Adaptations to a fluctuating market and intensified customer demands for unique products are a challenge for manufacturers. Manual manufacturing is still the most flexible, nevertheless, automation ensures stable quality, minimizes wear and tear of the operators, and contributes to a safer and better working environment as the distance between the operator and the process can be increased and screened off. Hence, the manufacturing industry is searching for human-centric automation solutions that are flexible enough to handle these challenges. Conventional automation is tailored for one or a few similar variants of products, in addition, increased flexibility implies increased complexity to handle. This licentiate thesis demonstrates a flexible Plug &amp; Produce automated manufacturing concept where the complexity is redirected to focus on the products and manufacturing processes by utilizing artificial intelligence. Together with digital threads that connect the product design to automatic manufacturing that enables manufacturing companies to manage new production scenarios with their in-house knowledge. Data is picked directly from the computer-based design of the products and process knowledge that normally exists within the manufacturing company is added through graphical user interfaces. The graphical configuration tools visualize the flow of sequential and parallel manufacturing operations together with process-bound information. Plug &amp; Produce relies on pluggable process modules with re-cyclical manufacturing resources that can be plugged in and out as needed. As an example, a module with a robot can be plugged in to help an existing robot and thereby balance the production capacity. In Plug &amp; Produce resources start working and cooperate with other resources automatically when they are plugged in. To achieve this, the resources are provided with distributed artificial intelligence together with intelligent products that know how to be finalized. In this concept, everything is digitally configurable by the in-house knowledge of the manufacturing companies. A Plug &amp; Produce test bed was built to verify the concept in cooperation with industrial representatives. / Denna licentiatavhandling påvisar ett koncept för att öka flexibiliteten och samtidigt rikta om komplexiteten i automatiserade produktionssystem hos tillverkande företag på ett sätt så att deras interna personal på egen hand kan ställa om tillverkningen mot nya produkter. Anpassningar till marknadens fluktuationer och efterfrågan av nya unika produkter är en ständigt pågående process. Alltmer av produktionen flyttas tillbaka till Sverige och övriga Europa vilket ökar efterfrågan på flexibel och omställbar automation. Automation håller nere prisnivån då arbetskraften är dyr, säkerhetsställer jämn kvalité, minimerar förslitningsskador på de anställda och bidrar till säkrare och trevligare arbetsmiljö då distansen mellan operatör och process kan ökas och avskärmas. Produktion som flyttas till hemmamarknaden från låglöneländer ersätter ofta högflexibel och anpassningsbar manuell tillverkning vilket är en stor utmaning för industrin. Ett Plug &amp; Produce koncept för automatiserad tillverkning utvecklas och beskrivs i denna avhandling där automationen enkelt kan ställas om av den interna personalen och anpassas till nya produkter. Omställning med hjälp egen personal möjliggörs genom att så mycket information som möjligt utvinns från produktens datorbaserade design. Processkunskap som normalt besitts inom det tillverkande företaget adderas därtill med hjälp av grafiska användarinterface som visar flödet av tillverkningsoperationer tillsammans med processpecifika uppgifter såsom mått, bearbetningshastigheter, temperaturer och färg. Plug &amp; Produce system är uppbyggda kring processmoduler med tillverkningsresurser som kan pluggas in och ut efter behov. Till exempel kan en modul med en robot pluggas in för att avlasta befintlig robot och därmed öka produktionshastigheten. Specialdesignade resurser kan pluggas in för att öka effektiviteten och minimera energikonsumtionen. För att den inpluggade processmodulen självmant skall börja jobba och samarbeta med de andra modulerna är den försedd med egen lokal artificiell intelligens. Dessa processmoduler kan tack vare sin intelligens pluggas in i olika Plug &amp; Produce system och är därmed återvinningsbara i nya system. Intelligensen kan vara lokalt placerad i en dator på resursen eller i datormolnet kopplat till resursen. På samma sätt kan produkterna förses med intelligens och kallas då för smarta produkter. Dessa produkter har som mål att bli färdigproducerade genom delmål i form av tillverkningsoperationer. Denna intelligens förses med kunskap och erfarenheter av personalen inom det tillverkande företaget genom användarvänliga interface. När användarvänligheten Plug &amp; Produce testbädd har byggts upp tillsammans med representanter frånprefabricerade trähusindustrin. Tillverkning av prefabricerade trähus är i idag ihög grad manuell då existerande automationslösningar inte är flexibla nog eftersom husen är i hög grad är kundanpassade. Arbetet som beskrivs i denna avhandling gynnar trähusindustrin och därmed klimatet då trä binder kol för en lång tid framåt. / <p>Paper A is not included in the digital licentiate thesis due to copyright . </p>

