RESULTS
Scientific Publications
A robot-based inspecting system for 3D measurement
Author: Marche Polytechnic University
Zero Defect Manufacturing aims to minimize the number of defects within a process through proper measurement and control that make possible defect prediction and prevention. This procedure should ideally be performed in a non-destructive approach. Hence, this paper presents two novel non-destructive measurement systems for the geometric control of metal bars concerning a plant producing steel parts. The aforementioned systems are designed to be integrated into the actual process with minimum intervention supporting the online quality control of the manufactured product.The two measurement systems exploit an industrial robotic manipulator and an optical sensor mounted on the robot’s end effector. They differ in the strategy of motion of the laser line triangulation sensor relative to the steel part to be measured. The proposed systems have been implemented as prototypes and deployed at the premises of a steel manufacturer to test and validate their performance, with the preliminary findings being provided and discussed in this work.
Beam Straightness Measurement with Laser Triangulation System: a steel industry use case
Author: Marche Polytechnic University
With reference to a steel bars manufacturing process, there is a variety of factors which can contribute to defect generation such as geometrical non-conformity. Nondestructive Inspection systems (NDIs) are a key element for the early detection of defects in a production line. This paper considers a particular steel industry use case focusing on the design and development of an NDI system to measure the straightness of steel bars in line, by a non-intrusive approach. This NDI is based on the laser line triangulation technique, interacts with a robot and is connected to a software platform where additional services may be exposed. The paper presents a parametric study of the laser line triangulation system to be developed, highlighting the influence of design parameters over system resolution and measurement range. Considering the use case this paper focuses on, the straightness deviation can be correctly estimated as the resolution value can be extremely fine: 0.01 mm if using subpixel accuracy. The steel beam is ideally modelled as a parallelepiped, however in reality its shape can deviate: this causes uncertainty due to the model of the measurand. The paper then discusses this uncertainty and the one related to the misalignment of the laser plane with respect to the beam axis. Results show erroneous estimation of the straightness deviation up to 1.8 mm in some cases of beam distortions analysed. The simulation presented in the paper is therefore of primary importance to evaluate the factors which may influence the overall uncertainty of the straightness measurement process.
Integration of Non-Destructive Inspection (NDI) systems for Zero-Defect Manufacturing in the Industry 4.0 era
Author: Marche Polytechnic University
Industry 4.0 paradigm and its enabling technologies, such as Internet of Things (IoT), have increased the potential of industrial automation with the growing implementation of Cyber-Physical Production Systems (CPPS). They improve the production processes thanks to an optimal use of data. Based on the Reference Architecture Model Industry 4.0 (RAMI 4.0) and the Asset Administration Shells (AAS), according to standards and communication protocols, the European project OpenZDM is realizing an open platform to perform the Zero-Defect Manufacturing paradigm. The platform will be developed and demonstrated in five representative production lines combining ICT solutions and innovative Non-Destructive Inspection systems (NDI). NDI systems will be used as an Industry 4.0 enabling technology to collect and analyze data from the physical assets and convert them to value-added information, as an IoT technology. A relevant example of an NDI system is presented in this paper, based on the acquisitions of an infrared camera, to focus on the business logic unit and the OT/IT convergence and integration.
Laser Line Triangulation Sensor With Wide Measurement Range: A Steel Industry Use Case
Author: Marche Polytechnic University
Laser line triangulation is a key measurement technology that can be applied in quality control processes to inspect product geometry without contact. This paper presents the design of a Laser Line Triangulation (LLT) sensor developed in the context of the Horizon Europe (HE) project openZDM and tested inline in a steel industry use case.The LLT sensor has been developed focusing on two main target objectives: wide measurement range along transversal (X) and axial (Z) directions and small resolution along the Z axis. These have been achieved thanks to the optimization of the design parameters of the system which has been implemented with a triangulation angle of 45° reaching over a stand-off distance of 1000 mm and a 1000 mm camera-laser distance. The overall measurement range results in a transversal Field-OfView (X-FOV) from 800 mm to 2000 mm and a minimum Z distance of 630 mm in the near field up to 1580 mm in the far field.The resolution along the Z axis has been enhanced thanks to the introduction of subpixel accuracy reaching 0.026 mm at the stand-off distance which is 1000 mm. In this paper, these values are compared to other Laser Line Triangulation sensors available on the market showing that the system developed by UNIVPM is innovative.The Laser Line Triangulation sensor has been also tested inline and this paper presents some results of the laser profiles acquired on a steel bar held by a robot. These laser profiles are then processed in order to detect geometric defects, in particular the non-straightness of the bar, using an algorithm properly developed for the use case. From the results of these feasibility tests, the application has proven effective and opens the path to a full-inline installation, in order to measure non-straightness on 100% of the products.
