This dissertation aims to provide critical methodological advancements for sensor fusion and physics-informed machine learning in metal additive manufacturing (MAM) to assist practitioners in detecting quality control structural anomalies. In MAM, there is an urgent need to improve knowledge of the internal layer fusion process and geometric variation occurring during the directed energy deposition processes. A core challenge lies in the cyclic heating process, which results in various structural abnormalities and deficiencies, reducing the reproducibility of manufactured components. Structural abnormalities include microstructural heterogeneities, porosity, deformation and distortion, and residual stresses. Data-driven monitoring in MAM is needed to capture process variability, but challenges arise due to the inability to capture the thermal history distribution process and structural changes below the surface due to limitations in in-situ data collection capabilities, physical domain knowledge integration, and multi-data and multi-physical data fusion. The research gaps in developing system-based generalizable artificial intelligence (AI) and machine learning (ML) to detect abnormalities are threefold. (1) Limited fusion of various types of sensor data without handcrafted selection of features. (2) There is a lack of physical domain knowledge integration for various systems, geometries, and materials. (3) It is essential to develop sensor and system integration platforms to enable a holistic view to make quality control predictions in the additive manufacturing process. In this dissertation, three studies utilize various data types and ML methodologies for predicting in-process anomalies. First, a complementary sensor fusion methodology joins thermal and ultrasonic image data capturing layer fusion and structural knowledge for layer-wise porosity segmentation. Secondly, a physics-informed data-driven methodology for joining thermal infrared image data with Goldak heat flux improves thermal history simulation and deformation detection. Lastly, a physics-informed machine learning methodology constrained by thermal physical functions utilizes in-process multi-modal monitoring data from a digital twin environment to predict distortion in the weld bead. This dissertation provides current practitioners with data-driven and physics-based interpolation methods, multi-modal sensor fusion, and anomaly detection insights trained and validated with three case studies.

An autoclave is an advanced machine that sterilizes objects using high-pressure and high heat, with water steam as the medium. Autoclaves are frequently found in hospitals and other places where sterility is required. This project aims to meet the company's need to create a digital twin (DT) of an autoclave. The purpose is to test the control unit that controls the physical autoclave by exposing the DT to the same program as a real autoclave. A DT is a virtual model of a physical system, and in this project, it represented the autoclave and its sensors. The model was programmed in a graphical programming language NI LabView, with the same input and output signals as a real autoclave. The model was based on data-driven logic rather than physical based logic. From a real autoclave run where signals were recorded every second, conclusions could be drawn about how much each unique analog signal changed in combination with other signals through interpolation. The interpolation of the analog signals was used to capture the characteristics of these signals.  For validation, the DT was loaded into a Hardware In the Loop (HIL) system that simulates the autoclave with the DT but retains the control unit from the autoclave, which is the unit the company wants to test. The developed DT was tested against three goals describing how closely the values should align over an accumulated time. The results were compared each second between the real run and the DT run. The data-driven DT model met one of the three goals set, however, the DT model's characteristics resembled those of the real run, making the model useful as the control system does not interrupt the simulation for disallowed or deviant values. / En autoklav är en avancerad maskin som rengör objekt till en steril nivå med ett högt tryck och hög temperatur, där vattenånga används som medium. Autoklaven har ett vanligt förekommande på sjukhus och andra platser där sterilitet är ett krav. Det här projektet går ut på att möta företagets behov av att skapa en digital tvilling (DT) av en autoklav. Syftet är att testa kontrollenheten som styr den fysiska autoklaven genom att en DT ska utsättas för samma programkörning som en verklig autoklav. En DT är en virtuell modell av ett fysiskt system och i detta projekt var autoklaven och sensorerna i maskinen en DT. Modellen programmerades i ett grafiskt programmeringsspråk, NI LabVIEW med samma in- och utsignaler som en verklig autoklav. Modellen utgår ifrån en datadriven metod och inte en fysikalisk formulerad logik. Datan samlades in från en körning av en verklig autoklav, där signalerna sparades varje sekund. Slutsatser för hur mycket varje unik analog signal förändrades i kombination med övriga signaler kunde dras med hjälp av interpolering. Interpoleringen av de analoga signalera kunde användas för att fånga deras karakteristik.  För validering integrerades DT i ett Hardware In the Loop (HIL) system som hjälper till att simulera autoklaven. HIL-systemet har kontrollenheten kvar från autoklaven som är den enhet företaget vill utföra tester på. Den framtagna DT testades mot tre mål som beskriver hur nära värdena skall ligga under en ackumulerad tid. Resultatet jämfördes för varje sekund mellan den verkliga och DT körningen. Den datadrivna DT modellen uppfyllde 1 av 3 mål som ställdes, men DT modellens karakteristik efterliknade den från verkliga körningen vilket gör modellen användbar då kontrollsystemet inte avbryter simuleringen för ej tillåtna eller avvikande värden.

