Papers by Ricardo Silva Peres
Agronomy
Investment in technological research is imperative to stimulate the development of sustainable so... more Investment in technological research is imperative to stimulate the development of sustainable solutions for the agricultural sector. Advances in Internet of Things, sensors and sensor networks, robotics, artificial intelligence, big data, cloud computing, etc. foster the transition towards the Agriculture 4.0 era. This fourth revolution is currently seen as a possible solution for improving agricultural growth, ensuring the future needs of the global population in a fair, resilient and sustainable way. In this context, this article aims at characterising the current Agriculture 4.0 landscape. Emerging trends were compiled using a semi-automated process by analysing relevant scientific publications published in the past ten years. Subsequently, a literature review focusing these trends was conducted, with a particular emphasis on their applications in real environments. From the results of the study, some challenges are discussed, as well as opportunities for future research. Finall...
A Highly Flexible, Distributed Data Analysis Framework for Industry 4.0 Manufacturing Systems
Studies in Computational Intelligence, 2017
IEEE Access
The advent of the Industry 4.0 initiative has made it so that manufacturing environments are beco... more The advent of the Industry 4.0 initiative has made it so that manufacturing environments are becoming more and more dynamic, connected but also inherently more complex, with additional inter-dependencies, uncertainties and large volumes of data being generated. Recent advances in Industrial Artificial Intelligence have showcased the potential of this technology to assist manufacturers in tackling the challenges associated with this digital transformation of Cyber-Physical Systems, through its data-driven predictive analytics and capacity to assist decision-making in highly complex, non-linear and often multistage environments. However, the industrial adoption of such solutions is still relatively low beyond the experimental pilot stage, as real environments provide unique and difficult challenges for which organizations are still unprepared. The aim of this paper is thus twofold. First, a systematic review of current Industrial Artificial Intelligence literature is presented, focusing on its application in real manufacturing environments to identify the main enabling technologies and core design principles. Then, a set of key challenges and opportunities to be addressed by future research efforts are formulated along with a conceptual framework to bridge the gap between research in this field and the manufacturing industry, with the goal of promoting industrial adoption through a successful transition towards a digitized and data-driven companywide culture. This paper is among the first to provide a clear definition and holistic view of Industrial Artificial Intelligence in the Industry 4.0 landscape, identifying and analysing its fundamental building blocks and ongoing trends. Its findings are expected to assist and empower researchers and manufacturers alike to better understand the requirements and steps necessary for a successful transition into Industry 4.0 supported by AI, as well as the challenges that may arise during this process.
IEEE Access, 2019
Product dimensional variability is a crucial factor in the quality control of complex multistage ... more Product dimensional variability is a crucial factor in the quality control of complex multistage manufacturing processes, where undetected defects can easily be propagated downstream. The recent advances in information technologies and consequently the increased volume of data that has become readily available provide an excellent opportunity for the development of automated defect detection approaches that are capable of extracting the implicit complex relationships in these multivariate data-rich environments. In this paper, several machine learning classifiers were trained and evaluated on varied metrics to predict dimensional defects in a real automotive multistage assembly line. The line encompasses two automated inspection stages with several human-operated assembly and pre-alignment stages in between. The results show that non-linear models like XGBoost and Random Forests are capable of modelling the complexity of such an environment, achieving a high true positive rate and showing promise for the improvement of existing quality control approaches, enabling defects and deviations to be addressed earlier and thus assist in reducing scrap and repair costs. INDEX TERMS Machine learning, quality control, predictive manufacturing system, multistage, automotive industry, industry 4.0.
International Conference on Intelligent Systems (IS), 2018
The proliferation of Information and Communication Technologies allowed the development of new so... more The proliferation of Information and Communication Technologies allowed the development of new solutions to be applied at the shop-floor and all the tools which helps the manufacturers. Hence, new solutions such as cyber-physical production systems, data analytics and knowledge management were developed and proposed to solve the well-known issues, such as quality control in multistage manufacturing systems. However, those solutions can only have a small contribution in solving that issues compared to an optimized and fully integrated approach. To allow the development of a fully integrated environment, it is necessary to deliver a standard way to communicate and interact with the different functionalities. The proposed research aims to provide an integration layer, capable of translating the rules defined at the knowledge management level, structured as Decision Model and Notation rules, into an AutomationML based language. This allows the cyber-physical production system the ability to apply these rules near the shop-floor. This article presents the template defined to represent the rules in AutomationML as well as the infrastructure developed to receive the rules from the knowledge management, translate them and deliver to the cyber-physical production system. At the end of the article is presented a test bed where the solution is instantiated with rules focused on quality control.
IEEE 16th International Conference on Industrial Informatics (INDIN), 2018
In the last decades, several research initiatives suggested new solutions regarding the interoper... more In the last decades, several research initiatives suggested new solutions regarding the interoperability and interconnectivity among heterogeneous production components and all the actors that somehow interact within the shop-floor. However, most of the proposed data representations are focused on the description of the production capabilities. In this paper, it is proposed a common data model focused not only in the production capabilities of the different components as well as the description of all the events, variables and resources that could indicate quality issues. Hence, the proposed data model describes all the information required by the GO0DMAN solution to reduce as much as possible, the defects, the respective causes and the strategies to avoid the propagation along the line. In order to increase the adoption of the proposed data model, it was developed using AutomationML. The proposed data model was designed and tested within the scope of the Horizon 2020 GO0DMAN project.
