Development of a Graph-based Metamodelling Framework for Additive Manufacturing and Its Simulation Using Machine Learning

Mallinnuksella ja simuloinnilla voi olla merkittävä rooli monimutkaisen valmistusjärjestelmän ja sen toiminnan ymmärtämisen lisäämisessä. Teollisuus 4.0:n myötä on tarpeen integroida kehittyneitä valmistusprosesseja, esimerkiksi käyttämällä esineiden internetin (IIoT) -teknologioita, jotta voidaan luoda valmistusjärjestelmiä, jotka eivät ole vain yhteydessä toisiinsa, mutta kommunikoivat keskenään paremmin sekä pystyvät analysoimaan ja käyttämään informaatiota fyysisen maailmaan sijoittuviin älykkäisiin ohjaus menetelmiin. Tällainen edistys edellyttää perinteisten massatuotannon valmistus paradigmojen siirtymistä monimutkaisempiin ja monipuolisempiin tuotantoteknologia-alueisiin, jotka koskevat massaräätälöintiä ja tehostettua tuotteiden eriyttämistä, modifiointia ja innovaatioita. Ainetta lisäävät valmistus menetelmät ovat nousseet esiin luotettavana vaihtoehtona tavanomaisille tuotantomenetelmille, mukaan lukien ainetta vähentävät menetelmät, johtuen usein kyseisten menetelmien ennennäkemättömistä muotoilun vapauksista ja monipuolisuudesta pitkälle räätälöityjen tuotteiden valmistuksessa. Lisäainevalmistusteknologioiden onnistunut ottaminen käyttöön valtavirran tuotannossa edellyttää kuitenkin lisäaine valmistusjärjestelmän mallintamista ja simulointia kokonaisuudessaan sen muotoilun, toiminnan ja käytön simuloimiseksi ja optimoimiseksi, samalla kun saavutetaan halutut tuotantotulokset. Tällä hetkellä mallit, jotka on kehitetty karakterisoimaan eri toimintoja additiivisessa valmistusprosessissa, saavat eri muotoja (esim. analyyttisiä, empiirisiä, fysiikkapohjaisia ja koneoppimismalleja) vaihtelevalla tarkkuudella. Täten, kokonaisvaltainen järjestelmän mallinnus vaatii joukon mallinnuksia yksittäisistä lisäainevalmistus teknologioiden karakterisoinneista. Erilaisten prosessitoimintojen, geometrioiden ja materiaalien yhdistäminen tekee kuitenkin haasteelliseksi tarvittavien osajärjestelmätasojen heterogeenisien mallien kokoamisen kokonaisvaltaiseksi järjestelmämalliksi. Tämän puutteen korjaamiseksi, tämän tutkimuksen tavoitteena on kehittää graafipohjainen metamallinnuskehys tuotesuunnittelun ja valmistusstrategioiden integroimiseksi digitaalisesti kokonaisvaltaisten ja simuloitavien monialaisten metamallien kehittämiseksi. Kehitetty viitekehys tukee 1) eri tiedon muotojen integrointia monialaisten metamallien kehittämiseen, 2) determinististen ja todennäköisyyspohjaisten koneoppimislähestymistapojen soveltamista kehitettyjen metamallien simuloinnin mahdollistamiseksi ja 3) ennustavaa analyysiä ja optimointia simuloimalla kehitettyjä metamalleja, jotta voidaan mahdollistaa suunnittelun ja valmistuksen päätöksenteko. Tämä tutkimus mahdollistaa lisäainevalmistusprosessin syöttö arvojen ja tulosten systeemisen karakterisoinnin olemassa olevan tiedon ja kokeellisen tiedon avulla. Lisäainevalmistusprosessin mallintamista ohjasivat tuotesuunnittelu- ja prosessitiedot, ja sitä tuettiin simulaatiolla päätöksentekoa varten. Tutkimuksen taustalla olevat mallit kattavat kaksi kaupallisesti saatavilla olevaa lisäainevalmistusprosessia. Tämä tutkimus osoittaa, että datalähtöisten ja muiden lähestymistapojen käyttö, joissa hyödynnetään sekä kerättyä dataa että olemassa olevaa tietoa, voi mahdollistaa tarkkojen ja selitettävissä olevien metamallien kehittämisen lisäainevalmistuksen tarkkaan seurantaan ja valvontaan halutun tuotteen laadun varmistamiseksi.Modelling and simulation can play a significant role in enhancing the understanding of a complex manufacturing system and its operation. With the advent of Industry 4.0, there is a need to integrate advanced manufacturing processes, e.g., using industrial internet of things (IIoT) technologies, to create manufacturing systems that are not only interconnected, but communicate better and can analyse and use information to drive intelligent action into the physical world. Such progress requires traditional manufacturing paradigms of mass production to move into more complex and diverse production technology domains of mass customization and enhanced product differentiation, modification, and innovation. Additive manufacturing has emerged as a reliable alternative to conventional manufacturing, including subtractive processes, often attributed to its claim for unprecedented design freedom and versatility for the production of highly customized products. However, for successful adoption of additive technologies into mainstream production, modelling and simulation of the manufacturing system in the entirety of its complexity is required to simulate and optimize its design, operation, and use, while achieving desirable production outcomes. At present, models developed to characterize the various activities in an additive manufacturing process take different forms (e.g., analytical, empirical, physics-based, and machine learning models) at varying levels of granularity. Thus, holistic system modelling requires an array of heterogenous models for characterizing a single additive manufacturing technology. However, the inclusion of different process activities, geometries, and materials makes it a challenge to compose the necessary subsystem-level heterogenous models into a holistic system model. To address this gap, this research aims to develop a graph-based metamodelling framework for digitally integrating the product design and manufacturing strategies to develop holistic and simulatable multi-domain metamodels. The developed framework supports 1) integration of different forms of knowledge to develop multi-domain metamodels, 2) application of deterministic and probabilistic machine learning approaches to enable simulation of developed metamodels, and 3) predictive analysis and optimization through simulation of the developed metamodels to enable design and manufacturing decision making. This research enables systemic characterization of additive manufacturing process inputs and outputs using pre-existing knowledge and experimental data. Additive manufacturing process modelling was driven by product design and process data/information, and supported by simulation for decision making. Underpinning models within the research encompass two commercially available additive manufacturing processes. This research demonstrates that the use of data-driven and other approaches that utilize both collected data and pre-existing knowledge can enable the development of accurate and explainable metamodels for close monitoring and control of additive manufacturing to ensure desirable product quality

