<img src="https://s3-us-west-1.amazonaws.com/wevolver-project-images/froala%2F1697527279301-Demo-28+copy.jpg" style="width: 300px;" class="fr-fic fr-dib">Additive Manufacturing is disrupting the industrial landscape - and, specifically, 3D printing of large-format components (Large Format Additive Manufacturing - LFAM) now represents a fast-growing niche market, with increasing demand from various sectors such as aerospace, marine, architecture, and design. Caracol is among the first and most renowned technology providers of large-format composite pellet-based additive manufacturing systems.&nbsp;&nbsp;Quality represents one of the greatest challenges for AM technologies: always guaranteeing the required functional performance and a high level of accuracy on final products is crucial to enable these technologies' adoption, especially for advanced and highly regulated applications. Continuous monitoring of&nbsp;process variables, machine status, and working environment conditions (e.g. temperature and humidity) all contribute to ensuring stability, repeatability, and efficiency of the production cycle.&nbsp;&nbsp;Caracol has been working&nbsp;assiduously to introduce this technology to its Heron AM systems, providing the highest degree of reliability and repeatability for its process. Monitoring systems usually detect possible threats and unexpected behaviors, performance issues, or non-compliance problems in a fully automated way, through a wide range of sensors installed on the machinery. By using algorithms and exploiting Machine Learning, data is extracted, stored, and displayed through real-time insightful visualizations. The automatic detection and signaling functions lay the foundation for&nbsp;in-line&nbsp;adjustments of process parameters. The automatic modification occurs while the printing process is taking place, to mitigate or correct the defect itself and thus preventing the generation of any production waste or non-conformities. In addition, the positive impact of monitoring and control systems, integrated into industrial 3d printers, results in significant optimization of the resources employed in the process. The reduction of the costs related to 'poor quality' leads to less waste while lowering the raw materials usage, machine and man hours needed, and time spent on inspection and post-production of defective parts.&nbsp;In this scenario, Caracol worked on the project&nbsp;RAMICoS - Intelligent Control System for Robotic Large Scale Additive Manufacturing System, a project financed by the Italian Ministry of Economic Development. Through the collaboration with MADE Competence Centre&nbsp;and Politecnico di Milano, the company aimed to achieve several objectives by the end of 2023:<ul><li data-leveltext="" data-font="Symbol" data-listid="3" data-list-defn-props='{"335552541":1,"335559684":-2,"335559685":720,"335559991":360,"469769226":"Symbol","469769242":[8226],"469777803":"left","469777804":"","469777815":"hybridMultilevel"}' data-aria-posinset="1" data-aria-level="1">Identifying common problems occurring during the printing process of Robotic LFAM Systems, defining and characterizing the related defects generated that affect the deposition of the extruded material;&nbsp;</li><li data-leveltext="" data-font="Symbol" data-listid="3" data-list-defn-props='{"335552541":1,"335559684":-2,"335559685":720,"335559991":360,"469769226":"Symbol","469769242":[8226],"469777803":"left","469777804":"","469777815":"hybridMultilevel"}' data-aria-posinset="1" data-aria-level="1">Identifying the types of sensors and their correct placement, to gather the appropriate data from process monitoring and control;&nbsp;&nbsp;</li><li data-leveltext="" data-font="Symbol" data-listid="3" data-list-defn-props='{"335552541":1,"335559684":-2,"335559685":720,"335559991":360,"469769226":"Symbol","469769242":[8226],"469777803":"left","469777804":"","469777815":"hybridMultilevel"}' data-aria-posinset="3" data-aria-level="1">Carry out testing and trial sessions to assess the effectiveness of the proposed sensors’ integration;&nbsp;</li><li data-leveltext="" data-font="Symbol" data-listid="3" data-list-defn-props='{"335552541":1,"335559684":-2,"335559685":720,"335559991":360,"469769226":"Symbol","469769242":[8226],"469777803":"left","469777804":"","469777815":"hybridMultilevel"}' data-aria-posinset="4" data-aria-level="1">Carry out a trial session to mitigate the risks related to material quality issues, regarding fiber-reinforced polymers and new materials;&nbsp;</li><li data-leveltext="" data-font="Symbol" data-listid="3" data-list-defn-props='{"335552541":1,"335559684":-2,"335559685":720,"335559991":360,"469769226":"Symbol","469769242":[8226],"469777803":"left","469777804":"","469777815":"hybridMultilevel"}' data-aria-posinset="4" data-aria-level="1">Identifying a data collection and analysis system, to detect and track anomalies while providing insights to support decision-making.&nbsp;</li></ul>For the project's execution phase, Caracol's LFAM system Heron AM was equipped with about ten sensors to track different parameters,&nbsp;including the system and its components' electricity consumption, the working environment’s temperature and humidity, the extrusion temperature, possible shocks and vibrations to the extruder, the energy consumed by the extruder’s screw motor, and many more. By registering all these signals and collecting data, a digital twin of the printed component was generated, enabling the identification of the areas affected by problems with material deposition. Moreover, Caracol developed&nbsp;machine-learning&nbsp;algorithms for automatic defect identification and alarm generation.The use of such tools can positively impact manufacturing processes and companies in several ways. First of all by facilitating the work of operators as decision-making support, it helps the duties of quality and maintenance departments by quickly identifying the root cause of problems, and it helps forecast the use of resources for production such as energy consumption by operational managers.&nbsp;Bringing this project to completion has opened a phase of further developments for Caracol. The company wants to implement&nbsp;these smart 3D printing solutions to ensure clients can benefit from being promptly notified of any occurring printing issues, and put into place automated corrective actions. The ultimate objective for Caracol is always ensuring the best quality of the printed components while minimizing costs and maximizing process efficiency.

