Modern manufacturing processes' increasing complexity and variability demand advanced systems capable of real-time monitoring, adaptability, and data-driven decision-making. This paper introduces a novel runtime condition model to enhance interoperability, data integration, and decision support within intelligent manufacturing environments. The model encapsulates key manufacturing elements, including asset management, relationships, key performance indicators (KPIs), capabilities, data structures, constraints, and configurations. A key innovation is the integration of a knowledge graph enriched with embedding techniques, enabling the inference of missing relationships, dynamic reasoning, and predictive analytics. The proposed model was validated through a case study conducted in collaboration with TQC Automation Ltd., using their MicroApplication Leak Test System (MALT). A dataset of over 9,000 unique test configurations demonstrated the model's capabilities in representing runtime conditions, managing operational parameters, and optimising test configurations. The enriched knowledge graph facilitated advanced analyses, providing actionable insights into test outcomes and enabling proactive decision-making. Empirical results showcase the model's ability to harmonise diverse data sources, infer missing connections, and improve runtime adaptability. This study highlights the potential of combining runtime modelling with knowledge graphs to address the challenges of modern manufacturing. Future research will explore the model's application to additional domains, integration with larger datasets, and the use of machine learning for enhanced predictive capabilities.

The paper discusses the design and implementation of a cognitive digital twin (CDT) to enhance the capabilities of autonomous industrial mobile manipulator (AIMM) in industrial settings. The integration of data in CDT facilitates enhanced positioning, even in the presence of obstacles, and optimized recharging schedules. The use of external sensors significantly improves the robot’s accuracy. The implementation of ML models facilitates intelligent planning of charging stops and minimizing downtime. This enhancement is achieved by creating a virtual representation of the physical system that incorporates cognitive capabilities, such as reasoning, learning, and planning. The study demonstrates the potential of CDTs to serve as advanced tools for decision-making, optimization, and predictive maintenance in industrial settings. The results also highlight the challenges in developing CDTs, particularly the need for high fidelity in replicating the physical system and the environment.

This study examines barriers to integrating design for assembly (DFA) principles into modular product architectures using the modular function deployment (MFD) method–a critical step for enabling cost-efficient mass customisation. Despite the known benefits of DFA, its adoption in early design stages remains limited. Drawing on qualitative insights from a focus group involving industry practitioners and advanced engineering students, as well as a prior systematic literature review, the study identifies key challenges. Results are classified into primary–closely aligned with research gaps–and secondary themes, emerging during practice-based discussions. These findings are synthesised into a conceptual framework structured around technological, economic, regulatory, and organisational (TERO) barriers. The framework provides guidance for integrating DFA earlier in modularisation, supporting more efficient, adaptable, and high-quality designs. It also outlines actionable areas for future research, including early-stage evaluation methods, AI-augmented design practices, and strategies for structuring knowledge repositories and promoting cross-functional collaboration.

How early should assembly considerations shape modular product architecture?

This thesis addresses the question by reframing Design for Assembly (DfA) from a downstream optimisation task into a design logic embedded at the start of modular product development. In many industrial settings, modularisation is shaped by customer values and market variety, while production and assembly remain implicit or postponed. When these factors are left until later, the chance to create automation-ready, lifecycle-resilient architectures and to enable reconfigurable manufacturing systems (RMS) creation is often lost.

The research develops an expanded Modular Function Deployment (MFD) method that integrates DfA into the earliest stages of decision-making. MFD is well established as a way to structure architectures around customer needs, but it has usually treated assembly as a concern for later. This has led to DfA being applied reactively, once architectural choices are already locked, which limits its ability to influence strategic or system-level design.

To change this, the thesis introduces a new dual framing: module-level DfA (mDfA) for guiding the early selection and grouping of technical solutions, and architecture-level DfA (aDfA) for evaluating spatial layout, sequencing logic, and assembly complexity once a candidate architecture exists.

This framing is made practical through a set of lightweight, prescriptive tools designed to fit within the standard MFD process.

1. DfA-based internal evaluation criteria for concept selection.
2. Assembly-oriented module drivers within the Module Indication Matrix.
3. A coded interface taxonomy to structure and retain assembly knowledge.
4. The Assembly Directions and Connections Draft (ADCD) for improving the planning of spatial logic and insertion directions.
5. The Module Set Assembly Strategy Matrix (MSASM) for the evaluation of module-set complexity and automation potential.

These supports allow teams to analyse assembly implications before geometries are fixed, making it easier to align modularity with production realities.

The research follows a design research methodology (DRM), combining literature synthesis, industrial case studies, expert workshops, and applications in graduate-level engineering education. The tools were tested in both greenfield and brownfield contexts, in sectors ranging from professional equipment to consumer products. Results show that they help bring assembly consequences into view earlier, improve interface considerations, and strengthen cross-functional alignment.

