Product development is affected by uncertainties due to changing customer requirements, changing regulations, technological developments, long lead times, high product complexities, and geopolitical issues. Automation, increased flexibility of production, and reduced lead times are drivers that allow product development to be competitive in this scenario. Design engineers should be aware of production capabilities to facilitate early producibility assessments and to avoid late changes. Production preparation is identified as an important activity in the product development process, whereby the producibility of a product is assessed. In this thesis, the current state of production preparation during product development is investigated and a method is introduced supporting production preparation using production requirements. The work was carried out using the design research methodology framework and comprised four studies based on the four steps of the framework. The research clarification and descriptive study 1 phases aimed at developing understanding and were done by means of data collection at the companies through interviews and document studies. The next two steps were prescriptive study and descriptive study 2, which aimed at developing and evaluating the support. This was done through observation, workshops, and solution development. The production preparation process is supported by Design for Manufacturing and Assembly, failure mode and effects analysis, lesson-learnt documents, and computer-aided design, and the efficiency of the process is dependent on individual skills and knowledge. Tools to support common understanding, remove ambiguity in requirements, and enable collaboration between design and production engineers are needed. The developed method allows for the identification, definition, structuring, and sharing of production requirements, aligning with varying maturities of product and production systems during product development. This helps improve the collaboration between design and production engineers for production preparation.

Material utilisation is of importance in the manufacturing industry formaking the most of each material, minimising waste and increasing cost-effectiveness. In this thesis, samples of Waspaloy built with the method of L-DED-P has been analysed in order to investigate how process pa-rameters influence the build quality and geometrical accuracy in as-builtobjects. The samples analysed was built in single rows of one, three,five and fifteen layers. A build process was used in which the sampleswere built with individual combinations of the process parameters laserpower, scanning speed, and powder flow. Each combination of processparameters was used to build one track for each layer height.Analysis included defect analysis with light optical microscopy, andpost-processing with ImageJ for automatic identification, quantification,and collection of measurements. A qualitative analysis was performed andthe sample properties and characteristics was described in terms of theamount of defects, including a descriptive assessment of defect severity.Etched samples revealed a columnar grain structure in samples, which was apparent in builds with at least three layers.The results presented show a difference in build height, quantity andsize of pores, and the presence or absence of other defects such as lack of fusion. Sample 3 which was built with high laser power, slow scan-ning speed, and high powder feed show promising results with one ofthe highest build rates of all samples, combined with a low normalisedpore distribution. The sample experiences partial hardening, with hard-ness values reaching 320 HV, but still promisingly show no sign of crackformation.It is concluded that powder feed relates primarily to the build rateof the samples, and the scanning speed together with the laser power influence the quality of the build, where high laser power and low scanningspeed tends to form well behaving samples with few defects, whilst other combinations increase the risk of defects.

Detta arbete initieras för att möta Utform AB:s önskan om att utveckla säkra,hållbara och pedagogiska lekplatser av trä som ger barn både pedagogiska värdenoch lekglädje. Syftet är att utveckla säkra och pedagogiska lekutrustningar isamarbete med företaget. Fokus ligger på att främja barns lek och lärande, samtidigtsom lekutrustningerna uppfyller gällande riktlinjer och krav för lekplatsmiljöer.En omfattande produktutvecklingsprocess omfattar kravspecifikation, konceptutveckling,materialval och stabilitetsberäkningar. Kravspecifikationen formuleras genomlitteraturstudier, intervjuer och workshops, med betoning på fri lek och tillgänglighet,särskilt för barn med rullstol. Konceptutvecklingen involverade brainstormingoch designthinking, med prototypskapande och konceptsållning för att utvärderaoch kombinera idéer. Materialvalet baseras på en systematisk metod somintegrerade egenskaper, kostnader och miljöpåverkan. Stabilitets- och hållfasthetsberäkningar,inklusive Eulers knäckningsfall, användas för att säkerställa att lekutrustningernakan hantera belastningar och maximera säkerheten.Designen av lekutrustning som tas fram anpassas för att uppfylla standarden SSEN1176-1:2017 och säkerhetsaspekter som höjd och öppningar behandlades för attundvika risker för fall. De preliminära beräkningarna visade att det valda materialet,uppfyller kraven för att hantera belastningar enligt standarden. Beräkningarnaindikerade också att förstärkningsåtgärder skulle genomföras för att ytterligare ökastabiliteten. Resultaten av undersökningen ligger till grund för att skapa en säkerlekmiljö, vilket är en primär målsättning under hela designprocessen. / This project is initiated to meet the desire of Utform AB to develop safe, sustainable,and educational wooden playgrounds that provide both educational value and joyof play for children. The goal is to develop safe and educational play equipment incollaboration with the company, focusing on promoting children’s play and learningwhile ensuring that the products comply with current guidelines and requirementsfor playground environments.An extensive product development process includes requirement specification, conceptdevelopment, material selection, and stability calculations. The requirementspecification is formulated through literature studies, interviews, and workshops,with an emphasis on free play and accessibility, especially for children using wheelchairs.Concept development involved brainstorming and design thinking, withprototype creation and concept evaluation to assess and combine ideas. Materialselection is based on a systematic method integrating properties, costs, and environmentalimpact. Stability and strength calculations, including Euler’s buckling,are used to ensure that the products can handle loads and maximize safety.The design of the play equipment is tailored to meet the standard SS-EN 1176-1:2017, and safety aspects such as height and openings are addressed to avoid fallrisks. Preliminary calculations showed that the chosen material meets the requirementsto handle loads according to the standard. The calculations also indicatedthat reinforcement measures would be implemented to further enhance stability.The results of the investigation form the basis for creating a safe play environment,which is a primary objective throughout the design process.