Yes / This paper presents the development of a new chatter model using measured cutting forces instead of a mathematical model with empirical nature that describes them. The utilisation of measured cutting forces enables the prediction of real-time chatter conditions and stable machining. The chatter model is validated using fast Fourier transform (FFT) analyses for detection of chatter. The key contribution of the developed chatter model is that it can be incorporated in digital twins for process monitoring and control in order to achieve greater material removal rates and improved surface quality in future industrial applications involving machining processes. / Research Development Fund Publication Prize Award winner, Sep 2020.

Historically, automotive body panels have been made of steel and joined by a process called resistance spot welding (RSW). However, in efforts to reduce vehicle weight to improve the energy efficiency of the vehicle, automotive manufactures have begun substituting aluminum in place of steel. While aluminum can be joined with RSW, a myriad of challenges arise from doing so. These challenges result in less consistent weld quality and accelerated electrode wear. Refill friction stir spot welding (RFSSW) is an emerging joining technology that could replace RSW as it is believed to be capable of creating superior joints in thin sheet aluminum. This research's goal is to compare RFSSW and RSW for joining aluminum automotive body panels. To accomplish this goal two studies were conducted and reported on in this thesis. The first focused on evaluating the manufacturing performance of RFSSW and RSW while the second focused on comparing the microstructure and mechanical performance of RFSSW and RSW joints. To improve the relevance of the study, a Toyota automated welding cell was used as a case study. The cell utilizes AA6061-T4 in 8 unique stack-ups to create door frames. This cell served as the base for the manufacturing performance comparison while also providing the three stack-ups used to compare microstructure and mechanical performance. The study compares manufacturing performance utilized a digital twin to compare how each technology would interact within the manufacturing cell. Parameters such as joining time and maintenance time were considered while overall output of the manufacturing cell was recorded. The results showed that RFSSW and RSW could produce the same number of parts in the given manufacturing cell. However, as modifications were made to the cell to increase output RFSSW proved to be capable of greater output due to its longer tool life. Concluding that RFSSW is a viable option from a manufacturing performance view. The second study conducted a comparison of the microstructure and mechanical properties of RFSSW and RSW. This study found that each technology created unique surface topographies and grain structures. Mechanical performance testing found that depending on the stack-up RFSSW joints were between 16% and 73% stronger than RSW joints in tensile loading conditions. RFSSW also showed improved fatigue life, in one test surviving 2600% more cycles. Concluding that RFSSW joints have superior mechanical performance over RSW joints.