A methodology to assess circular economy strategies for sustainable manufacturing using process eco-efficiency
Author: Laboratory for Manufacturing Systems & Automation (LMS)
This study discusses an extended eco-efficiency indicator targeted at a manufacturing process level, facilitating decision support on the selection of circular economy strategies that a company may adopt to improve its environmental impact. The impact of non-zero-defect manufacturing processes is coupled with the process’s environmental impact and associated cost to derive a new indicator that can support decision-making at a process level regarding the adoption of more sustainable strategies.
A hybrid digital twin approach for proactive quality control in manufacturing
Author: Laboratory for Manufacturing Systems & Automation (LMS)
Quality control is a critical aspect in today’s fast-paced and competitive business landscape. The increasing digital transformation of manufacturing companies allows for the implementation of even proactive quality control strategies. This, however, requires the proper integration and analysis of shop floor data, regarding monitoring, diagnosis, and prognostics. This is to support defect recognition and recuperation, along with potential system reconfiguration based on knowledge extraction and human experience integration. Digital twins, being virtual replicas of physical assets, support real-time monitoring, analysis, and optimization. However, quality-related aspects may not be related to monitored parameters, thus solely data-driven models may not be accurate enough for proactive quality control. In this work, a hybrid digital twin is proposed, where data-driven models are used to finetune the behaviour of the digital twin based on its physics model. A use case concerning an industrial asset and the heat transfer to a steel bar is investigated with the results presented and commented on.
3D point cloud analysis for surface quality inspection: A steel parts use case
Author: Laboratory for Manufacturing Systems & Automation (LMS)
A manufacturing process includes inspecting the product to verify it meets its quality standards. Such steps, however, are time-consuming and, depending on the means, prone to errors. If not identified in time, defects occurring at an early step of a manufacturing process may result in significant waste, especially if the product is not easy to re-work. Today, however, the combination of AI with computer vision technologies can enable manufacturers to transform quality inspection by automating the detection of defects. This study discusses the use of products’ 3D shape for inline surface defect detection, facilitating the adoption of proactive control strategies facilitating the reduction of waste. The product’s 3D shape, represented by a point cloud is acquired by two fixed laser triangulation sensors orthogonally arranged. The K-means method is adopted for the point cloud data analysis, while Voxel Grid filters are used for downsampling to reduce computational time. The proposed approach has been evaluated in a use case related to the production of steel parts, with the findings supporting that an in-line implementation can facilitate the detection of surface or geometry defects, which, in turn, may facilitate the reduction of waste, by avoiding further processing of the defective product.
A process-level LCA for evaluating the contribution of digitalization in the greening of a manufacturing system
Author: Laboratory for Manufacturing Systems & Automation (LMS)
Life Cycle Assessment (LCA) methodology is usually applied to products’ lifecycle and value chains to identify their environmental performance and identify circular economy strategies and options. As a result, different product configurations and strategies may be evaluated and compared, ensuring a positive environmental balance. However, LCA is not usually focused on a manufacturing process level. In this study, LCA is performed at a process level to assess the correlation between digitalization and green manufacturing. The GaBi professional database was used for assessing the manufacturing gate-to-gate processes.
