In these years, the role of 3D concrete printing has been gaining importance among researchers and industrial stakeholders in the construction sector, due to sustainability, safety, and cost optimization reasons. However, this technology has still numerous challenges to face. Among them, this study aimed to shed a light on the extrusion phase of additive manufacturing, both providing guidelines for optimizing concrete filament extrusion and introducing innovative techniques to estimate and measure concrete yield stress. Several tools were employed to achieve these goals. Numerical printing simulations have been carried out to define rules for adjusting printing process parameters and concrete rheology in order to achieve the desired layer shapes. While printer parameters can be easily imposed, concrete rheology depends strictly on the mix design. Therefore, an artificial neural network has been developed as a tool for a performance-based 3D printable mix-design. This algorithm was implemented in a MATLAB tool able to estimate the static yield stress value of a mix given by the user. In order to validate its predictions, an experimental test was successfully derived from the traditional flow table test to assess the actual yield stress of concrete. New mortars, associated with printable values of static yield stress, were generated using the neural network and actually tested through the adapted procedure of the flow table test. Results suggest that neural networks may have a great potential in this context. More in general, the goal of assessing and controlling the concrete extrusion phase in 3D printing has been achieved.
Nel corso degli ultimi anni, la stampa 3D del calcestruzzo ha acquisito un ruolo sempre più importante tra i ricercatori e gli industriali del settore delle costruzioni, per ragioni di sostenibilità, sicurezza e ottimizzazione dei costi. Tuttavia, questa tecnologia si trova ancora di fronte a diverse sfide. Tra queste, questo studio mira a far luce sulla fase di estrusione della manifattura additiva, sia fornendo linee guida per ottimizzare l’estrusione di filamenti di calcestruzzo, sia introducendo tecniche innovative per stimare e misurare lo sforzo di scorrimento del calcestruzzo. Diversi strumenti sono stati impiegati per raggiungere questi scopi. Sono state condotte simulazioni numeriche di stampa al fine di definire regole per impostare i parametri di processo e le proprietà reologiche del materiale. Sebbene i parametri di processo possano essere facilmente impostati, la reologia del calcestruzzo dipende strettamente dal mix-design. Pertanto, una rete neurale artificiale è stata sviluppata per diventare uno strumento con cui formulare un mix-design stampabile. Questo algoritmo è stato implementato in un’applicazione MATLAB in grado di stimare lo sforzo di scorrimento statico di un mix fornito dall’utente. Per validare questa stima, una prova sperimentale è stata derivata con successo a partire dalla classica prova della tavola a scosse per determinare il reale sforzo di scorrimento del calcestruzzo. Nuove malte, associate a valori stampabili di sforzo di scorrimento, sono state generate utilizzando la rete neurale e poi realmente testate con la procedura adattata della prova della tavola a scosse. I risultati ottenuti dimostrano il grande potenziale delle reti neurali in questo contesto. Più in generale, l'obiettivo di valutare e controllare la fase di estrusione nella stampa 3D del calcestruzzo è stato raggiunto.
A combined experimental, numerical and AI-based approach to 3D-printability of cementitious composites
Meni, Simone
2023/2024
Abstract
In these years, the role of 3D concrete printing has been gaining importance among researchers and industrial stakeholders in the construction sector, due to sustainability, safety, and cost optimization reasons. However, this technology has still numerous challenges to face. Among them, this study aimed to shed a light on the extrusion phase of additive manufacturing, both providing guidelines for optimizing concrete filament extrusion and introducing innovative techniques to estimate and measure concrete yield stress. Several tools were employed to achieve these goals. Numerical printing simulations have been carried out to define rules for adjusting printing process parameters and concrete rheology in order to achieve the desired layer shapes. While printer parameters can be easily imposed, concrete rheology depends strictly on the mix design. Therefore, an artificial neural network has been developed as a tool for a performance-based 3D printable mix-design. This algorithm was implemented in a MATLAB tool able to estimate the static yield stress value of a mix given by the user. In order to validate its predictions, an experimental test was successfully derived from the traditional flow table test to assess the actual yield stress of concrete. New mortars, associated with printable values of static yield stress, were generated using the neural network and actually tested through the adapted procedure of the flow table test. Results suggest that neural networks may have a great potential in this context. More in general, the goal of assessing and controlling the concrete extrusion phase in 3D printing has been achieved.| File | Dimensione | Formato | |
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2024_04_Meni.pdf
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https://hdl.handle.net/10589/217496