In order to quickly adapt the production on a production line to the demand, there is a needfor flexibility. A tool designed for this purpose, p-sop, has been developed at UniversityWest. This thesis deals with implementation of p-sop in a demonstrator, and developmentof a framework for priority policies as well as a graphical user interface to p-sop. The prioritypolicies evaluated in the demonstrator did note give an increased efficiency, and thegraphical user interface is shown to be well suited for the demonstrator and p-sop in general.

The increased demands for customization in manufacturing industries require new automation methods. This thesis presents the development of such a method for a cutting procedure of customized mesh trays at WIBE Group. The work aimed to automatically generate robot targets from data extracted from Autodesk AutoCAD and demonstrate the concept in ABB RobotStudio. A literature review was conducted to find an appropriate method for data extraction and conversion into robot targets. This resulted in an approach where data for the cut positions was collected by scripts created with AutoCAD’s inbuilt programming language AutoLISP. The data was then exported to a text file that could be read by the robot in the RobotStudio simulation model. By offsetting a pre-set robot target with the imported data, new robot targets for each cut were created. The developed method was tested for different types of mesh trays and cut positions. The conclusion is that the method is fully working and that the concept is user-friendly, but some improvements are needed before the concept is ready to be put into operation. For example, further measures are needed to ensure that the correct data is collected from AutoCAD and that the concept gets easier to troubleshoot. Furthermore, development work is also needed for aspects outside the purpose of this thesis, such as the robot cell layout and design of the robot tool.

Today the global market is changing to increased competition, short product life cycles and mass customisation. Therefore, the pressure on manufacturing companies to become more flexible in their production has increased. As a response to this increased pressure, many companies have implemented automation solutions to achieve the required flexibility and stay competitive. Small and medium-sized enterprises (SMEs) are particularly under pressure, because they are usually subcontractors to large companies which trigger and promote high-mix and low-volume production. The literature shows that there are couple of reasons, for example poor management skills, impacting SMEs' behaviour and the way they run businesses. Moreover, the academia uncovers the misassumption that the conceptual frameworks, organisational theories, and processes developed through researching large companies are directly applicable and relevant to SMEs. The truth is SMEs run the business and adopt models and theories differently from larger counterparts. Therefore, the purpose of this study is to increase knowledge about managing successful implementation of flexible automation projects in companies with a high mix, low volume production. The methodological approach of this work was theoretical and empirical driven. The theory part includes a literature review, and the empirical section includes a single case study and experts involvement. Thereby five key words for flexible automation could be identified which built the basis for the research. The outcome of the study is 21 general recommendations regarding implementing flexible automations at SMEs with high mix, low volume production. These recommendations have been developed to help SMEs in their journey with automation as they can be used as a road map to cover the implementation process, before, during and after.

Purpose - The purpose of this study is to investigate how the challenges of Internet of Things that manufacturers can influence can be overcome. Theoretical foundation - This study conducted an extensive literature review to identify and understand the challenges to Internet of Things and actions to overcome them. 11 technical challenges, 13 organisational and 6 resource availability challenges were identified. 4 actions were identified. Method - To fulfil the purpose, an embedded multiple case study at a global Aerospace manufacturer was conducted. 7 unstructured interviews, 12 semi-structured interviews and a survey were conducted. Respondents were picked due to their experience in Internet of Things projects and relevant technologies. The survey was conducted to evaluate the challenges’ relevance to high-technology manufacturers, on a 7-point Likert scale. The semi-structured interviews aimed to find actions to overcoming the challenges relevant to high-technology manufacturers. Findings - The evaluation of the challenges relevance to high-technology manufacturers resulted in 10 common, 9 occasional and 2 uncommon challenges. 12 actions to overcoming the challenges and their tasks were identified. Theoretical contribution - The study provides a comprehensive list of potential challenges to Internet of Things. It evaluated the challenges’ impact on high-technology manufacturers, thus challenging and validating Internet of Things challenges presented in literature. It provides a set of actions and a framework which aids in overcoming challenges that impact high-technology manufacturers’ Internet of Things initiatives, thus contributing to digital change management. Finally, it aids in the progress towards concretising Industry 4.0 and its trends toward connectivity, intelligence, and flexible automation. Practical contribution - The study provides an increased understanding of potential challenges of Internet of Things, and recommendations to how high-technology manufacturer can overcome the challenges. A framework is provided which gives an overview of which actions, and subsequent tasks, to take to overcome a specific challenge.