A Zero-Shot Learning Approach for Task Allocation Optimization in Cyber-Physical Systems
Author: Laboratory for Manufacturing Systems & Automation (LMS)
The design and reorganization of Cyber-Physical Systems (CPSs) faces challenges due to the growing number of interconnected devices. To effectively handle disruptions and improve performance, rapid CPS design and development is crucial. The Task Resources Estimator and Allocation Optimizer (TREAO) addresses these challenges, by simulating and optimizing the tasks assignment to the CPS machines, recommending suitable software layouts for the CPS characteristics. It employs Zero-Shot Learning (ZSL) to predict task requirements in heterogeneous devices, enabling the characterization of software pipeline execution in distributed systems. The Genetic Algorithm (GA) component then optimizes the task assignment across available machines. Through experiments, the tool is evaluated for task characterization, CPS modeling and optimization performance. TREAO, when compared with similar tools, allows the simulation of more resource usage metrics (CPU, RAM, processing time and network delay) and increases flexibility in heterogeneous CPSs by predicting the task execution behavior and optimizing the task assignment.
Data Analytics and AI for Quality Assurance in Manufacturing: Challenges and Opportunities
Author: Laboratory for Manufacturing Systems & Automation (LMS)
Data analytics and Artificial Intelligence (AI) have emerged as essential tools in manufacturing over recent years, providing better insight into production systems. Their importance can be highlighted by the way it can transform quality control, from prescriptive to proactive. Data analytics combined with AI can identify abnormal trends and patterns in huge amounts of data, that could uncover potential defects and allow pre-emptive action to minimize or even prevent these from happening. A direct effect of this is the contribution to waste reduction, as well as saving time and resources. While data in a digital factory is ample and the resources for developing artificial intelligence applications are accessible, the implementation of accurate, robust, standard, and economically viable quality monitoring and assessment approaches is not straightforward. This is also strengthened by the scarce skillset in today’s manufacturing companies in this area. In this study, the capabilities and potential of data analytics combined with AI are reviewed with a focus on manufacturing. The implementation challenges posed for a practitioner, as well as the benefits of implementing a solution for a manufacturer using data analytics and AI for quality assessment are discussed, based on real-world experiences from existing production environments. Lastly, a learning approach utilizing a high-fidelity digital twin at its core is presented which a practitioner can utilize to create, test and continuously improve a predictive analytics model.
A Zero-Shot Learning Approach for Task Allocation Optimization in Cyber-Physical Systems
Author: University of Porto
A manufacturing process includes inspecting the product to verify it meets its quality standards. Such steps, however, are time-consuming and, depending on the means, prone to errors. If not identified in time, defects occurring at an early step of a manufacturing process may result in significant waste, especially if the product is not easy to re-work. Today, however, the combination of AI with computer vision technologies can enable manufacturers to transform quality inspection by automating the detection of defects. This study discusses the use of products’ 3D shape for inline surface defect detection, facilitating the adoption of proactive control strategies facilitating the reduction of waste. The product’s 3D shape, represented by a point cloud is acquired by two fixed laser triangulation sensors orthogonally arranged. The K-means method is adopted for the point cloud data analysis, while Voxel Grid filters are used for downsampling to reduce computational time. The proposed approach has been evaluated in a use case related to the production of steel parts, with the findings supporting that an in-line implementation can facilitate the detection of surface or geometry defects, which, in turn, may facilitate the reduction of waste, by avoiding further processing of the defective product.