Nowadays, interconnectivity is becoming increasingly important. According to industry, the Industrial Internet of Things (IIoT) is the core technology of Industry 4.0. Connectivity between different processes or components in the industry makes one process aware of each other, making systems more intelligent and self-sufficient. This thesis proposes various ways to control and monitor an industrial process with the cloud, explaining step by step the connections between the industrial equipment and the cloud. Furthermore, this thesis's one sub-objective is to make an industrial process, which was previously PLC-based, entirely cloud-based. For the latter work, simulation software is used to create a production line, as there is a lack of equipment. Consequently, it will also test the flexibility of the system to move between the virtual and the real environment, which is another challenge of Industry 4.0 and digitalisation. In the discussion part, the thesis talks about the shift from PLC-based to PC-based systems in the industry, mentioning the limitations and advantages.

Programming industrial robots for welding or part manipulation tasks is a time-consuming and complicated process, resulting in companies not able to implement robot systems and exploit their advantages. To reduce the time needed for programming, research is looking into ways to automate this process and reduce manual labour.In this thesis a concept for automating the programming process of industrial robots was investigated using EXCEL VBA and CATIA/DELMIA. It was done for an industrial grating model of varying sizes and configurations, resulting in a time reduction of 99% compared to manual creation. For this, the model was first automatically created from scratch for the required configuration and afterwards a robot motion was created fully automatically. The concept and modelling approach is described, and the automation approach detailed. Finally, the results are analysed and discussed.

With the development towards Industry 5.0, manufacturing companies are developing towards smart production. In smart production, data is used as a resource to interconnect different elements in the production system to learn and adapt to changing production conditions. Common objectives include human-centricity, resource-efficiency, and sustainable production. To enable these desired benefits of smart production, there is a need to use digital technologies to create and manage the entire flow of data. To enable smart production, it is essential to deploy digital technologies in a way so that collected raw data is converted into useful data that can be applied by equipment or humans to generate value or reduce waste in production. This requires consideration to the data flow within the production system, i.e., the entire process of converting raw data into useful data which includes data management aspects such as the collection, analysis, and visualization of data. To enable a good data flow, there is a need to combine several digital technologies. However, many manufacturing companies are facing challenges when selecting suitable digital technologies for their specific production system. Common challenges are related to the overwhelming number of advanced digital technologies available on the market, and the complexity of production system and digital technologies. This makes it a complex task to understand what digital technologies to select and the recourses and actions needed to integrate them in the production system. Against this background, the purpose of this licentiate thesis is to examine the selection and integration of digital technologies to enable smart production within manufacturing companies. More specifically, this licentiate thesis examines the challenges and critical factors of selecting and integrating digital technologies for smart production. This was accomplished by performing a qualitative-based multiple case study involving manufacturing companies within different industries and of different sizes. The findings show that identified challenges and critical factors are related to the different phases of the data value chain: data sources and collection, data communication, data processing and storage, and data visualisation and usage. General challenges and critical factors that were related to all phases of the data value chain were also identified. Moreover, the challenges and critical factors were related to people, process, and technology aspects. This shows that there is a need for holistic perspective on the entire data value chain and different production system elements when digital technologies are selected and integrated. Furthermore, there is a need to define a structured process for the selection and integration of digital technologies, where both management and operational level are involved. / Med utvecklingen mot Industri 5.0 utvecklas tillverkningsföretag mot smart produktion. I smart produktion används data som en resurs för att koppla samman olika element i produktionssystemet i syfte att lära sig om och anpassa sig efter förändrade produktionsförhållanden. Vanliga mål för smart produktion inkluderar resurseffektivitet, och en hållbar produktion anpassad utifrån människan. För att åstadkomma dessa önskade fördelar, behöver tillverkningsföretag använda digitala teknologier för att skapa och hantera hela dataflödet. För att möjliggöra smart produktion är det viktigt att implementera digitala teknologier på ett sätt så att insamlad rådata omvandlas till användbar data som kan tillämpas av maskiner eller människor för att skapa värde eller minska slöseri i produktionen. Detta kräver hänsyn till dataflödet inom produktionssystemet, det vill säga hela processen att omvandla rådata till användbar data som inkluderar datahanteringsaspekter som exempelvis insamling, analys och visualisering av data. För att möjliggöra ett bra dataflöde krävs det att flera digitala teknologier kombineras. Många tillverkningsföretag står dock inför flera utmaningar när de ska välja lämpliga digitala teknologier för sitt specifika produktionssystem. Vanliga utmaningar är relaterade till det överväldigande antalet avancerade digitala teknologier som finns på marknaden, samt komplexiteten hos produktionssystem och digitala teknologier. Detta gör det till en komplex uppgift att förstå vilka digitala tekniker som ska väljas och vilka resurser och åtgärder som behövs för att integrera dem i produktionssystemet. Mot denna bakgrund är syftet med denna licentiatuppsats att undersöka hur tillverkningsföretag ska välja och integrera digitala teknologier för att uppnå smart produktion. Mer specifikt så undersöker denna licentiatuppsats vilka utmaningar och kritiska faktorer som finns för att välja och integrera digitala teknologier för att uppnå smart produktion. Detta uppnåddes genom en kvalitativ multipel fallstudie med tillverkningsföretag inom olika branscher och av olika storlekar. Resultaten visar att identifierade utmaningar och kritiska faktorer är relaterade till de olika faserna av datavärdekedjan: datakällor och insamling, datakommunikation, databearbetning och lagring samt datavisualisering och användning. Generella utmaningar och kritiska faktorer som var relaterade till alla faser av datavärdekedjan identifierades också. Dessutom var utmaningarna och kritiska faktorerna relaterade till människa, process och tekniska aspekter. Detta visar att det finns ett behov av helhetsperspektiv på hela datavärdekedjan och olika element i produktionssystemet när digitala teknologier väljs och integreras. Dessutom finns det ett behov av att definiera en strukturerad process för val och integration av digital teknik, där både ledning och operativ nivå är involverade.