The contribution is twofold. Theoretically, it introduces the dual framing of module-level and architecture-level DfA, extending assembly reasoning from part-level simplification to architectural planning. Practically, it delivers a workflow that supports production-aware modularisation without requiring high digital maturity or large resource investments. By enabling adaptable, automation-ready architectures that align with lifecycle goals, the work contributes to long term manufacturing resilience and as a consequence connects to United Nations’ Sustainable Development Goals (SDGs) 7, 9, and 12.

Hur tidigt bör monteringsaspekter forma modulära produktarkitekturer?

Denna avhandling adresserar den frågan genom att omformulera Design for Assembly (DfA) från en nedströms optimeringsuppgift till en konstruktionslogik inbäddad redan från början av utvecklingen av modulära produkter. I flera industriella sammanhang formas modularisering av kundvärden och marknads variation, medan produktion och montering förblir underförstådda eller skjuts upp. När dessa faktorer lämnas till senare skeden går ofta möjligheten förlorad att skapa arkitekturer som är redo för automatisering, livscykelresistenta och möjliggör skapandet av omkonfigurerbara tillverkningssystem (Reconfigurable Manufacturing Systems, (RMS)).

Forskningen utvecklar en utökad version av Modular Function Deployment (MFD) som integrerar DfA i de tidigaste beslutsfaserna. MFD är väl etablerad som metod för att strukturera arkitekturer utifrån kundbehov, men har traditionellt sett behandlat montering som en senare fråga. Detta har lett till att DfA ofta tillämpas reaktivt, när arkitektoniska val redan är låsta, vilket begränsar dess möjlighet att påverka strategisk eller systemnivådesign.

För att ändra detta introducerar avhandlingen en ny dubbel inramning: modulnivå-DfA (mDfA) för att styra det tidiga urvalet och grupperingen av tekniska lösningar, samt arkitekturnivå-DfA (aDfA) för att utvärdera rumslig layout, sekvenslogik och monteringskomplexitet när en kandidatarkitektur finns.

Denna inramning operationaliseras genom en uppsättning lättviktiga, föreskrivande verktyg utformade för att passa in i den standardiserade MFD-processen.

1. DfA-baserade interna utvärderingskriterier för konceptval.
2. Monteringsorienterade moduldirektiv i Module Indication Matrix.
3. En kodad gränssnittstaxonomi för att strukturera och bevara monteringskunskap.
4. Assembly Directions and Connections Draft (ADCD) för planering av rumslig logik och insättningsriktningar.
5. Module Set Assembly Strategy Matrix (MSASM) för att kvantifiera modulset-komplexitet och automatiseringspotential.

Dessa stöd möjliggör analys av monteringskonsekvenser före fastställda geometrier och underlättar anpassning av modularitet till produktionsrealiteter.

Forskningen följer Design Research Methodology (DRM) och kombinerar litteratur syntes, industriella fallstudier, expertworkshops och tillämpningar i högre ingenjörsutbildning. Verktygen testades både i greenfield- och brownfield-sammanhang,i sektorer som spänner från professionell utrustning till konsumentprodukter. Resultaten visar att verktygen bidrar till att synliggöra monteringskonsekvenser tidigare, förbättra resonemang kring gränssnitt och stärka tvärfunktionell samordning.

Bidraget är tvådelat. Teoretiskt introducerar arbetet den dubbla inramningen av modulnivå- och arkitekturnivå-DfA, vilket utökar monteringsresonemanget från komponentnivåförenkling till arkitektonisk planering. Praktiskt levereras ett arbetsflöde som stödjer produktionsmedveten modularisering utan krav på hög digital mognad eller stora resursinvesteringar. Genom att möjliggöra anpassningsbara, automatiseringsredo arkitekturer som är i linje med livscykelmål bidrar arbetet till långsiktig tillverkningsresiliens och knyter an till Förenta Nationernas globala mål 7, 9 och 12.

Self-diagnosis functionalities, as integral components of advanced manufacturing services within cyber-physical systems (CPSs), are made possible through cloud computing technologies and machine learning techniques. These services play a crucial role in enhancing the autonomy of CPSs and introducing cost-efficient and scalable solutions. Despite the promising outlook, a gap exists in the literature regarding the lack of clear architectural frameworks and requirements for implementing self-diagnosis services in industrial settings. This paper addresses this gap by presenting a comprehensive requirement set and developing a high-level architecture tailored for self-diagnosis services. The proposed approach is validated through a detailed case study of a cloud-based self-diagnosis service, demonstrating alignment with the established architecture and requirements. The anticipated outcome of this research is to offer concrete implementation guidelines to support researchers, engineers, and practitioners in deploying CPS-based self-diagnosis services and improving production processes and system performance.