Digital Twins (DTs) are software representations of physical systems (PSs) that interact with other entities on behalf of their real-world counterparts. To ensure accurate representation and effective interaction, DTs must remain synchronized with their PSs through timely updates—a process known as DT synchronization. This thesis addresses key challenges related to DT synchronization to optimize performance metrics, including the synchronization period and Age of Information (AoI). In the first part, we address the challenge of optimally placing DTs on execution servers (ESs) to minimize both the data request-response delay experienced by applications and the synchronization period between PSs and their DTs, while satisfying communication and computation constraints. We formulate the DT placement problem in two ways. First, we model it as an integer quadratic program (IQP) aiming to minimize the maximum application response delay subject to maximum data age target constraints at the DTs and the application server. Due to the NP-completeness of the problem, we develop practical polynomial-time approximation algorithms that offer trade-offs between application latency and data age targets. Second, we tackle the Minimum Synchronization Period (MSP) problem by modeling it as a multi-commodity quickest flow evacuation problem, considering synchronization data and processing tasks as network flows with flow dependent edge delays. This innovative approach allows us to use well-established techniques from flow network theory to efficiently find the quickest flow solution. An unsplittable flow rounding procedure ensures that each DT is assigned to a single ES. Simulation results demonstrate the effectiveness of our proposed algorithms in both methods, compared to optimal solutions serving as lower bounds. In the second part, we address DT migration in vehicular systems, where maintaining acceptable AoI is challenging due to high mobility and frequent handoffs between cellular domains. We formulate the optimal initiation time for migrating a vehicle's DT as a Markov decision process, aiming to minimize the time-averaged AoI at the DT. An online optimal migration initiation algorithm is proposed using dynamic programming and optimal stopping problem. We also develop a more computationally intensive adaptive version of this algorithm, which recalculates the decision policy at each time step for improved performance. Additionally, we introduce a best-in-expectation algorithm that offers a balance between computational efficiency and AoI performance. These algorithms are compared with heuristic approaches, such as immediate migration and migration at handoff, as well as an offline algorithm providing a theoretical lower bound on the average AoI. Performance evaluations show that our proposed algorithms significantly enhance the efficiency of DT migrations while minimizing the time-averaged AoI compared to other methods. / Dissertation / Candidate in Philosophy / Digital Twins (DTs) are virtual replicas of real-world Physical Systems (PSs), such as mobile devices, vehicles, or smart cities. These digital counterparts are hosted by network servers. They mirror the state and behavior of their physical versions in real time, allowing them to interact with other devices or applications on behalf of their PSs. For a DT to effectively mirror and reflect any changes in its PS, it must consistently remain synchronized through timely updates, which consume the network resources. As a result, the placement of DTs on network servers affects the quality of the DTs. The problem becomes challenging when placing the DTs of a large number of PSs, and is further complicated when the PSs are mobile. This thesis tackles some key challenges towards optimal DT placements. \begin{enumerate} \item Optimizing Synchronization Timing and Placement: We investigate how to optimally place DTs within the network infrastructure to minimize synchronization delay. To achieve this, we develop algorithms that efficiently assign DTs to servers, balancing the need for timely updates, quick application responses, and the amount of network resources. \item Enhancing DT Migration in Vehicular Systems: Vehicles are constantly on the move. Therefor, the PS-DT synchronization delay varies with the PS locations, and at some point, it is better to migrate the DT to a different server. We develop algorithms that decide when to initiate the migration to minimize costs associated with the migration.

The Advanced Manufacturing section at ÅF Pöyry AB is exploring the possibilities of virtual commissioning and digital twins. As part of this exploration, this thesis sets out to demonstrate a method of developing scalable, reusable and realistic digital mechatronic models – the heart of a digital twin – for the virtual environment in RobotStudio. Research has shown that one of the major obstacles to implementing virtual commissioning as a standard in industry today is the lack of scalable and reusable digital twins. This is also the experience of ÅF Pöyry AB. After reviewing existing and proposed methods for developing digital twins, this thesis explains the necessary steps for developing a SmartComponent in RobotStudio, using the programming language C#. The results show that the SmartComponent developed is scalable and thus reusable. It works with grippers with any number of fingers and allows gripping by applying pressure to the target object from both the outside and the inside. It is also realistic in the sense that the interaction between the grippers and the objects to be picked in the virtual environment behaves and looks like it does in reality. The implementation of the SmartComponent developed is much faster and less complex than the method used today at ÅF Pöyry AB. The downsides of the developed method are the added competence required of the automation engineer and the risk that the digital twin may not be future-proof. / Avdelningen Advanced Manufacturing på ÅF Pöyry AB utforskar möjligheter med ”Virtual Commissioning” och ”Digital Twins”. Som ett led i detta projekt har syftet med detta examensarbete varit att visa ett sätt att utveckla en skalbar, återanvändbar och realistisk digital mekatronisk modell för den virtuella miljön i RobotStudio. Tidigare forskning har visat, i linje med ÅF Pöyry AB:s satsning, att ett av de stora hinder för att Virtual Commissioning ska kunna implementeras som standard i industrin idag är bristen av skalbara och återanvändbara digitala tvillingar. Efter en genomgång av den befintliga och föreslagna metoden för att utveckla digitala tvillingar presenteras de nödvändiga stegen för att utveckla en SmartComponent för RobotStudio, med programmeringsspråket C#. Resultaten av utvecklingen och testen har visat att den utvecklade SmartComponent är skalbar och återanvändbar: den fungerar med gripare oavsett antal fingrar och den tillåter gripning både genom att applicera tryck på plockobjektet från utsidan och insidan. Den är också realistiskt på så vis att interaktionen mellan griparen och objekten som ska plockas i den virtuella miljön beter sig och ser ut som i verkligheten. Implementeringen av den utvecklade SmartComponent är också mycket effektivare och mindre komplex jämfört med den metod som används idag på ÅF Pöyry AB. Nackdelarna med den föreslagna metoden är de extra kompetenskraven för automationsingenjörer och risken att den digitala tvillingen inte är framtidssäker.

