1,721,009 research outputs found
Tools and methods based on knowledge elicitation to support engineering design
No full textLe aziende di successo sono quelle che hanno saputo valorizzare le risorse umane e che hanno creato soprattutto le condizioni perché la conoscenza ed il know-how presente potesse evolvere e svilupparsi. Ciò non avviene per caso ma solo nel momento in cui si capisce che la conoscenza è la più grande ricchezza di un’impresa. Queste considerazioni sono sempre più necessarie se si considera l’evoluzione dell’economia occidentale e la difficoltà di accontentare il sempre più esigente consumatore finale che ricerca prodotti in poche quantità più ridotte ma sempre più personalizzabili.
La sfida per le aziende meccaniche italiane è quella di commercializzare prodotti di qualità ma continuando a sviluppare soluzioni innovative in tempi rapidi e contenendo i costi. Per vincere bisogna investire sulla parte più importante di un azienda: la progettazione, al fine di assicurare il futuro all’impresa stessa tramite strategie dedicate all’innovazione ed alla tecnologia.
Non esiste un manuale in grado di guidare l’ottimizzazione di una tipologia di progettazione, esistono però molte alternative sia commerciali che personalizzate per ingegnerizzare ed automatizzare il processo di progettazione industriale.
In particolare, all’interno di questa tesi è stato analizzato lo stato dell’arte sui sistemi Knowledge Based e sui loro aspetti implementativi. Una profonda analisi sul Knowledge Elicitation è stata richiesta per poter catturare e poi formalizzare non solo la conoscenza esplicita ma anche quella implicita e tacita. Con molta accuratezza è stato generalizzato il tipico flusso di progettazione al fine di trovare le criticità e poter fondare le basi sull’introduzione di procedimenti migliorativi. Sono stati proposti tre diversi livelli di metodologie di progettazione, uno per ogni grado di automazione. Il livello base di progettazione automatizzata è il livello della progettazione basata sull’esperienza in cui i progettisti hanno un buon livello di competenze ingegneristiche e conoscenze informatiche basilari tramite le quali poter implementare piccoli strumenti di lavoro come librerie di features oppure tabelle di variabili personalizzate per ridurre le operazioni ripetitive in fase di progettazione. Passando ad un livello di automazione maggiore, quello intermedio, le conoscenze ingegneristiche del progettista devono essere maggiori perché la progettazione è basata sulla prototipazione virtuale, quindi sull’uso ingegneristico di applicativi commerciali in grado di
automatizzare calcoli per la valutazione rapida delle prestazioni fisiche del prodotto. Al livello più automatizzato corrisponde una progettazione completamente basata sulla conoscenza aziendale implementata su applicativi software dedicati; quindi, in aggiunta alla competenza ingegneristica, sono richieste altre competenze riguardanti la programmazione ad oggetti ed in particolare conoscenze specifiche sulle interfacce di programmazione messe a disposizione da molti software ingegneristici.
Per poter formulare l’approccio metodologico di questa tesi sono state seguite da vicino molte aziende localizzate nella regione Marche, tuttavia ai fini della trattazione sono stati riportati solo due degli esempi più significativi. Mentre nel primo caso applicativo si è valutata e realizzata una metodologia di lavoro basata sulla prototipazione virtuale, nel secondo caso si è accuratamente formalizzata la conoscenza al fine di realizzare un applicativo software dedicato alle specifiche richieste dei progettisti.Successful companies are those that have been able to improve human resources and that have created particular conditions to evolve and develop knowledge and know-how. This doesn’t happen by chance but begins when companies realize that knowledge is the greatest richness. These considerations are increasingly necessary when considering the evolution of the occidental economy and the difficulty to satisfy the increate of consumer’s demanding for lesser quantities of products but much more customizable.
The challenge for Italian engineering enterprises is to sell quality products continuing to develop innovative solutions quickly, and keeping costs down. To win they have to invest in the most important part of an industrial companies: the design process, in order to ensure the future of the same company with dedicated strategies for innovation and technology.
There isn’t a guide about optimization of a type of design, but there are many alternatives both commercial and customized to engineering and automatize the process of industrial design.