0-DMF - A Decision-Support Framework for Zero Defects Manufacturing
Author: University of Porto
Manufacturing companies are increasingly focused on minimising defects and optimising resource consumption to meet customer demands and sustainability goals. Zero Defect Manufacturing (ZDM) is a widely adopted strategy to systematically reduce defects. However, research on proactive defect-reducing measures remains limited compared to traditional defect detection approaches. This work presents the 0-DMF decision support framework, which employs data-driven techniques for defect reduction through (1) defect prediction, (2) process parameter adjustments to prevent predicted defects, and (3) clarifying prediction factors, providing contextual information about the manufacturing process. For defect prediction, Machine Learning (ML) algorithms, including XGBoost, CatBoost, and Random Forest, were evaluated. For process parameter adjustments, optimisation algorithms such as Powell and Dual Annealing were implemented. To enhance transparency, Explainable Artificial Intelligence (XAI) method s, including SHAP and LIME, were incorporated. Tailored for the melamine-surfaced panels process, the methods showed promising results. The defect prediction model achieved a recall value of 0.97. The optimisation method reduced the average defect probability by 28 percentage points. The integration of XAI enhanced the framework’s reliability. Combined into a unified tool, all tasks delivered fast results, meeting industrial time constraints. These outcomes signify advancements in predictive quality through data-driven approaches for defect prediction and prevention.
HyPredictor - Hybrid Failure Prognosis Approach combining Data-Driven and Knowledge-Based Methods
Author: University of Porto
In modern manufacturing, marked by an unprecedented surge in data generation, utilising this wealth of information to enhance company performance has become essential. Within the industrial landscape, one of the significant challenges is equipment failures, which can result in substantial financial losses and wasted time and resources. This work presents the HyPredictor framework, a comprehensive failure prediction and reporting system designed to enhance the reliability and efficiency of industrial operations by leveraging advanced machine learning techniques and domain knowledge. Six machine learning algorithms were evaluated for failure prediction. The predictions from the algorithms are then refined using rule-based adjustments derived from domain knowledge. Additionally, Explainable Artificial Intelligence (XAI) techniques were incorporated, as well as the capability of users to customise the system with their own rules and submit failure reports, prompting model retraining and continuous improvement. Integrating domain-specific rules improved the performance by up to 28 percentage points in the F1 Score metric in some prediction models, with the best hybrid approach achieving an F1 Score of 90% and a Recall of 92% in failure prediction. This adaptive, hybrid approach improves prediction accuracy and fosters proactive maintenance, significantly reducing downtime and operational costs.
Data-Driven Predictive Maintenance for Component Life-Cycle Extension
Author: University of Porto
In the era of Industry 4.0, predictive maintenance is crucial for optimizing operational efficiency and reducing downtime. Traditional maintenance strategies often cost more and are less reliable, making advanced predictive models appealing. This paper assesses the effectiveness of different survival analysis models, such as Cox Proportional Hazards, Random Survival Forests (RSF), Gradient Boosting Survival Analysis (GBSA), and Survival Support Vector Machines (FS-SVM), in predicting equipment failures. The models were tested on datasets from Gorenje and Microsoft Azure, achieving C-index values on test data such as 0.792 on the Cox Model, 0.601 using RSF, 0.579 using the GBSA model and 0.514 when using the FS-SVM model. These results demonstrate the models’ potential for accurate failure prediction, with FS-SVM showing significant improvement in test data compared to its training performance. This study provides a comprehensive evaluation of survival analysis methods in an industria l context and develops a user-friendly dashboard for real-time maintenance decision-making. Integrating survival analysis into Industry 4.0 frameworks can significantly enhance predictive maintenance strategies, paving the way for more efficient and reliable industrial operations.
Design of an ISO 23247 Compliant Digital Twin for an Automotive Assembly Line
Author: Polytechnic Institute of Bragança
The integration of Industry 4.0 (I4.0) technologies is transforming various society sectors, and particularly the manufacturing sector, promoting a digital transformation that fosters smart and interconnected systems. The Digital Twin (DT) acts as a key component in the digital transformation landscape and its integration with 14.0 technologies paves the way for the implementation of Zero Defect Manufacturing strategies. A spotlight on the automotive industry underscores the power of DT applications for real-time defect detection and improvement of product quality and process efficiency. However, the proper DT implementation, contributing to their integration and interoperability, requires the compliance with standards and reference architectures. Having this in mind, this paper describes the design of a DT architecture compliant with the ISO 23247 standard and aligned with the RAMI 4.0 to cover the dimensional measurement process comprising inspection stations placed in the body shop area of an automotive assembly line. This implementation enables the real-time and early identification of defects along the assembly process, allowing to prevent their occurrence at a single stage and their propagation to downstream processes.