Procedia Manufacturing, 2017
To meet flexibility and reconfigurability requirements, modern production systems need hardware a... more To meet flexibility and reconfigurability requirements, modern production systems need hardware and software solutions which ease the connection and mediation of different and heterogonous industrial cyber-physical components. Following the vision of Industry 4.0, the H2020 PERFoRM project targets, particularly, the seamless reconfiguration of robots and machinery. This paper describes the implementation of a highly flexible, pluggable and distributed architecture solution, focusing on several building blocks, particularly a distributed middleware, a common data model and standard interfaces and technological adapters, which can be used for connecting legacy systems (such as databases) with simulation, visualization and reconfiguration tools.
Computers in Industry, 2018
The manufacturing industry represents a data rich environment, in which larger and larger volumes... more The manufacturing industry represents a data rich environment, in which larger and larger volumes of data are constantly being generated by its processes. However, only a relatively small portion of it is actually taken advantage of by manufacturers. As such, the proposed Intelligent Data Analysis and Real-Time Supervision (IDARTS) framework presents a possible solution for the implementation of scalable, flexible and pluggable data analysis and real-time self-monitoring systems for manufacturing environments. IDARTS is aligned with the current Industry 4.0 trend, being aimed at allowing manufacturers to translate their data into a business advantage, combining distributed data acquisition, machine learning and run-time decision making support to assist in fields such as predictive maintenance and quality control, reducing the impact of disruptive events in production.
Service Orientation in Holonic and Multi-Agent Manufacturing, pp.373-381, 2017
In modern manufacturing, high volumes of data are constantly being generated by the manufacturing... more In modern manufacturing, high volumes of data are constantly being generated by the manufacturing processes. However, only a small percentage is actually used in a meaningful way. As part of the H2020 PERFoRM project, which follows the Industry 4.0 vision and targets the seamless reconfiguration of robots and machinery, this paper proposes a framework for the implementation of a highly flexible, pluggable and distributed data acquisition and analysis system, which can be used for both supporting run-time decision making and triggering self-adjustment methods, allowing corrections to be made before failures actually occur, therefore reducing the impact of such events in production.
IECON 2016 - 42nd Annual Conference of the IEEE Industrial Electronics Society, 2016
With the emergence of the Industry 4.0 concept, or the fourth industrial revolution, the industry... more With the emergence of the Industry 4.0 concept, or the fourth industrial revolution, the industry is bearing witness to the appearance of more and more complex systems, often requiring the integration of various new heterogeneous, modular and intelligent elements with pre-existing legacy devices. This challenge of interoperability is one of the main concerns taken into account when designing such systems-of-systems, commonly requiring the use of standard interfaces to ensure this seamless integration. To aid in tackling this challenge, a common format for data exchange should be adopted. Thus, a study to select the foundations for the development of such a format is hereby presented, taking into account the specific needs of four different use cases representing varied key European industry sectors.
Technological Innovation for Smart Systems, pp.125-134, 2017
With the advent of the Industry 4.0 movement, smart multiagent-based cyber-physical systems (CPS)... more With the advent of the Industry 4.0 movement, smart multiagent-based cyber-physical systems (CPS) are being more and more often proposed as a possible solution to tackle the requirements of intelligence, pluggability, scalability and connectivity of this paradigm. CPS have been suggested for a wide array of applications, including control, monitoring and optimization of manufacturing systems. However, there are several associated challenges in terms of validating and testing these systems due to their innate characteristics, emergent behavior, as well as the availability and cost of physical resources. Therefore, a dynamic simulation model constructed in V-REP is proposed as a way to test, validate and improve such systems, being applied to a data acquisition and pre-processing scenario as one of the key aspects of the interaction between a CPS and the shop-floor.
2015 10th International Symposium on Mechatronics and its Applications (ISMA), 2015
The manufacturing industry has been steadily evolving over the years, with new market trends enco... more The manufacturing industry has been steadily evolving over the years, with new market trends encouraging manufacturers to find new ways to meet the consumers' demands and quickly adapt to new business opportunities. Manufacturing systems are therefore required to be more and more agile and flexible in an environment dominated by unpredictable changes and disturbances. As a direct consequence several new solutions have been proposed, revolving around agility, flexibility, reconfigurability and modularity, enabling concepts such as Plug & Produce (P&P). Following this trend, the present article proposes a possible implementation for a multiagent-based knowledge extraction architecture to support P&P in flexible, distributed manufacturing monitoring systems. The validation process is also described, entailing the application of said system in a real industrial environment, more specifically monitoring two robotic cells performing the welding of a car's side member.
2015 IEEE 13th International Conference on Industrial Informatics (INDIN), 2015
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Papers by Ricardo Silva Peres