Purpose: Information management is crucial for a food manufacturing company as it increases productivity, lowers cost, and enables traceability as well as data-driven decision-making. A lack of information management leads to consequences such as lack of customer demand and requirements, which result in high inventory or stockout. The purpose of this study is to enable digitalized information management in food value chains, and this study aims to know which information needs to be considered for data-driven decision-making. The purpose and aim align with adapting to the current trends of industry 4.0 and digitalized information management. To fulfill the purpose, two research questions are formulated: (1) What type of information needs to be considered for data-driven decision-making in food value chains? and (2) How should the parts of the value chain be digitalized to enable data-driven decision-making in food value chains?  Method: The chosen approach for this study was an inductive approach and the chosen strategies were a literature review and a single case study in the Swedish food manufacturing industry. To gain an understanding of the case company’s value chain, interviews were used as a data collection technique. The interviews were then analyzed and then combined with the literature review to answer both the research questions. The quality of the study was evaluated by using thetrustworthiness criteria. There were also five principles, when it comes to research ethics, that were used during this study.   Findings: After the conducted interviews, the relevant actors were identified and mapped in the case company’s value chain. It was found that the company used a lot of manual information management procedures, which led to several challenges for data-driven decision-making within the company. The mapping revealed which information was relevant for the respective actors within the case company. This provided a starting point from which empirical and theoretical data were studied to address these challenges and answer the two research questions and fulfill the purpose of this study.   Analysis: To build a framework for digital information management, it was necessary to customize existing equipment and resources, adapt the process to the specific industry, and move toward the concept of industry 4.0. With these elements in mind, the framework was created using data acquired from both the literature research and the case study. The framework consists of a loop, that allows for continual improvement.   Conclusion: Applying the correct technology is important for digitalized information management, and the food manufacturing industry suffers from limited technologies in this aspect. This thesis informs the reader on how to digitalize information management. The academic contribution is a theoretical framework for the digitalization of information management using industry 4.0 concepts, which can support companies to generate revenue. / <p>Externt samarbete utesluts, eftersom företaget inte vill skylta med sitt namn. </p>