The industry is moving towards a higher degree of automation and connectivity, where previously manual operations are being adapted for interconnected industrial robots. This thesis focuses specifically on the automation of tightening applications with pre-tightened bolts and collaborative robots. The use of 3D computer vision is investigated for direct localization of bolts, to allow for flexible assembly solutions. A localization algorithm based on 3D data is developed with the intention to create a lightweight software to be run on edge devices. A restrictive use of deep learning classification is therefore included, to enable product flexibility while minimizing the computational load. The cloud-to-edge and cluster-to-edge trade-offs for the chosen application are investigated to identify smart offloading possibilities to cloud or cluster resources. To reduce operational delay, image partitioning to sub-image processing is also evaluated, to more quickly start the operation with a first coordinate and to enable processing in parallel with robot movement. Four different hardware architectures are tested, consisting of two different Single Board Computers (SBC), a cluster of SBCs and a high-end computer as an emulated local cloud solution. All systems but the cluster is seen to perform without operational delay for the application. The optimal hardware architecture is therefore found to be a consumer grade SBC, being optimized on energy efficiency, cost and size. If only the variance in communication time can be minimized, the cluster shows potential to reduce the total calculation time without causing an operational delay. Smart offloading to deep learning optimized cloud resources or a cluster of interconnected robot stations is found to enable increasing complexity and robustness of the algorithm. The SBC is also found to be able to switch between an edge and a cluster setup, to either optimize on the time to start the operation or the total calculation time. This offers a high flexibility in industrial settings, where product changes can be handled without the need for a change in visual processing hardware, further enabling its integration in factory devices. / Industrin rör sig mot en högre grad av automatisering och uppkoppling, där tidigare manuella operationer anpassas för sammankopplade industriella robotar. Denna masteruppsats fokuserar specifikt på automatiseringen av åtdragningsapplikationer med förmonterade bultar och kollaborativa robotar. Användningen av 3D-datorseende undersöks för direkt lokalisering av bultar, för att möjliggöra flexibla monteringslösningar. En lokaliseringsalgoritm baserad på 3Ddata utvecklas med intentionen att skapa en lätt mjukvara för att köras på Edge-enheter. En restriktiv användning av djupinlärningsklassificering är därmed inkluderad, för att möjliggöra produktflexibilitet tillsammans med en minimering av den behövda beräkningskraften. Avvägningarna mellan edge- och moln- eller klusterberäkning för den valda applikationen undersöks för att identifiera smarta avlastningsmöjligheter till moln- eller klusterresurser. För att minska operationell fördröjning utvärderas även bildpartitionering, för att snabbare kunna starta operationen med en första koordinat och möjliggöra beräkningar parallellt med robotrörelser. Fyra olika hårdvaruarkitekturer testas, bestående av två olika enkortsdatorer, ett kluster av enkortsdatorer och en marknadsledande dator som en efterliknad lokal molnlösning. Alla system utom klustret visar sig prestera utan operationell fördröjning för applikationen. Den optimala hårdvaruarkitekturen visar sig därmed vara en konsumentklassad enkortsdator, optimerad på energieffektivitet, kostnad och storlek. Om endast variansen i kommunikationstid kan minskas visar klustret potential för att kunna reducera den totala beräkningstiden utan att skapa operationell fördröjning. Smart avlastning till djupinlärningsoptimerade molnresurser eller kluster av sammankopplade robotstationer visar sig möjliggöra ökad komplexitet och tillförlitlighet av algoritmen. Enkortsdatorn visar sig även kunna växla mellan en edge- och en klusterkonfiguration, för att antingen optimera för tiden att starta operationen eller för den totala beräkningstiden. Detta medför en hög flexibilitet i industriella sammanhang, där produktändringar kan hanteras utan behovet av hårdvaruförändringar för visuella beräkningar, vilket ytterligare möjliggör dess integrering i fabriksenheter.