, Arthur Ferreira, Ugo Ibusuki, Erik Gustavo Del Conte 2025 16th IEEE International Conference on Industry Applications Induscon 2025 Proceedings, 2025
Is lean manufacturing maturity a prerequisite for industry 4.0? Survey of SMEs in Japan and Brazil Osamu Tsukada, Ugo Ibusuki, Shigeru Kuchii, Anderson Tadeu de Santi Barbosa de Almeida International Journal of Lean Six Sigma, 2024 Purpose The purpose of this study is to explore the relationship between Lean manufacturing and Industry 4.0 for small and medium size of enterprise in Japan and Brazil. Design/methodology/approach The authors conducted a quantitative survey (20 companies in Japan and 30 companies in Brazil) combined with a qualitative interview (2 companies in Japan and 15 companies in Brazil). Findings According to the quantitative study, 90% of them practice Lean manufacturing and 40% of them practice Industry 4.0. In the qualitative study in Brazil, four managers responded that the Lean manufacturing is a prerequisite for Industry 4.0 since any production process with waste cannot be productive, even with sophisticated digitalization technology. Originality/value The authors explored further the relationship between “defensive Digital Transformation (DX),” which is based mainly on Lean manufacturing, and “offensive DX,” which relates to customer value creation through Industry 4.0. This study clarifies the relationship and plays as a roadmap to develop better the manufacturing from current status to the vision of Industry 4.0.
Power Supply Solutions to Enable the Development of eVTOL Aircrafts Ugo Ibusuki, Vinicius Mafra Viti SAE Technical Papers, 2023 <div class="section abstract"><div class="htmlview paragraph">The aerospace industry is undergoing a revolution with the large-scale development of eVTOL (Electric Vertical Take-Off &amp; Landing) and MEA (More Electric Aircraft). These aerial vehicles, many of them unmanned vehicles (UAV), will serve a variety of service-related functions: Search and Rescue (SAR), Medivac, delivery and lift operations, aerial mapping, and, of course, human transportation [<span class="xref">1</span>].</div><div class="htmlview paragraph">Despite its numerous functionalities, this type of vehicle has a serious problem, which is its usual batteries, the main means for its operation. Due to its autonomy not being so effective compared to its charging time, generating a considerable loss of time. In this context, it is necessary to find forms of components that can replace these batteries, so that the effective development of these vehicles is possible.</div><div class="htmlview paragraph">Studies done in other means of transportation point out that the use of hydrogen fuel cells has grown a lot. In this way, it is known that this type of fuel is seen as something of the future, but many companies have evaluated the possibility of implementation in cars, trains, and even airplanes. Therefore, a literature review will be presented about this new type of technology, demonstrating its advantages and its use. Finally, after careful analysis of the issues highlighted throughout the article, the best effective solution for replacing the batteries will be presented, a component that has caused major problems for the developers of this new aircraft [<span class="xref">28</span>].</div></div>