The recharge is the focus of data analysis of the different situations with the integration of the system and development of the two-stage 2/2 proportional cartridge valve for the steel industry machine. Using the statistical analysis technique to visualize the valve signal data behavior identify the accuracy of the machine data and apply the statistical feature extracting model using classification and clustering algorithms of real-time data analysis for the manufacturing. The fundamental principles of data analysis with a particular emphasis on its key function in the collection, cleansing, and analysis of substantial amounts of data to develop significant insights. Moreover, we explore the importance of data visualization in effectively presenting intricate research outcomes. We get the data accuracy of 76 percent for train and test set data in the statistical analysis feature indicating the best accuracy in the early stage. Our model gives high accuracy of the recommendation data automation system of the steel industry. Analysis of the valve data in multiple ways for the predictive maintenance of conditional monitoring of the tubes mail production machine. PdM is used for data processing of predictive manufacturing, behavior patterns of machines data, and correlation of statistical model for decision making for the maintenance activity avoiding downtime.  The data consists of different channels in the steel industry machine. Some automation process is used for the feature combination of the analysis of valve data in industry between each feature and signals. Using a dataset comprised of sensor data, operation logs, and maintenance records industrial control data of machines and use of this predictive model has the potential to yield significant cost savings for the steel industry through the prevention of unplanned maintenance, while also enhancing operational safety manufacturing of machine in the industry.

Global consumption levels currently extend far beyond what planet Earth in terms of natural resources can regenerate in a sustainable manor and will by 2050 reach levels corresponding to what it would require three Earths to sustain. This overexploitation and unsustainable management of the Earth’s resources in combination with the necessity of mitigating climate change and reaching net zero CO2 emissions by 2050 require action across all sectors, not least the manufacturing industry. This thesis covers how large manufacturing enterprises can implement and utilize strategic targets and Key Performance Indicators (KPIs) to align with the principles of a Circular Economy (CE), and as a result, improve sustainability and business performance. Based on a case study conducted at Siemens Energy (SE) involving a literature study, interview study, and focus groups, a carefully selected set of strategic circularity targets and KPIs are presented to measure, evaluate, and drive circularity performance within large manufacturing enterprises. Since the thesis’ ambition was to provide valuable insights beyond SE, strategic circularity targets and KPIs specifically directed at SE were further generalized to be universally relevant for academia and other large manufacturing enterprises. Enterprises within the given sector share several key characteristics such as extensive material resource flows and complex value chains, hence strategic targets and KPIs emphasize material efficiency through decreasing virgin material dependency, increasing recirculation rates, and transitioning towards circular business models. While suggested targets and KPIs are universally directed at large manufacturing enterprises, individual organizations are recommended to conduct internal investigations and analyzes to further tailor and adapt strategic targets and KPIs towards the specific enterprise. In addition to strategic targets and KPIs, the thesis also presents an overview of opportunities, benefits, risks, and potential impacts for large manufacturing enterprises aspiring to increase circular initiatives, highlighting key principles to manage risk and capitalize on opportunities. The findings conclude that the main opportunity enabled by CE is to leverage synergies which align environmental, economic, and strategic corporate incentives, with key benefits including aspects such as decarbonization and reduced environmental impact, increased revenues and cost savings, risk management, and new business opportunities. Risks associated with CE include rebound effects, organizational insufficiencies, lack of material quality and safety, as well as a low product performance, which further can lead to potential impacts mitigating the positive effects of CE, or at worst setbacks causing a net negative output from implemented circular measures. In summary, the opportunities and benefits associated with CE are many, but implemented circular measures require risk awareness and continuous management to ensure efficiency.