The term Industry 4.0, sometimes referred to as a buzzword, is today on everyone’s tongue and the benefits undeniably seem to be promising and have potential to revolutionize the manufacturing industry. But what does it really mean? From a high-level business perspective, the concept of Industry 4.0 most often demonstrates operational efficiency and promising business models but studies show that many companies either lack understanding for the concept and how it should be implemented or are dissatisfied with progress of already implemented solutions. Further, there is a perception that it is difficult to implement the concept without interference with the current production system.The purpose of this study is to interpret and outline the main characteristics and key components of the concept Industry 4.0 and further break down and conclude the potential benefits and enablers for a manufacturing company within the heavy automotive industry. In order to succeed, a case study has been performed at a manual final assembly production unit within the heavy automotive industry. Accordingly, the study intends to give a deeper understanding of the concept and specifically how manual assembly within an already existing manual production system will be affected. Thus outline the crucial enablers in order to successfully implement the concept of Industry 4.0 and be prepared to adapt to the future challenges of the industry. The case study, performed through observations and interviews, attacks the issue from two perspectives; current state and desired state. A theoretical framework is then used as a basis for analysis of the result in order to be able to further present the findings and conclusion of the study. Lastly, two proof of concept are performed to exemplify and support the findings. The study shows that succeeding with implementation of Industry 4.0 is not only about the related technology itself. Equally important parts to be considered and understood are the integration into the existing production system and design and purpose of the manual assembly process. Lastly the study shows that creating understanding and commitment in the organization by strategy, leadership, culture and competence is of greatest importance to succeed. / Begreppet Industri 4.0, ibland benämnt som modeord, är idag på allas tungor och fördelarna verkar onekligen lovande och tros ha potential att revolutionera tillverkningsindustrin. Men vad betyder det egentligen? Ur ett affärsperspektiv påvisar begreppet Industri 4.0 oftast ökad operativ effektivitet och lovande affärsmodeller men flera studier visar att många företag antingen saknar förståelse för konceptet och hur det ska implementeras eller är missnöjda med framstegen med redan implementerade lösningar. Vidare finns det en uppfattning att det är svårt att implementera konceptet utan störningar i det nuvarande produktionssystemet. Syftet med denna studie är att tolka och beskriva huvudegenskaperna och nyckelkomponenterna i konceptet Industri 4.0 och ytterligare bryta ner och konkludera de potentiella fördelarna och möjliggörarna för ett tillverkande företag inom den tunga bilindustrin. För att lyckas har en fallstudie utförts vid en manuell slutmonteringsenhet inom den tunga lastbilsindustrin. Studien avser på så sätt att ge en djupare förståelse för konceptet och specifikt hur manuell montering inom ett redan existerande manuellt produktionssystem kommer att påverkas. Alltså att kartlägga viktiga möjliggörare för att framgångsrikt kunna implementera konceptet Industri 4.0 och på så sätt vara beredd att ta sig an industrins framtida utmaningar. Fallstudien, utförd genom observationer och intervjuer, angriper frågan från två perspektiv; nuläge och önskat läge. Ett teoretiskt ramverk används sedan som underlag för analys av resultatet för att vidare kunna presentera rön och slutsats från studien. Slutligen utförs två experiment för att exemplifiera och stödja resultatet. Studien visar att en framgångsrik implementering av Industri 4.0 troligtvis inte bara handlar om den relaterade tekniken i sig. Lika viktiga delar som ska beaktas och förstås är integrationen i det befintliga produktionssystemet och utformningen och syftet med den manuella monteringsprocessen. Slutligen visar studien att det är av största vikt att skapa förståelse och engagemang i organisationen genom strategi, ledarskap, kultur och kompetens.