In particular, within this research thesis has been analysed the state of the art on Knowledge Based Systems and their implementation issues. A deep analysis on the Knowledge Elicitation has been required to capture and then to formalize not only explicit knowledge but also the implicit and tacit one. With great care it has been generalized the typical design flow in order to find the critical points and to establish the bases on the introduction of improvement process. Three different levels of design methodologies have been proposed, one for each degree of automation. The basic level of automated design is based on the designer experience in which the same designers have a good level of engineering skills and basic computer skills through which they can develop little tools such as features libraries or customized tables variables to reduce repetitive tasks in the planning stage. Moving to a higher level of automation, the intermediate one, engineering skills of the designer must be more because the design is based on virtual prototyping, so on engineering use of commercial applications that automate calculations for the rapid physical assessment of the product. At the upper of automation, design is completely based on business knowledge implemented on dedicated software applications; then, in addition to engineering expertise, other skills are required on object-
oriented programming and in particular expertise on the programming interfaces provided by many engineering software.
Formulating the methodology of this thesis, many enterprises located in the region of Marche have been followed; however for the discussion only two of the most significant applicative examples have been reported. In the first test case a working methodology based on virtual prototyping has been evaluated and implemented, in the second case knowledge has been thoroughly formalized in order to realize a software application dedicated to the specific requirements of the designers
A Design Methodology to Support the Optimization of Steel Structures
No full textAbstractSteel constructions are widely used in several applications such as structures for buildings, stores, factories, and power plants. The scope of the research is to study a methodology to reduce the weight and the cost related to big frame steel structures during the early design phase, which is the phase where most of the project layout is defined. The main aim of this paper is the development of a platform-tool to support the automatic optimization of a steel structure using virtual prototyping tools and genetic algorithms. The focus is on the design of heavy steel structures for oil & gas power plants. This work describes in detail the design methodology and estimates the weight saving related to the re-design process of a test case structure. The design cases considered in the paper are those relevant to the operating
A CAD-based Tool to Support the configuration of parts storage shelving in assembly workstations
No full textA Knowledge Based Approach to Support Li-Ion Battery Cooling Design
No full textProceedings of EVCC (European Electric Vehicles Congress
A multi-objective sequential method for manufacturing cost and structural optimization of modular steel towers
Full text linkThis paper proposes a methodological approach for the multi-objective optimization of steel towers made from prefabricated cylindrical stacks that are typically used in the oil and gas sector. The goal is to support engineers in designing economical products while meeting structural requirements. The multi-objective optimization approach involves the minimization of the weights and costs related to the manufacturing and assembly phases. The method is based on three optimization levels. The first is used in the preliminary design phase when a company receives a request for proposal. Here, minimal information on the order is available, and the time available to formulate an offer is limited. Thus, parametric cost models and simplified 1-D geometries are used in the optimization loop performed by genetic algorithms. The second phase, the embodiment design phase, starts when an offer becomes an order based on the results of the first stage. Simplified shell geometries and advanced parametric cost models are used in the optimization loop, which present a restricted problem domain. In the last phase involving detailed design, a full 3-D computer-aided design model is generated, and specific finite-element method simulations are performed. The cost estimations, given the high levels of detail considered, are analytic and are performed using dedicated software
A Neural Network-Based Approach to Estimate Printing Time and Cost in L-PBF Projects
Full text linkAdditive manufacturing is one of the foundational pillars of Industry 4.0, which is rooted in the integration of intelligent digital technologies, manufacturing, and industrial processes. Machine learning techniques are resources used to support Design for Additive Manufacturing, particularly in design phases and process analysis. Neural Networks are suited to manage complex and non-linear datasets. The article proposes a methodology for the time and cost assessment of the Laser-Powder Bed Fusion 3D printing process using a Neural Network-based approach. The methodology analyzes the main geometrical features of STL files to train Neural Network Machine Learning models. The methodology has been tested on a preliminary dataset that includes a set of parametric CAD models and their corresponding Additive Manufacturing simulations. The trained models achieve an R2 value greater than 0.97. A web-service platform has been implemented to provide a valuable tool for users, transforming a research-grade model into a production-grade online endpoint
A machine learning method to predict printing time for the L-PBF process
Full text linkThe machine learning usage in the L-PBF process for metal powders helps to identify the optimal parameter combination. Machine learning can find non-linear correlations between the high number of variables of this production process. One of the obstacles to the widespread adoption of L-PBF in the industry, in addition to the high costs, is the long printing time required for a complex component. The possibility of an early evaluation of the 3D printing time could promote the overall diffusion of this production process in the industry. Correct time prediction can improve cost efficiency and production capacity, reducing energy consumption, environmental impacts, and lead time. This paper proposes a machine learning approach, such as Random Forest Regressor, to predict the printing time of a metal component starting from the STereo Lithography (.stl) CAD format for the L-PBF process. A case study is proposed to evaluate and demonstrate the approach, obtaining a high-level prediction accuracy
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