This thesis presents the design and prototype implementation of a digital twin based on a knowledge graph for Internet of Things (IoT) platforms. The digital twin is a virtual representation of a physical object or system that must continually integrate and update knowledge in rapidly changing environments. The proposed knowledge graph is designed to store and efficiently query a large number of IoT devices in a complex logical structure, use rule-based reasoning to infer new facts, and integrate unanticipated devices into the existing logical structure in order to adapt to changing environments. The digital twin is implemented using the open-source TypeDB knowledge graph and tested in a simplified automobile production line environment. The main focus of the work is on the integration of unanticipated devices, for which a similarity metric is implemented to identify similar existing devices and determine the appropriate integration into the knowledge graph. The proposed digital twin knowledge graph is a promising solution for managing and integrating knowledge in rapidly changing IoT environments, providing valuable insights and support for decision-making. / I den här avhandlingen presenteras utformningen och prototypimplementeringen av en digital tvilling baserad på en kunskapsgraf för IoT-plattformar (Internet of Things). Den digitala tvillingen är en virtuell representation av ett fysiskt objekt eller system som måste integrera och uppdatera kunskap i snabbt föränderliga miljöer. Den föreslagna kunskapsgrafen är utformad för att lagra och effektivt söka efter en stor uppsättning IoT-enheter i en komplex logisk struktur, använda regelbaserade resonemang för att härleda nya fakta och integrera oväntade enheter i den befintliga logiska strukturen för att anpassa sig till föränderliga miljöer. Den digitala tvillingen genomförs med hjälp av kunskapsgrafen TypeDB med öppen källkod och testas i en förenklad miljö för bilproduktion. Huvudfokus ligger på integrationen av oväntade enheter, för vilka ett likhetsmått implementeras för att identifiera liknande befintliga enheter och bestämma lämplig integration i kunskapsgrafen. Den föreslagna kunskapsgrafen för digitala tvillingar är en lovande lösning för att hantera och integrera kunskap i snabbt föränderliga IoT-miljöer, vilket ger värdefulla insikter och stöd för beslutsfattande. / Esta tesis presenta el diseño e implementación de un prototipo de gemelo digital basado en un grafo de conocimiento para plataformas de Internet de las Cosas (IoT). El gemelo digital es una representación virtual de un objeto o sistema físico que debe integrar y actualizar continuamente el conocimiento en entornos que cambian rápidamente. El grafo de conocimiento propuesto está diseñado para almacenar y consultar eficientemente un gran número de dispositivos IoT en una estructura lógica compleja, utilizar el razonamiento basado en reglas para inferir nuevos hechos e integrar dispositivos imprevistos en la estructura lógica existente para adaptarse a los cambios del entorno. El gemelo digital se implementa utilizando el grafo de conocimiento de código abierto TypeDB y se prueba en un entorno simplificado basado en una línea de producción de automóviles. El objetivo principal del trabajo es la integración de dispositivos no previstos, para lo cual se implementa una métrica de similitud para identificar dispositivos existentes similares y determinar la integración adecuada en el grafo de conocimiento. El grafo de conocimiento propuesto es una solución prometedora para la gestión del conocimiento y la integración en entornos IoT que cambian rápidamente, proporcionando información valiosa y apoyo a la toma de decisiones.

