Laser powder bed fusion is one of the most effective ways to achieve metal additive manufacturing. However, this method still suffers from deformation, delamination, dimensional error, and porosities. One of the most significant issues is poor printing accuracy, especially for small features such as turbine blade tips. The main reason for the shape inaccuracy is the heat accumulation caused by using constant laser power regardless of the shape variations. Due to the highly complex and dynamic nature of the laser powder bed fusion, improving the printing quality is challenging. Research gaps exist from many perspectives. For example, the lack of understanding of multi-physical melt pool dynamics fundamentally impedes the research progress. The scarcity of a customizable laser powder bed platform further restricts the possibility of testing the improvement strategies. Additionally, most state-of-the-art quality inspection techniques suitable for laser powder bed fusion are costly in economic and time aspects. Lastly, the rapid and chaotic printing process is hard to monitor and control. This dissertation proposes a complete research scheme including a fundamental physics study, process signature and quality correlation, smart additive manufacturing platform development, high-performance sensor development, and a robust real-time closed-loop control system design to address all these issues. The entire research flow of this dissertation is as follows: 1. This work applies and integrates three advanced sensing technologies: synchrotron X-ray imaging, high-speed IR camera, and high-spatial-resolution IR camera to characterize the melt pool dynamics, keyhole, porosity formation, vapor plume, and thermal evolution in Ti-64 and 410 stainless steel. The study discovers a strong correlation between the thermal and X-ray data, enabling the feasibility of using relatively cheap IR cameras to predict features that can only be captured using costly synchrotron X-ray imaging. Such correlation is essential for thermal-based melt pool control. 2. A highly customizable smart laser powder bed fusion platform is designed and constructed. This platform integrates numerous sensors, including but not limited to co-axial cameras, IR cameras, oxygen sensors, photodiodes, etc. The platform allows in-process parameter adjusting, which opens the boundary to test various control theories based on multi-sensing and data correlations. 3. To fulfill the quality assessment need of laser powder bed fusion, this dissertation proposes a novel structured light 3D scanner with extraordinary high spatial resolution. The spatial resolution and accuracy are improved by establishing hardware selection criteria, integrating the proper hardware, designing a scale-appropriate calibration target, and developing noise reduction procedures during calibration. Compared to the commercial scanner, the proposed scanner improves the spatial resolution from 48 µm to 5 µm and the accuracy from 108.5 µm to 0.5 µm. 4. The final goal of quality improvement is achieved through designing and implementing a real-time closed-loop system into the smart laser powder bed fusion platform. The system regulates the laser power based on the monitoring result from a novel thermal sensor. The desired printing temperature is found by correlating the laser power, the dimensional accuracy, and the thermal signatures from a set of thin-wall structure printing trails. An innovative high-speed data acquisition and communication software can operate the whole system with a graphic user interface. The result shows the laser power can be successfully controlled with 2 kHz, and a significant improvement in small feature printing accuracy has been observed. / Doctor of Philosophy / Laser powder bed fusion is one of the most effective ways to achieve metal additive manufacturing. However, this method still suffers from defects such as deformation, delamination, dimensional error, and porosities. Due to the highly complex and dynamic nature of the laser powder bed fusion, improving the printing quality is challenging. Research gaps exist from many perspectives, such as the lack of understanding of melt pool dynamics; the scarcity of a customizable laser powder bed platform; the need for suitable sensors; and the missing of a control system that can effectively regulate the rapid and chaotic printing process. This dissertation proposes a complete research scheme to address all these issues. The fundamental study characterizes the melt pool dynamics and discovers a strong correlation between the melt pool thermal and geometrical data, enabling thermal-based melt pool control. Following that, a highly customizable smart laser powder bed fusion platform is designed and constructed. The platform allows in-process parameter changes, opening the boundary to test various control theories. A novel structured light 3D scanner with an ultra-high spatial resolution was proposed to fulfill the quality assessment need. The final goal of quality improvement is achieved through designing and implementing a real-time closed-loop system into the smart laser powder bed fusion platform. The system regulates the laser power based on real-time thermal monitoring. The result shows the laser power can be successfully controlled with 2 kHz, and a significant improvement in printing accuracy is achieved.

Abstract: Title: The emergence of Big Data and Auditors' Perception (A comparative study on India and Bangladesh) Aim: The aim of the study is to explore and compare the perception of auditors in India and Bangladesh towards the implementation of big data analytics in audit. Method: In this study a qualitative method has been applied using semi-structured interviews. The study is an exploratory research and has been analysed thematically. Results and conclusions: Employing the Technology Acceptance Model (TAM) as a conceptual framework, this study conducted a comparative analysis of auditors' perceptions, emphasizing the components of perceived usefulness, perceived ease of use, intention to adopt, and their interactions. The results of the study show that the intention to adopt big data analytics tools emerges as a shared aspiration among auditors from both India and Bangladesh.

