|Tipo di tesi||Tesi di laurea magistrale|
|Titolo||Apprendimento autonomo e indotto: analisi delle curve di apprendimento per la produzione di un componente in fibra di carbonio. Il caso Automobili Lamborghini.|
|Titolo in inglese||Autonomous and induced learning: analysis of learning curves for a CFRP component production. Automobili Lamborghini’s case study.|
|Struttura||Dipartimento di Scienze e Metodi dell'Ingegneria|
|Corso di studi||INGEGNERIA GESTIONALE (D.M.270/04)|
|Data inizio appello||2019-04-16|
|Disponibilità||Accessibile via web (tutti i file della tesi sono accessibili)|
Questo studio esamina i processi e le curve di apprendimento a partire dal monitoraggio della produzione di un componente in fibra di carbonio.
This study examines learning processes and learning curves based on the production monitoring of a Carbon Fibre Reinforced Polymer component. It focuses on a curve model that describes a real dataset collected from the production of a new component in Automobili Lamborghini S.p.A (Sant’Agata Bolognese, BO), leader in the production of luxury sports car with CFRP components. This research is the result of my five-month internship in the Pre-Series department of the Composites Centre in the aforesaid automotive company. The aim of the thesis is the study of a curve that describes the effect of autonomous and induced learning on the productivity improvement for a composite component. This kind of approach is new in the scientific literature. The close monitoring of the manufacturing process of a new automotive component has been conducted. This component is the carbon-look roof of Lamborghini’s Coupé Aventador SVJ Livery. The production process is manual and it consists of a lamination process and besides of other minor assembly operations. Four workers have been involved for five-weeks: the production time of every single operation has been collected for each of them. The aggregation of these data led to a significant analysis. The most important learning curve models available in the scientific literature have been tested on the real dataset, then new possible models have been created, and finally the learning curve with the best fitting, i.e. the one with the least Mean Squared Error, has been identified. This bivariate model, tested on real data, considers both the production volume and the training hours as independent variables. The results achieved are crucial for decision making processes and production planning of CFRP components in the automotive industry. It is suggested to test this model in different industries and for different processes, hence further investigation is recommended.