In the evolving landscape of modern manufacturing, a novel concept known as Feeding-as-a-Service (FaaS) is emerging, part of the larger Automationas-a-Service (AaaS) framework. FaaS aims to optimize feeding systems in cloud manufacturing environments to meet the demands of mass customization and allow for quick responses to production changes. Therefore, it fits into the Manufacturing as-a-Service (MaaS) system as well. As the manufacturing industry undergoes significant transformations through automation and service-oriented models, understanding how FaaS fits into the other frameworks is essential.This study presents a systematic literature review with two primary objectives: first, to contextualize FaaS within AaaS and MaaS, highlighting similarities, differences,and distinctive characteristics; second, to identify and clarify the essential Key Performance Indicators (KPIs) crucial for its strategic implementation.KPIs are pivotal metrics guiding organizations toward manufacturing excellence.In this context, common KPIs focus on efficiency and quality, such as resource utilization, and error rates. Other KPIs are also crucial, such as the ones related tocost reduction and customer satisfaction. For FaaS, the most relevant include also data security, data management, and network speed.This research provides a valuable KPI framework for FaaS developers, aidingin strategic decision making and deployment in industrial settings. It also contributes to a broader understanding of KPIs in manufacturing, which benefits both researchers and industrial practitioners.The results of the review, though, fail to address other crucial indicators for ‘asa-Service’ business, such as Churn Rate and Total Contract Value. Future research will address these limitations through methods ranging from questionnaires to practitioner interviews, with the aim of gathering the knowledge needed for real-world implementations.

This study, conducted with Northvolt, examines battery system recyclability and disassembly dynamics. It introduces indices for material and product recyclability, along with disassembly time assessment. The goal is to create a design tool to streamline the evaluation of battery disassembly, aiding in designing recyclable and serviceable components. These methodologies serve as a blueprint for enhancing battery systems' overall sustainability and circularity design, presenting a base for future product development in alignment with environmental and economic objectives.

This paper explores the qualification of a digital twin (DT) for a mobile manipulator (MOMA) in industrial applications. We discuss the development of different DT models based on various industrial needs and highlight the dependence of model accuracy on online sensor precision. Limitations of DTs for MOMA are examined, including challenges in respecting qualifiers due to the inability to incorporate unstructured aspects of the factory environment. Through a case study and some examples, we show the latent potential and limitations of DTs for MOMA in industrial contexts. The challenges of fidelity, real-time operation, and environment modeling are discussed. It is emphasized that creating a true digital twin of a mobile manipulator is hindered by the inability to include the complete surrounding environment. Recommendations for future research focus on addressing these limitations to enhance the effectiveness of DTs for MOMA in Industry 4.0 and smart manufacturing.

The success of a product in the market is largely defined by the quality of design decisions made during the early stages of development. The product design requires designers to balance multiple objectives such as functionality, cost, and user satisfaction, while addressing the challenges posed by increasing product variants and customization demands. To tackle these challenges, one approach is to structure a comprehensive model that incorporates design for assembly (DFA) guidelines during the formulation of product architecture in the conceptual phase of development. While numerous strategies have been proposed in the literature, information is often scattered, making it difficult for readers to gain a comprehensive understanding of the topic. This paper systematically reviews the role and impact of DFA in product development, consolidating and presenting the information coherently. The review provides an overview of the methods developed, along with their potential benefits and limitations. A common framework is identified that defines the structure of the models, helping designers integrate assembly consideration into their design processes, thus reducing assembly time, cost, and complexity. The framework describes the operational setting, including the domain and context in which models operate, and offers a classification of possible methods and desired outputs. Additionally, the review identifies the industry in which case studies have been most frequently presented, and the software used to facilitate the process. By connecting with such a framework, future models can be created following a structured approach, and existing models can be classified and upgraded accordingly.

Digital twins and artificial intelligence have shown promise for improving the robustness, responsiveness, and productivity of industrial systems. However, traditional digital twin approaches are often only employed to augment single, static systems to optimise a particular process. This article presents a paradigm for combining digital twins and modular artificial intelligence algorithms to dynamically reconfigure manufacturing systems, including the layout, process parameters, and operation times of numerous assets to allow system decision -making in response to changing customer or market needs. A knowledge graph has been used as the enabler for this system-level decision-making. A simulation environment has been constructed to replicate the manufacturing process, with the example here of an industrial robotic manufacturing cell. The simulation environment is connected to a data pipeline and an application programming interface to assist the integration of multiple artificial intelligence methods. These methods are used to improve system decision-making and optimise the configuration of a manufacturing system to maximise user-selectable key performance indicators. In contrast to previous research, this framework incorporates artificial intelligence for decision -making and production line optimisation to provide a framework that can be used for a wide variety of manufacturing applications. The framework has been applied and validated in a real use case, with the automatic reconfiguration resulting in a process time improvement of approximately 10%.