Purpose. Real-time Locating Systems (RTLS) are used to identify and locate physical assets in real-time. Essentially, the aim of RTLS is to increase process visibility and thus provide accurate process data concerning for example, lead times, cycle times and inventory control. Today, process visibility is one of the most critical aspects for manufacturing companies in their quest for higher quality and shorter lead times. Since the introduction of Industry 4.0, RTLS has received an upswing in attention in the literature. However, despite the technical advances, enterprises are struggling to fully comprehend the entire impact of RTLS. Further, when integrating RTLS and other IT systems, such as the ERP, business operations can be carried out based on real-time information, providing more value than a standalone RTLS. Enterprise Resource Planning (ERP) system is the core IT system for enterprises. The ERP is in turn divided into several modules: Manufacturing, Warehouse, Human resources, Sales &amp; Distribution, and Finance &amp; Accounting. Evaluating the applicability for each module will provide a holistic view on RTLS and ERP integration that has a wider perspective than previous studies on the topic. Consequently, the purpose of this study was to examine and evaluate the applicability of integrating RTLS with the various ERP modules and present a set of guidelines for RTLS and ERP integration. The guidelines will be created based on the following research questions. RQ1: How can the various ERP modules utilize RTLS data? RQ2: What barriers and enablers are there when integrating RTLS and ERP? Research method. An extensive literature review and interviews with industry experts were conducted. Thematic analysis and thematic narrative analysis were applied to extract relevant information form the literature review and industry expert interviews. Thematic synthesis was applied to synthesize the information received from the result of the thematic analysis and thematic narrative analysis, thence answer the research questions, and create the guidelines. Findings. Applicability was found for all ERP modules and the main barriers identified was lack of mature processes, knowledge, and cost. The guidelines created in the study included five main steps. 1) organizational enablers, 2) IT structure enablers, 3) ERP module applicability, 4) knowledge and understanding, and 5) business case generation. The guidelines were presented in text and as a picture. Contributions. This study has contributed to research by expanding the perspective on RTLS and ERP integration further than previous studies on the topic. The study showed that all ERP modules can utilize the RTLS data to provide value for that particular module. This study contributes to industry by providing guidelines that can be utilized by managers and decision makers in their process of evaluating RTLS implementation Managers and decision makers can use these guidelines to create a fundamental understanding of how RTLS can create value for all ERP modules. This knowledge is a key to create profitable business cases. The guidelines will therefore facilitate enterprises to overcome the initial knowledge barriers and thus, contribute to the acceleration of the RTLS expansion within the industry.

Background: The term physical behavior includes sedentary and non-sedentary everyday physical activities. Objective measurement methods are recommended when sedentary behaviors are to be measured. Data from a thigh worn accelerometer can, together with the Acti4 software, be used to distinguish different physical behaviors. Due to different shapes of thighs and how the accelerometers have been placed on the thighs there is a need to compensate for individual differences in reference angles in respect to gravity. Normally this is done by letting the participants perform a reference position when they stand up and that time is noted in a diary. However, this is very time  consuming. One alternative is to automatically estimate the compensation angles from walking episodes in the existing data. Objective: The aim of this study was to validate two new features to the Acti4 software, detection of lying and automatic reference angle generation. Methods: Ten participants underwent a 45-minute standardized protocol, and a 48-hour free-living protocol, with recordings of the different physical behaviors lying, sitting, standing, walking, running, stair walking, and cycling. Video recordings of the standardized protocol, and accelerometer data and diaries together with the participants review of the results from the software from the free-living protocol, has been used to validate the software. Results: During the standardized protocol, the overall accumulated performance of the software to identify the same physical behavior as in the video recordings was 81.1% (sensitivity), 96.7% (specificity) and 94.8% (accuracy). Participants agreed that most identified physical behavior episodes were correct, when reviewing the result from the software. When the two different reference angle generation methods were compared, almost no difference was found in the results of the standardized protocol, neither when comparing total time registered per behavior accumulated for all participants during the free-living protocol. Conclusions: The software may be used in research as a valid tool to measure physical behavior. However, the sensitivity to detect lying and stair walking may vary between individuals. When conducting research with thigh worn accelerometers, researchers may use the easier to use method with automatically generated reference angles instead of the more burdensome method with manually recorded diary annotation.

The advanced handling of uncertainties arising from a wide range of sources is fundamental in quality control and dependability to reach advantageous decisions in different organizational levels of industry. Es-pecially in the competitive edge of production, uncertainty shall not be solely object of estimation but the result of a systematic management process. In this process, the composition and utilization of proper in-formation acquisition systems, capability models and propagation tools play an inevitable role. This thesis presents solutions from production system to operational level, following principles of the introduced con-cept of uncertainty-based thinking in production. The overall aim is to support transparency, predictability and reliability of production sys-tems, by taking advantage of expressed technical uncertainties. On a higher system level, the management of uncertainty in the quality con-trol of industrial processes is discussed. The target is the selection of the optimal level of uncertainty in production processes integrated with measuring systems. On an operational level, a model-based solution is introduced using homogeneous transformation matrices in combination with Monte Carlo method to represent uncertainty related to machin-ing system capability. Measurement information on machining systems can significantly support decision-making to draw conclusions on man-ufactured parts accuracy, by developing understanding of root-causes of quality loss and providing optimization aspects for process planning and maintenance. / <p>QC 20181015</p>