The objective of this work is to implement and validate methodologies for predictive maintenance of agricultural equipment, in collaboration with Maschio Gaspardo S.p.A. The overall aim is to develop a digital twin of the Campo 32 sprayer (ALA400 version), namely a virtual replica of the real component on which fatigue strength analyses can be performed based on the real-time loads acting on the structure, within a predictive maintenance framework. Variable amplitude fatigue tests were carried out on axial specimens representative of the geometry and dimensions of the welded joints in the actual structure. The load spectrum adopted for these tests was derived from strain signals obtained through combined accelerometric and strain gauge measurements of the sprayer operating under real service conditions; these measurements were acquired as part of this thesis work. Furthermore, a preliminary finite element analysis was conducted using the “Ansys Twin AI” extension within the Ansys software environment. In addition, constant amplitude fatigue tests were performed on full-scale components to assess their fatigue behavior and to validate the Peak Stress Method.
Lo scopo è quello di implementare e validare delle metodologie per la manutenzione predittiva di attrezzature agricole, in collaborazione con l'azienda Maschio Gaspardo S.p.A. L'idea generale è quella di sviluppare un "Digital Twin" dello Sprayer Campo 32 nella versione ALA400, cioè un gemello digitale del componente reale sul quale si effettuano analisi di resistenza a fatica a partire dalle sollecitazioni alle quali è soggetto il componente in tempo reale, nell'ottica della manutenzione predittiva. Sono state effettuate delle prove di fatica ad ampiezza variabile su dei provini assiali che sono rappresentativi della geometria e delle dimensioni delle saldature presenti nella struttura reale. Lo spettro di carico utilizzato in queste prove a fatica ad ampiezza variabile è stato ricavato a partire dai segnali estensimetrici, ottenuti tramite delle acquisizioni accelerometriche ed estensimetriche dello Sprayer in utilizzo nelle condizioni di esercizio, acquisizioni effettuate contestualmente a questo lavoro di tesi. Infine, è stato svolto un lavoro preliminare di analisi agli elementi finiti tramite l'estensione "Ansys Twin AI" del software Ansys, inoltre sono state effettuate delle prove di fatica ad ampiezza costante su dei componenti full scale, per verificarne il comportamento a fatica e per validare il Peak Stress Method.
Sviluppo e validazione di metodi per la manutenzione predittiva di attrezzature agricole
DELLA VOLPE, DAVIDE
2025/2026
Abstract
The objective of this work is to implement and validate methodologies for predictive maintenance of agricultural equipment, in collaboration with Maschio Gaspardo S.p.A. The overall aim is to develop a digital twin of the Campo 32 sprayer (ALA400 version), namely a virtual replica of the real component on which fatigue strength analyses can be performed based on the real-time loads acting on the structure, within a predictive maintenance framework. Variable amplitude fatigue tests were carried out on axial specimens representative of the geometry and dimensions of the welded joints in the actual structure. The load spectrum adopted for these tests was derived from strain signals obtained through combined accelerometric and strain gauge measurements of the sprayer operating under real service conditions; these measurements were acquired as part of this thesis work. Furthermore, a preliminary finite element analysis was conducted using the “Ansys Twin AI” extension within the Ansys software environment. In addition, constant amplitude fatigue tests were performed on full-scale components to assess their fatigue behavior and to validate the Peak Stress Method.| File | Dimensione | Formato | |
|---|---|---|---|
|
DellaVolpe_Davide.pdf
Accesso riservato
Dimensione
29.65 MB
Formato
Adobe PDF
|
29.65 MB | Adobe PDF |
The text of this website © Università degli studi di Padova. Full Text are published under a non-exclusive license. Metadata are under a CC0 License
https://hdl.handle.net/20.500.12608/107456