The 4th Advanced Manufacturing Student Conference (AMSC24) presents a collection of review papers written exclusively by students to promote the learning and application of research methods in advanced manufacturing. Organised by Chemnitz University of Technology and the Fraunhofer Institute for Machine Tools and Forming Technology, the conference covers topics such as supply chain resilience 4.0, IoT and cybersecurity in smart manufacturing, sustainable additive manufacturing and immersive technologies. While the 72 individual papers cover important aspects, they are not exhaustive, as the focus was on giving students experience in writing a review article in a limited setting. Each submission underwent a two-stage review process to ensure academic rigour. However, given the rapid advances in the field of artificial intelligence, including tools such as ChatGPT, it cannot be guaranteed that all published reviews will be free of AI-generated content. In conclusion, AMSC24 not only highlights current trends and challenges in advanced manufacturing, but also plays a crucial role in developing the research skills of emerging early-stage researchers.:# Supply Chain Resilience 4.0 • Integration of Artificial Intelligence and Machine Learning in Supply Chain Management • Digital Transformation in Supply Chains: The Role of Industry 4.0 in Enhancing Resilience • Importance of Sustainable and Resilient Supply Chains • Advancing Sustainability in Supply Chain Management through Block Chain Integration • IoT-Powered Supply Chain and Logistics Improvement: Governing Industry 4.0's Challenges and Opportunities # IoT & Cybersecurity: Smart Manufacturing • IoT Integration in Industry 4.0: Enhancing Manufacturing Efficiency through Predictive Maintenance • AI-Assisted Customized Manufacturing in Smart Factory • Integrating Artificial Intelligence with Lean Management for Industry 4.0: - A Comprehensive Review • Cybersecurity Challenges and Solutions in SmartManufacturing • Predictive Maintenance Strategies for Small and Medium-Sized Enterprises (SMEs) in the Context of Industry 4.0 Technologies • Smart Manufacturing - Challenges, Opportunities and Future Prospects • Securing the Digital Factory: A Review of Cybersecurity Challenges, Threats, and Countermeasures in Smart Manufacturing • Evolution of Vehicle-to-Everything with the onset of 6G: Technologies, Challenges, and Opportunities • Enhancing Manufacturing 4.0 Efficiency through IoT and Robotics: A Systematic Literature Review # Sustainable Additive Manufacturing • Optimizing Hot Wire Laser Metal Deposition of Inconel 625: A Comprehensive Review • Advancing Sustainability in Manufacturing: The Role of Additive Manufacturing Technologies • A Review of Generative Design and Topology Optimization for Sustainability in Additive Manufacturing • Enhancing Environmental Efficiency: Investigating Sustainable Product Design in Additive Manufacturing • Improving Tensile Properties of Poly-Lactic Acid using Natural Fibre Reinforcement in Fused Deposition Modelling • Sustainable 3D Printing: Carbon Fibre Composites for Advanced Lightweight Structures • Impacts of Sustainability in Additive Manufacturing # Circular Economy & Collaboration • Smart Manufacturing and Circular Economy: Enabling Sustainable Manufacturing Systems for Electric Vehicle Batteries • Overview of Low-Cost Prosthetic Feet using Non-Conventional Materials and Modern Manufacturing Processes for Lower Limb Amputees • Impact of Circular Economy on Supply Chain Sustainability • Circular Economy: Transforming Industries for Environmental Sustainability • Implementing Circular Economy in Textile and Apparel Industry • Human-Robot Teaming and its Barriers in Manufacturing • Enhancing Safe Human-Robot Collaboration through Robot Skin: Challenges and Potential • Advancement and Implication of Human–Robot Collaboration in Advanced Manufacturing: A Comprehensive Review # Robotics & Digital Twins in Manufacturing • A Review: Seam Tracking Operation for Robotic Gas Metal Arc Welding • Advancements in Predictive Maintenance: Comprehensive Review of AI Applications in Industry • Use of Machine Learning for Defect Detection on Additively Manufactured Materials • Sink Marks in Plastic Injection Molding process: Detection and Reduction with Machine Learning and Artificial Intelligence • Autonomous Robots for Intralogistics in Warehouse Management • Strategic Adoption of Industry 4.0: A Guide for SMEs in Developing Nations • Predictive Maintenance in Supply Chains: Leveraging Digital Twins in Industry 4.0 • Advances in Multiscale Numerical Modeling and Experimental Analysis of Additive Manufacturing Processes with Carbon Fiber Reinforced Polymers • Digital Twins and