Monitoring and automation systems are enabling factors to enhance productivity of innovative production technologies such as additive manufacturing. In this scenario, Caracol developed the project RAMICoS - Intelligent Control System for Robotic Large Scale Additive Manufacturing System, together with MADE Competence Center and Politecnico di Milano. 

Developing continous monitoring of 3D printing processes for large format additive manufacturing

In scientific and technical research, industrial product development, and other applications, computer simulations support work of all kinds. From semiconductor chips to electronic circuits to machine parts, engines, and other sophisticated mechanical systems &ndash; even chemicals and materials &ndash; digitally modeling industrial products makes it possible to accurately reproduce real-world electrical, mechanical, physical, and chemical phenomena on a computer. This approach also enables us to develop effective, efficient design or production processes without actually having to spend time, effort, and money on repeated prototyping and testing.<h2>Digital twins: virtual factory models</h2>Technologies for IoT, digital modeling, and advanced analysis meet in a form of simulation known as digital twinning that is becoming more and more valuable (Fig. 1). As a digital model on a computer, a digital twin fully reproduces factory functionality and conditions to support problem-solving or planning for higher productivity or quality. It is a fundamental technology essential in building cyber-physical systems (CPS), the core information system for smart factories.<img src="https://s3-us-west-1.amazonaws.com/wevolver-project-images/froala%2F1690281005828-smartfactory_06-1_en.png" style="width: 766px;" class="fr-fic fr-dib">Fig. 1: Differences between conventional simulation and analysis with digital twins&nbsp;Simulation to date has created digital models from design data and other data to predict product characteristics or behavior under the various analytical conditions applied. Digital twin analysis differs because data representing real-world properties or behavior detected by sensors or the like is applied to a digital model (based on design data) in real time. As actual objects age and deteriorate, and as their environment of use changes, their properties and behavior changes. Digital twins enable analysis that accounts for these realistic changes.Conventional simulation is used mainly to narrow down optimal conditions to meet the requirements, given many material and structural options at the time of design. By contrast, digital twins let us see how things that already exist may be a little in the future. They help us find operating conditions for more effective use of products, adopt optimal and efficient maintenance practices, and more.<h2>Benefits of peering slightly ahead, into the future of a factory</h2>In what situations are digital twins likely to be useful for smart factories?One application is creating production schedules for lines with variable-mix variable-lot production &nbsp;. Changeovers required in these production lines tend to disrupt or temporarily stop the flow of production, which reduces productivity. For mixed production lines making multiple products at once, it is extremely difficult to find a production schedule that accounts for these setup changes while maintaining maximum productivity of the factory as a whole.To create production schedules, manufacturers have traditionally relied on the experience of seasoned supervisors. By introducing digital twin analysis, manufacturers can rapidly conduct trial-and-error analysis of various production line operating conditions on a computer. This enables efficient changeovers in a production schedule that offers maximum productivity.As long as the behavior of line equipment is always reflected in the digital twin, manufacturers can also speed up the simulated passage of time on the computer to see what may happen in the future. The benefit of this, for example, lies in predicting and taking preventive action against several problems: short stoppages (which occur after an accumulation of out-of-sync operation between processes on the line), timing for replacement of consumable machine tool parts, and timing of failure due to fatigue.<h2>Technologies to implement and take advantage of digital twins</h2>Effective use of digital twinning hinges on creating high-precision digital twins that accurately capture properties and behavior close to those of real objects.This calls for modeling technology that applies knowledge of physics and chemistry, as well as sensor technology to collect data that accurately reflects conditions of objects at the factory (Fig. 2). Sensors used in these applications must also be environmentally resistant and highly reliable to collect highly accurate data even where they are subject to high temperatures, significant vibration, or electromagnetic noise.<img src="https://s3-us-west-1.amazonaws.com/wevolver-project-images/froala%2F1690281027157-smartfactory_06-2_en.jpg" style="width: 766px;" class="fr-fic fr-dib">Fig. 2: Fundamental technologies to implement digital twins&nbsp;Moreover, analyzing the digital twin to find optimal production and line operating conditions requires advanced information processing technology. Big-data analysis, AI, and perhaps one day, quantum computers may be combined for this purpose. It may be hard to imagine now, but we stand at the verge of an era where even smaller factories will be able to use AI and quantum computers through cloud services for digital twin analysis and factory improvement.Digital twin applications are not limited to smart factories. Digital twinning is being used to capture business activities across companies, distribution across supply chains, and even social activities across cities, as users seek optimal operation.<hr id="isPasted"><img data-fr-image-pasted="true" src="https://images.wevolver.com/eyJidWNrZXQiOiJ3ZXZvbHZlci1wcm9qZWN0LWltYWdlcyIsImtleSI6ImZyb2FsYS8xNjg5MDcyNzcyNTIxLTE2ODkwNzI3NzI1MjEucG5nIiwiZWRpdHMiOnsicmVzaXplIjp7IndpZHRoIjo5NTAsImZpdCI6ImNvdmVyIn19fQ==" class="fr-fic fr-dii">If you have any questions based on this article that you would like to discuss with an expert at Murata, don&#39;t hesitate to reach out. There&#39;s an entire team ready to support you with your question.<a class="button-main-action" href="https://www.murata.com/en-eu/contactform?&Detail=Wevolver%20request" rel="nofollow" target="_blank">GET IN TOUCH</a><hr>

Digital Twins

ORGANIZATIONS.

SHAPING THE INDUSTRY.

Siemens

Latest Posts

Developing continous monitoring of 3D printing processes for large format additive manufacturing

A Closer Look at Digital Twins, Virtual Factory Models that Expand the Potential of Their Real Counterpart