Periodic Reporting for period 3 - FASTEN (Flexible and Autonomous Manufacturing Systems for Custom-Designed Products)
Période du rapport: 2019-11-01 au 2021-04-30
The 4th industrial revolution emerged in Germany in 2011 and is now a global innovation paradigm for the manufacturing industry worldwide. Starting from its initial conception of full adoption of Cyber Physical Production Systems, the Industry 4.0 paradigm has extended its scope to a broader set of technologies and to the most vital processes in the product and production systems lifecycle. The terms Industrial Internet of Things, Additive Manufacturing, Robotics, Mass Customisation, Product-Service Systems and Sustainable Manufacturing are key cornerstones and top priority challenges. However, in the context of an increasing demand on the diversity of products, products with shorter life cycles, and low volumes per order, the gap between the leading edge and lagging behind countries risks to become a serious problem, in the absence of a solid and inclusive innovation policy.
In line with the Industry 4.0 paradigm and the EU-Brazil collaboration program, the FASTEN project aims to be a key enabler of the full adoption of IoT technologies in digital manufacturing businesses, by demonstrating such technologies on both sides of the Atlantic. As such, FASTEN “mission” is to develop, demonstrate, validate, and disseminate an integrated and open modular framework (Figure 1) for efficiently producing highly customized products. This ios achieved by pairing digital integrated service/products to additive manufacturing processes, on top of tools for decentralizing decision-making and data interchange. Software technologies are applied to build a fully connected additive manufacturing system by exploiting robotic, automation, simulation, as well as optimization and prescriptive analytics technologies. Demonstration of these technologies were performed in two pilot cases: ThyssenKrupp Elevators in Brazil and Embraer Portugal in Europe (aeronautics).
The specific objectives of FASTEN comprise the following:
-To develop and demonstrate a flexible and scalable robotic system and its integration with mass customisation production lines,
-To design, assemble and demonstrate a Reference Architecture for Industrial IoT and Industrial Analytics made of open source components,
-To develop and demonstrate a real-time application for monitoring the performance of manufacturing and logistics systems, using simulation, optimization and predictive analytical tools,
Strategically, the project aims to foster the digital manufacturing sustainability and be an enabler of technology development between Brazil and Europe, thus contributing to the competitiveness of Brazilian and European industries.
Major results to be delivered are identified in Figure 2.
In line with the Industry 4.0 paradigm and the EU-Brazil collaboration program, the FASTEN project aims to be a key enabler of the full adoption of IoT technologies in digital manufacturing businesses, by demonstrating such technologies on both sides of the Atlantic. As such, FASTEN “mission” is to develop, demonstrate, validate, and disseminate an integrated and open modular framework (Figure 1) for efficiently producing highly customized products. This ios achieved by pairing digital integrated service/products to additive manufacturing processes, on top of tools for decentralizing decision-making and data interchange. Software technologies are applied to build a fully connected additive manufacturing system by exploiting robotic, automation, simulation, as well as optimization and prescriptive analytics technologies. Demonstration of these technologies were performed in two pilot cases: ThyssenKrupp Elevators in Brazil and Embraer Portugal in Europe (aeronautics).
The specific objectives of FASTEN comprise the following:
-To develop and demonstrate a flexible and scalable robotic system and its integration with mass customisation production lines,
-To design, assemble and demonstrate a Reference Architecture for Industrial IoT and Industrial Analytics made of open source components,
-To develop and demonstrate a real-time application for monitoring the performance of manufacturing and logistics systems, using simulation, optimization and predictive analytical tools,
Strategically, the project aims to foster the digital manufacturing sustainability and be an enabler of technology development between Brazil and Europe, thus contributing to the competitiveness of Brazilian and European industries.
Major results to be delivered are identified in Figure 2.
The project started in November 2017 and concluded on April 2021. The initial focus was on the establishment of common working procedures, management practices and required supporting tools for managing the project. Shortly after, the consortium focused on the specification of the IIoT Reference Architecture and technology selection, on the robot and additive manufacturing systems integration architecture, and on the specification of both industrial use-cases. The use case experimentation was achieved thought two laboratorial and two industry based iterations. Progressively, the developed tools were integrated, deployed, tested and validated.
The project identified the following individual results for exploitation:
-3D-based Object Perception Framework.
-Open Scalable Production System, an Industry 4.0 framework for integration of Cyber-Physical Systems with existing Production Systems.
-Skill-based Navigation and Localization for Mobile Robots.
-Horizontal Interoperability through a ROS - CODESYS Bridge.
-Robotic Skills for kitting operations.
-Real-time 2D-based Object Detection system.
-A CAD based supervised bin-picking real-time Object Detection system.
-An agnostic bin-picking real-time 3D-based Object Detection system.
-Visual Path Following Control for Automated Guided Vehicles (AGVs).
-Vision-Based Grasping System.
-Deep learning neural networks for vision-based object detection.
-3D environment mapping using visual and Lidar sensors.
-Map-matching algorithm applied to localization of AGVs.
-Open source industrial IOT platform based on Apache and FIWARE components.
-Component for Sensor Ranking in IIoT.
-Predictive Simulator-optimizer tool for additive manufacturing systems.
-Predictive Simulator-Optimizer Consultancy Services for assembly lines and supply chains.
-Adaptive Pick & Place Robot for Logistics - Mobile Pick & Place Robot for logistic warehouses.
-Smart Robotic Additive Manufacturing (SRAM) unit.