Optimisation-driven design to explore and exploit the process–structure–property–performance linkages in digital manufacturing

An intelligent manufacturing paradigm requires material systems, manufacturing systems, and design engineering to be better connected. Surrogate models are used to couple product-design choices with manufacturing process variables and material systems, hence, to connect and capture knowledge and embed intelligence in the system. Later, optimisation-driven design provides the ability to enhance the human cognitive abilities in decision-making in complex systems. This research proposes a multidisciplinary design optimisation problem to explore and exploit the interactions between different engineering disciplines using a socket prosthetic device as a case study. The originality of this research is in the conceptualisation of a computer-aided expert system capable of exploring process–structure–property–performance linkages in digital manufacturing. Thus, trade-off exploration and optimisation are enabled of competing objectives, including prosthetic socket mass, manufacturing time, and performance-tailored socket stiffness for patient comfort. The material system is modelled by experimental characterisation—the manufacturing time by computer simulations, and the product-design subsystem is simulated using a finite element analysis (FEA) surrogate model. We used polynomial surface response-based surrogate models and a Bayesian Network for design space exploration at the embodiment design stage. Next, at detail design, a gradient descent algorithm-based optimisation exploits the results using desirability functions to isolate Pareto non-dominated solutions. This work demonstrates how advanced engineering design synthesis methods can enhance designers’ cognitive ability to explore and exploit multiple disciplines concurrently and improve overall system performance, thus paving the way for the next generation of computer systems with highly intertwined material, digital design and manufacturing workflows. Graphical abstract: [Figure not available: see fulltext.].publishedVersionPeer reviewe

Investigation of thermal influence on weld microstructure and mechanical properties in wire and arc additive manufacturing of steels

Alloy steels are commonly used in many industrial and consumer products to take advantage of their strength, ductility, and toughness properties. In addition, their machinability and weldability performance make alloy steels suitable for a range of manufacturing operations. The advent of additive manufacturing technologies, such as wire and arc additive manufacturing (WAAM), has enabled welding of alloy steels into complex and customized near net-shape products. However, the functional reliability of as-built WAAM products is often uncertain due to a lack of understanding of the effects of process parameters on the material microstructure and mechanical properties that develop during welding, primarily driven by thermal phenomena. This study investigated the influence of thermal phenomena in WAAM on the microstructure and mechanical properties of two alloy steels (G4Si1, a mild steel, and AM70, a high-strength, low-alloy steel). The interrelationships between process parameters, heating and cooling cycles of the welded part, and the resultant microstructure and mechanical properties were characterized. The welded part experienced multiple reheating cycles, a consequence of the layer-by-layer manufacturing approach. Thus, high temperature gradients at the start of the weld formed fine grain structure, while coarser grains were formed as the height of the part increases and the temperature gradient decreased. Microstructural analysis identified the presence of acicular ferrite and equiaxed ferrite structures in G4Si1 welds, as well as a small volume fraction of pearlite along the ferrite grain boundaries. Analysis of AM70 welds found acicular ferrite, martensite, and bainite structures. Mechanical testing for both materials found that the hardness of the material decreased with the increase in the height of the welded part as a result of the decrease in the temperature gradient and cooling rate. In addition, higher hardness and yield strength, and lower elongation at failure was observed for parts printed using process parameters with lower energy input. The findings from this work can support automated process parameter tuning to control thermal phenomena during welding and, in turn, control the microstructure and mechanical properties of printed parts.publishedVersionPeer reviewe

Improving worker health and safety in wire arc additive manufacturing : A graph-based approach

Research on human health and safety impacts of wire arc additive manufacturing is often overshadowed by the need for weld quality and mechanical strength improvements. To address this gap, a review of research literature is conducted focusing on the influence of welding process parameters, welding fumes, and fume exposure on worker health. The review uses a causal graph to classify research literature into two domains: manufacturing technology and public health. The graph serves as a precursor to development of a Bayesian network model, whose expected benefits, steps for implementation, and likely challenges that would be encountered during implementation are discussed.publishedVersionPeer reviewe

Knowledge based optimization of artificial neural network topology for additive manufacturing process modeling: a case study for fused deposition modeling

International audienc

Knowledge based optimization of artificial neural network topology for additive manufacturing process modeling: a case study for fused deposition modeling

International audienc