Artificial Intelligence for Predictive Maintenance in Aerospace # Sustainable 3D Printing • 3D Printed Continuous Fiber Reinforced Composite • Sustainability in Additive Manufacturing: From Design to Lifecycle Management • A Review on Optimizing AlSi10Mg Alloy Properties and Structures via Selective Laser Melting for Sustainable Production • Robotic Additive Manufacturing: Enhancing and Innovating Carbon Fiber Production • Implementation of Additive Manufacturing Techniques in the Fabrication of Jet Engines Parts • Review on Fused Deposition Modeling of PEEK for Medical Applications and its Process Optimization • Embracing Sustainability through Industry 4.0: Additive Manufacturing's Role in Reducing Carbon Footprints • Additive Manufacturing using Polyetheretherketone for Biomedical Applications • Low Cost Fabrication Technique of Stainless Steel 316L using Fused Filament Fabrication # Circularity & Energy Efficiency • Direct Metal Laser Sintering vs. Conventional Manufacturing for Sustainable Production: Comparative Analysis • Review of Circular Economy Practices for Lithium-Ion Batteries in Electric Vehicles Life Cycle • Economic Approach to Power Generation by Optimization of Microgrid CHP Systems • Catalytic Pyrolysis of Plastic Waste: Recent Advances, Emerging Technologies, and Prospects for Circular Economy • Fabrication of Compact Heat Exchangers using Additive Manufacturing • Optimizing Energy Consumption through Human-Machine Collaboration in Advanced Manufacturing • Energy Efficiency of Laser Manufacturing Technologies # Immersive Tech in Manufacturing • Virtual Reality Applications: Transforming Industrial Productivity • Augmented Reality Applications in Product Assembly Process • Implementation of Immersive Technologies in the Design and Development Phase of Manufacturing System: An Effective Approach towards Industry 4.0 • Advancements in Engineering Textile Materials for Hernia Repair • Immersive Innovations: Their Role in Modern Manufacturing • Role of Augmented Reality, Digital Twin, Extended Reality & Data Transmission in Manufacturing Advancements • Transforming Quality Assurance in Manufacturing through Augmented Reality Solutions # Sustainable Manufacturing Solutions • A Review on Fused Deposition Modeling of Polypropylene - Sustainability, Process Optimization • Optimization of Process Parameters of Selective Laser Melting of Invar 36 Alloy: A Review • Environmental Impact of Additive Manufacturing Processes – A Review • Optimization of Gear through Additive Manufacturing • Implementation of Circular Economy: Opportunities and Challenges # Digital Twins & Robotics Synergy • A Review of Digital Twin Applications in Industrial Safety • Digital Twins in Automotive Manufacturing Production Lines – A Review • Digital Twin Technologies in Industry 4.0: Review of Use Cases and Security Measures • Degradation Predictions of Proton Exchange Membrane Fuel Cells in Automotive Industries • Opportunities, Challenges and Applications of Swarm Robotic Systems in a Manufacturing Environment: A Protocol for a Systematic Review • Optimizing Process Parameters and Defect Prediction for Metallic Additive Manufacturing Processes using Machine Learning Methods: A Review

Self-adaptive systems have limited time to adjust their configurations whenever their adaptation goals, i.e., quality requirements, are violated due to some runtime uncertainties. Within the available time, they need to analyze their adaptation space, i.e., a set of configurations, to find the best adaptation option, i.e., configuration, that can achieve their adaptation goals. Existing formal analysis approaches find the best adaptation option by analyzing the entire adaptation space. However, exhaustive analysis requires time and resources and is therefore only efficient when the adaptation space is small. The size of the adaptation space is often in hundreds or thousands, which makes formal analysis approaches inefficient in large-scale self-adaptive systems. In this thesis, we tackle this problem by presenting an online learning approach that enables formal analysis approaches to analyze large adaptation spaces efficiently. The approach integrates with the standard feedback loop and reduces the adaptation space to a subset of adaptation options that are relevant to the current runtime uncertainties. The subset is then analyzed by the formal analysis approaches, which allows them to complete the analysis faster and efficiently within the available time. We evaluate our approach on two different instances of an Internet of Things application. The evaluation shows that our approach dramatically reduces the adaptation space and analysis time without compromising the adaptation goals.