Positioning Cyber-Physical Systems and Digital Twins in Industry 4.0
Author: Polytechnic Institute of Bragança
Industry 4.0 has brought innovative concepts and technologies that have greatly improved the development of more intelligent, flexible and reconfigurable systems. Two of these concepts, Cyber-Physical Systems (CPSs) and Digital Twins (DTs), have gained significant attention from various stakeholders, e.g., researchers, industry practitioners, and governmental organizations. Both are vital to support the digitalisation of products, machines, and systems, and they focus on the integration of physical and cyber processes, where one affects the other through feedback loops. Having this in mind, this paper aims to better understand how CPS and DT are correlated, particularly exploring their similarities and differences, their positioning within the Industry 4.0 paradigm, and their convergence to develop Industry 4.0 solutions. Some research challenges to develop Industry 4.0 solutions by integrating these concepts are also discussed.
Exploring Digital Twin Dynamics: An Analysis of Structure Configurations
Author: Polytechnic Institute of Bragança
Digital twin (DT) is an important technology to support the realization of the digital transformation, connecting the physical asset to its virtual copy and fostering realtime monitoring, simulation, and decision-support. However, the benefits of DT depend on its intended purpose and application, which is impacted by the design structure configuration that is used for its implementation. This paper discusses the advantages and challenges of considering different organizational structures for the implementation of DTs, namely centralised, hierarchical and decentralised, complemented with a case study that was used to analyse the implementation of DT focusing on centralised and decentralised approaches. Additionally, the paper includes an analysis of the main aspects of DT structures and their design guidelines, the key enabling technologies and the main challenges of distributing DTs.
Digitalization of Industrial Inspection Assets through the Asset Administration Shell
Author: Polytechnic Institute of Bragança
Developments in industrial manufacturing systems, through the use of new digital technologies, are revolutionizing the traditional means by which companies operate in terms of productivity and competitiveness. The adoption of technologies related to Industry 4.0 (I4.0) has led to the possibility of digitizing industrial assets to achieve more responsive, reconfigurable and efficient production systems. The Asset Administration Shell (AAS) corresponds to a form of digital representation of a physical asset, such as, a machine or device, that describes its properties, functionalities, and behaviours, as well as guarantee interoperability between different systems. Implementing AAS in the industrial environment allows the creation of a standard for digitalising asset scenarios, which can be used for intelligent and non-intelligent products. This paper explores modelling geometric inspection assets on an automotive assembly line using AAS standards. Once modelled, the AAS of the assets are applied to a new approach for reactive AAS based on a REST API for real-time data transmission to a framework focused on Zero Defects Manufacturing. The implications associated with the asset modelling and the creation of the AAS server are presented and discussed, giving a critical opinion on the current status of the technologies associated with AAS in I4.0.
An Augmented Reality Intelligent Guide for the Automotive Industry
Author: Polytechnic Institute of Bragança
Throughout the 21st century, there has been a rise in interest of increasing inter-connectivity and smart automation in the realms of industrial production, often called the 4th Industrial Revolution. Thus, interest in areas such as virtual, mixed, and augmented reality has increased as new devices and technologies related to these areas are seen as a possible solution to increase Industrial efficiency, create safer work environments for employees, and more effective training. In this project, a HoloLens app was developed, capable of identifying and showing to the user various zones where a specific vehicle in a production line requires checking and in which users have full spatial perception. Each vehicle is composed of several zones and each of these zones is associated with specific stations in a sequential order, so the user will only be able to see the zones associated with the station that is being treated. Using a specific “gesture” users can change the zone’s status in the database to indicate that the zone has been checked. These changes will be visible and the vehicle, stations, and zones will be updated to reflect the modifications. Various scripts in C# and PHP were used to allow modifications to the behaviour of the objects and database in the augmented reality scene through the access to a RESTful service in Unity.