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Объектом исследования являются cстатистические методы контроля производственных процессов. Предмет исследования – разработка ПО для статистического анализа производственных данных. В данном исследовании производится разработка программного обеспечения для контроля производственных процессов путём визуализации показателей датчиков оборудования методом контрольных карт Шухарта с использованием средства Jupyter Lab. / The object of this research are statistical production control methods. The subject of research is the development of an application for the statistical analysis of production process data. This research consists of developing software for production process control using visualization of machinery sensor data using Shewhart control charts with Jupyter Lab.

Purpose - Procurement is an integrated part of the supply chain and crucial for the success of manufacturing. Many organisations have already started the digitalisation of their manufacturing processes using Industry 4.0 technologies and consequently trying to understand how this would impact the procurement function. The research purpose is to conceptualize a procurement of 4.0 model for a truly data driven procurement. Two research questions were proposed to address the model from digital capabilities and sustainability preceptive. Design/Methodology/approach - This study is based on a systematic literature review. A method of reviewing the literature and the current research for the propose of conceptualizing a procurement 4.0 model. Findings - The findings from the literature review contributed to the development of a proposed procurement 4.0 model based on Industry 4.0 technologies, applications, mathematical algorithms and procurement processes automation. The model contributes to the research field by addressing the gap in the literature about the lack of visualization and conceptualization of procurement 4.0. Originality/Value - The current literature discusses the advantages, implementation and impact of individual or a group of industry 4.0 technologies and applications on procurement but lacks visualization of the transformation process of combining the technologies to enable a truly data driven procurement. This research supports the creation of knowledge in this area. Practical Implementation /Managerial Implications - The proposed model can support managers and digital consultants to have practical knowledge from an academic perspective in the area of procurement 4.0. The knowledge from the literature and the systematic literature review is used to create knowledge on procurement 4.0 applications and analytics taking in to consideration the importance of visibility, transparency, optimization and the automation of the procurement function and its sustainability. / Syfte - Upphandling är en integrerad del av supply chain och avgörande för tillverkningens framgång. Många organisationer har redan börjat digitalisera sina tillverkningsprocesser med hjälp av Industry 4.0-teknologier och försöker därför förstå hur detta skulle påverka upphandlingsfunktionen. Målet med studien är att konceptualisera en upphandling av 4.0-modellen för en verkligt datadriven upphandling. Två forskningsfrågor föreslogs för att ta itu med modellen från digital kapacitet och hållbarhet. Design / metod / tillvägagångssätt - Denna studie baseras på en systematisk litteraturstudie. En metod för att granska litteraturen och den aktuella forskningen för att föreslå konceptualisering av en upphandlings 4.0-modell. Resultat - Resultaten från litteraturstudien bidrog till utvecklingen av en föreslagen upphandlings 4.0-modell baserad på Industry 4.0-teknologier, applikationer, matematiska algoritmer och automatisering av upphandlingsprocesser. Modellen bidrar till forskningsområdet genom att ta itu med klyftan i litteraturen om bristen på visualisering och konceptualisering av upphandling 4.0. Originalitet / värde - Den nuvarande litteraturen diskuterar fördelarna, implementeringen och effekten av individer eller en grupp av industri 4.0-teknologier och applikationer på upphandling men saknar visualisering av transformationsprocessen för att kombinera teknologierna för att skapa en verklig datadriven upphandling. Denna forskning stöder skapandet av kunskap inom detta område. Praktisk implementering / chefsimplikationer - Den föreslagna modellen kan stödja chefer och digitala konsulter att ha praktisk kunskap ur ett akademiskt perspektiv inom området upphandling 4.0. Kunskapen från litteraturen och den systematiska litteraturstudien används för att skapa kunskap om inköp 4.0 applikationer och analyser med beaktande av vikten av synlighet, transparens, optimering och automatisering av upphandlingsfunktionen och dess hållbarhet.

Data Analytics (DA) has been blamed for contributing to discriminatory managerial decisions in organizations. To date, most studies have focused on the technical antecedents of such discriminations. As a result, little is known about how to ameliorate the problem by focusing on the human aspects of decision making when using DA in organizational settings. This study represents an effort to address this gap. Drawing on the cognitive elaboration model of ethical decision-making, construal level theory, and the literature on moral intensity, this study investigates how the availability and the design of demographic transparency (a form of decisional guidance) can lower DA users’ likelihood of agreement with discriminatory recommendations of DA tools. In addition, this study examines the role of user’s mindfulness and organizational ethical culture on this process. In an experimental study users interact with a DA tool that provides them with a discriminatory recommendation. The results confirm that demographic transparency significantly impacts both recognition of the moral issue at hand and perceived proximity toward the subject of the decision, which in turn help decrease the likelihood of users’ approval of the discriminatory recommendation. Moreover, the results suggest that user’s mindfulness and organizational ethical culture enhance the positive impacts of demographic transparency. / Dissertation / Doctor of Philosophy (PhD)