In order to succeed with the Sustainable Development Goals, the mining of minerals will need to not only continue, but increase in the coming years. The extraction of minerals such as gold and cobalt is often associated with human rights violations and the financing of criminal enterprises. This has led to increased pressure on firms to implement effective responsible sourcing practices. While often seen as a major challenge to many firms, if leveraged correctly, responsible sourcing can be turned into significant competitive advantages and increased supply chain visibility. Given the need for more research in this area, the aim of this study is to understand what responsible sourcing practices are used among manufacturing companies so that an approach for responsible sourcing of minerals can be developed. Using a qualitative research design and following a systematic combining approach, interviews with large industrial manufacturers were conducted. Using thematic analysis, this thesis identifies the main drivers, enablers, barriers, and strategies used by manufacturing companies. While the findings show that responsible sourcing is on the agenda for all companies, it is clear that some firms take responsible sourcing more seriously than others. Therefore, it is suggested that firms need to depart from a cost-centric perspective and instead see the benefits and long-term value generated from responsible sourcing. Furthermore, the findings suggest that some companies are becoming increasingly aware of the need to know their supply chain, and how responsible sourcing practices can be beneficial by providing supply chain visibility. However, the current practices are limited because they are an inherently static representation of a dynamic system. The implementation of a supply chain digital twin is therefore presented as a potential way for firms to improve their responsible sourcing practices while at the same time gaining substantial business value. / För att lyckas med målen för hållbar utveckling måste brytningen av mineraler inte bara fortsätta, utan även öka under de kommande åren. Utvinning av mineraler som guld och kobolt är ofta förknippad med kränkningar av mänskliga rättigheter och finansiering av kriminella organisationer. Detta har lett till ett ökat tryck på företag att införa effektiva metoder för ansvarsfullt inköp. Även om det ofta ses som en stor utmaning för många företag kan ansvarsfullt inköp, om det används på rätt sätt, omvandlas till betydande konkurrensfördelar och bidra till ökad synlighet i leveranskedjan. Med tanke på behovet av mer forskning på detta område är syftet med den här studien att förstå vilka metoder för ansvarsfullt inköp som används bland tillverkningsföretag, så att ett tillvägagångssätt för ansvarsfullt inköp av mineraler kan utvecklas. Genom en kvalitativ forskningsdesign och enligt en systematisk kombinationsmetod genomfördes intervjuer med stora tillverkande företag. Med hjälp av tematisk analys identifieras i denna avhandling de viktigaste drivkrafterna, möjliggörarna, hindren och strategierna som används av tillverkningsföretag. Resultaten visar att ansvarsfullt inköp står på agendan för alla företag, men det är tydligt att vissa företag tar ansvarsfullt inköp på större allvar än andra. Därför föreslås det att företag måste avvika från ett kostnadsfokuserat perspektiv och istället se fördelarna och det långsiktiga värdet som genereras av ansvarsfullt inköp. Dessutom tyder resultaten på att vissa företag blir allt mer medvetna om behovet av att känna till sin leverantörskedja och hur ansvarsfulla inköpsmetoder kan vara fördelaktiga genom att ge större synlighet i leverantörskedjan. De nuvarande metoderna är dock begränsade eftersom de är en statisk representation av ett dynamiskt system. Implementeringen av en digital tvilling i försörjningskedjan presenteras därför som ett potentiellt sätt för företag att förbättra sina metoder för ansvarsfulla inköp samtidigt som de får ett betydande affärsvärde.

The intention to reduce greenhouse gas emissions in the aviation industry is generating high pressure to innovate, which is also leading to an increasing need for improvements in the development process, especially as this is already subject to major changes due to changing products and value creation systems. In the future, the development process should be more digitalized and information better integrated in order to optimize the supply of information to developers. To this end, the following article presents a digital twin for product development, which is being developed as part of a research project on cross-lifecycle integration through digital twins in collaboration with Rolls-Royce Electrical and other research partners. The focus is on describing the conception of the digital twin, which produces a concept that is as user centric as possible based on user stories in order to specifically serve existing needs. The concept of the digital twin will be developed based on the current as-is process and the existing system landscape as well as the use cases derived from the user stories and presented here.

Detta projekt är givet som ett kandidatarbete i mjukvaruutveckling vid Linköpingsuniversitet, i kursen TDDD96 Kandidatprojekt i programvaruutveckling. Arbetet är utfört aven grupp tredjeårsstudenter på D- och U-programmen.Projektet ges av forskargruppen för integrerad systembiologi på IMT LiU, som utvecklarmatematiska modeller av biologiska system. Dessa modeller kan användas för att förståsystemen bättre, eller som beslutsstöd inom farmakologi och vård. Forskargruppens fokusför tillfället är att sammankoppla modeller för olika delar av människokroppen till en helhetsbildsom beskriver delsystemens samverkan.Denna helhetsbild för en särskild individs hälsotillstånd kallas för en digital tvilling, förvilken detta projekt ämnar framställa ett användargränssnitt.Användargränssnittet skall erbjuda grafer över hälsoparametrars förändring över tid,och användaren ska kunna ta fram olika sådana genom att interagera med gränssnittet.Utöver framtagning av själva gränssnittet, skall en brygga mellan applikationen ochkundens modeller tas fram. Detta har gjorts genom en version av programmet OpenCOR,vilket forskarna använder sig av, som exponerar ett API i Python. Detta API sammankopplatmed en server kallas i projektet för motor.