-Enhanced Decision and Management for Assembly Lines.
-SMN - Smart Manufacturing Network.
-Overall Equipment Effectiveness decision tool in Additive Manufacturing Supply Chain Networks.
The project identified the following individual results for exploitation:
-3D-based Object Perception Framework.
-Open Scalable Production System, an Industry 4.0 framework for integration of Cyber-Physical Systems with existing Production Systems.
-Skill-based Navigation and Localization for Mobile Robots.
-Horizontal Interoperability through a ROS - CODESYS Bridge.
-Robotic Skills for kitting operations.
-Real-time 2D-based Object Detection system.
-A CAD based supervised bin-picking real-time Object Detection system.
-An agnostic bin-picking real-time 3D-based Object Detection system.
-Visual Path Following Control for Automated Guided Vehicles (AGVs).
-Vision-Based Grasping System.
-Deep learning neural networks for vision-based object detection.
-3D environment mapping using visual and Lidar sensors.
-Map-matching algorithm applied to localization of AGVs.
-Open source industrial IOT platform based on Apache and FIWARE components.
-Component for Sensor Ranking in IIoT.
-Predictive Simulator-optimizer tool for additive manufacturing systems.
-Predictive Simulator-Optimizer Consultancy Services for assembly lines and supply chains.
-Adaptive Pick & Place Robot for Logistics - Mobile Pick & Place Robot for logistic warehouses.
-Smart Robotic Additive Manufacturing (SRAM) unit.
-Enhanced Decision and Management for Assembly Lines.
-SMN - Smart Manufacturing Network.
-Overall Equipment Effectiveness decision tool in Additive Manufacturing Supply Chain Networks.
At the month 12 of the project, progress has been achieved on the major FASTEN pillars:
-Integration of Robot and Additive Manufacturing Systems – an open scalable architecture has been designed and partly implemented, comprising a 3D virtual representation of manufacturing lines and logistic areas, an execution manager able to assign and follow tasks defined in a production schedule by a Manufacturing Execution System and an orchestrator of navigation/logistic/assembly tasks by means of robotic manipulators. A first version of 2D and 3D object recognizer algorithms proved the feasibility of recognising the different parts to be manipulated in the two targeted use cases. This progress will be materialized in the automation of kit assembly activities in logistic areas and in the automation of machine tending operations involving robotic and 3D printer solutions.
-Open Source Industrial IoT Platform – a Reference Architecture was defined and two instances demonstrated in laboratory environment, based on available Open Source software coming from two different communities: FIWARE (mostly Europe based) and Apache (worldwide based). First deployment of the FASTEN IoT Platform proved the feasibility of connecting physical entities in the shop floor (like robotic systems and manufacturing equipment) with factory level applications for controlling and monitoring production (e.g. energy consumption and alarm generation) and by using two different sets of technology. Its impact will be mostly measured by the time and effort needed to connect existing manufacturing equipment with control and monitoring applications.
-Predictive Real Time Simulation and Optimization – the main goal of defining and applying simulation and optimization techniques to support decision-makers in strategic and operational planning has been achieved in the realm of a supply chain, comprised by additive manufacturing cells spread on different locations in Brazil and of different entities ordering the manufacturing of single parts in the provision of local maintenance services.
At the end of second year, most of the tools developed in the project were assembled together and tested first in a laboratory context and then in the industry environment of the two use cases defined in the project. On the third and half year, all the developed results were integrated and finally tested and validated in a laboratory context. The forth and last iteration of the use case experimentation planned for industrial settings was impacted by the COVID-19 pandemic and realized in several laboratories and one industrial setting.
-Integration of Robot and Additive Manufacturing Systems – an open scalable architecture has been designed and partly implemented, comprising a 3D virtual representation of manufacturing lines and logistic areas, an execution manager able to assign and follow tasks defined in a production schedule by a Manufacturing Execution System and an orchestrator of navigation/logistic/assembly tasks by means of robotic manipulators. A first version of 2D and 3D object recognizer algorithms proved the feasibility of recognising the different parts to be manipulated in the two targeted use cases. This progress will be materialized in the automation of kit assembly activities in logistic areas and in the automation of machine tending operations involving robotic and 3D printer solutions.
-Open Source Industrial IoT Platform – a Reference Architecture was defined and two instances demonstrated in laboratory environment, based on available Open Source software coming from two different communities: FIWARE (mostly Europe based) and Apache (worldwide based). First deployment of the FASTEN IoT Platform proved the feasibility of connecting physical entities in the shop floor (like robotic systems and manufacturing equipment) with factory level applications for controlling and monitoring production (e.g. energy consumption and alarm generation) and by using two different sets of technology. Its impact will be mostly measured by the time and effort needed to connect existing manufacturing equipment with control and monitoring applications.
-Predictive Real Time Simulation and Optimization – the main goal of defining and applying simulation and optimization techniques to support decision-makers in strategic and operational planning has been achieved in the realm of a supply chain, comprised by additive manufacturing cells spread on different locations in Brazil and of different entities ordering the manufacturing of single parts in the provision of local maintenance services.
At the end of second year, most of the tools developed in the project were assembled together and tested first in a laboratory context and then in the industry environment of the two use cases defined in the project. On the third and half year, all the developed results were integrated and finally tested and validated in a laboratory context. The forth and last iteration of the use case experimentation planned for industrial settings was impacted by the COVID-19 pandemic and realized in several laboratories and one industrial setting.