The current production field operates on linear resource flows, characterized by known supply and consistent manufacturing materials. In contrast, a remanufacturing system experiences variable process flows for each produced unit, even though the end product remains the same. The uncertainty surrounding the quality of cores can impact tactical decisions. This variability is influenced by factors such as the quality and condition of the core upon arrival at the facility, leading to uncertainties in cost and production times. The purpose of this research is to develop a cost estimation method for product recovery decision-making processes and enhance the flow efficiency of remanufacturing processes. To achieve this goal, the study identifies and investigates the factors that affect the condition of the core, represented by processing time. Subsequently, the analysis focuses on processing time and spare parts cost, and introduces a cost estimation method based on historical data. Finally, the current process flow is examined, and suggestions for improvements are provided. This research employs two distinct approaches: statistical and quantitative analysis, as well as qualitative inquiry that considers the experiences and perceptions of employees and other non-numerical aspects. Therefore, an action research methodology has been used, combining scientific knowledge with existing expertise within an organization to address real organizational problems. The study develops a new inspection process that combines a prediction model for processing times with a thorough inspection of important parts identified through spare part analysis. Additionally, suggestions are made to improve the flow of the remanufacturing process by prioritizing critical inventory items and introducing a new inspection and cleaning station where necessary spare parts are ordered before the remanufacturing process begins. / REMARKABLE

Bridge inspection and infrastructure inspection are critical steps in the lifecycle of the built environment. Emerging technologies and data are driving factors which are disrupting the traditional processes for conducting these inspections. Because inspections are mainly conducted visually by human inspectors, this paper focuses on improving the visual inspection process with data-driven approaches. Data driven approaches, however, require significant data, which was sparse in the existing literature. Therefore, this research first examined the present state of the existing data in the research domain. We reviewed hundreds of image-based visual inspection papers which used machine learning to augment the inspection process and from this, we compiled a comprehensive catalog of over forty available datasets in the literature and identified promising, emerging techniques and trends in the field. Based on our findings in our review we contributed six significant datasets to target gaps in data in the field. The six datasets comprised of structural material segmentation, corrosion condition state segmentation, crack detection, structural detail detection, and bearing condition state classification. The contributed datasets used novel annotation guidelines and benefitted from a novel semi-automated annotation process for both object detection and pixel-level detection models. Using the data obtained from our collected sources, task-appropriate deep learning models were trained. From these datasets and models, we developed a change detection algorithm to monitor damage evolution between two inspection videos and trained a GAN-Inversion model which generated hyper-realistic synthetic bridge inspection image data and could forecast a future deterioration state of an existing bridge element. While the application of machine learning techniques in civil engineering is not wide-spread yet, this research provides impactful contribution which demonstrates the advantages that data driven sciences can provide to more economically and efficiently inspect structures, catalog deterioration, and forecast potential outcomes. / Doctor of Philosophy / Bridge inspection and infrastructure inspection are critical steps in the lifecycle of the built environment. Emerging technologies and data are driving factors which are disrupting the traditional processes for conducting these inspections. Because inspections are mainly conducted visually by human inspectors, this paper focuses on improving the visual inspection process with data-driven approaches. Data driven approaches, however, require significant data, which was sparse in the existing literature. Therefore, this research first examined the present state of the existing data in the research domain. We reviewed hundreds of image-based visual inspection papers which used machine learning to augment the inspection process and from this, we compiled a comprehensive catalog of over forty available datasets in the literature and identified promising, emerging techniques and trends in the field. Based on our findings in our review we contributed six significant datasets to target gaps in data in the field. The six datasets comprised of structural material detection, corrosion condition state identification, crack detection, structural detail detection, and bearing condition state classification. The contributed datasets used novel labeling guidelines and benefitted from a novel semi-automated labeling process for the artificial intelligence models. Using the data obtained from our collected sources, task-appropriate artificial intelligence models were trained. From these datasets and models, we developed a change detection algorithm to monitor damage evolution between two inspection videos and trained a generative model which generated hyper-realistic synthetic bridge inspection image data and could forecast a future deterioration state of an existing bridge element. While the application of machine learning techniques in civil engineering is not widespread yet, this research provides impactful contribution which demonstrates the advantages that data driven sciences can provide to more economically and efficiently inspect structures, catalog deterioration, and forecast potential outcomes.

<p dir="ltr">Unmanned Aerial Systems (UAS) have the potential to drastically change how civil infrastructure is inspected, monitored, and managed. This innovative technology can ensure the inspector’s safety, provide additional inspection information, and reduce costs. However, a challenge arose as this industry expanded: a lack of standardized guidelines or minimum performance requirements to perform these operations. With no standard tests to verify UAS’ ability to conduct inspections and unknown detection capabilities, agencies are left to rely upon consultants’ or vendors’ promotional material and claims when considering UAS deployment. The following work proposes a series of performance-based assessments and procedural documentation to establish minimum standards for using UAS in bridge inspection applications. Through this work, the following performance-based tests have been developed: (1) a controlled environment simulating bridge geometries to assess the overall capability of a UAS used for bridge inspection [evaluation chamber], (2) an assessment of UAS performance under multiple environmental temperatures [environmental temperature chamber], (3) a UAS performance assessment under varying wind speeds [wind chamber], (4) a consolidated checklist compiling Federal Aviation Administration guidelines and best practices [flight checklist], (5) a field assessment of UAS under conditions analogous to on-site bridge inspection [practical test]. For infrastructure owners, embracing these performance-based assessments will help ensure that UAS meets a minimum level of performance and allow owners to verify and distinguish between various UAS used for bridge inspection. This work also discusses positive feedback from beta testing provided by industry and infrastructure owner representatives, showcasing the effectiveness of providing an authentic assessment of UAS bridge inspection capabilities. Future work encourages the wide implementation of this assessment program and encourages owners to refrain from using untested technology in the inspection of their infrastructure.</p>

This study project looks at how metrology and inspection methods have advanced inside machining processes to improve production efficiency, accuracy, and precision. The study includes an extensive examination of the literature, observational research, and critical analysis to provide insights into the possibilities, difficulties, and practices that exist in the sector today. The literature review charts the development of metrology from manual measurement techniques to highly automated systems, emphasizing the crucial roles that optical inspection systems and coordinate measuring machines (CMMs) play in contemporary production. Modern inspection technologies, including as non-contact techniques and laser scanning, are also discussed, along with the difficulties in combining metrology data with digital production systems. Observational research offers direct knowledge of how metrology and inspection techniques are used in real-world industrial settings. Alongside difficulties including operator skill levels and equipment calibration, the usage of portable measuring tools, CMMs, and optical inspection systems for assessing surface finish and dimensional correctness is noted. Findings study points to growing problems such the requirement for experienced operators and operational unpredictability in machining processes, as well as the increasing integration of metrology and inspection methods into machining processes. But there are also prospects for development, with an emphasis on the ability to increase efficiency and quality control, via Industry 4.0 projects and additive manufacturing technology. All things considered, this study project clarifies the crucial function metrology and inspection methods play in contemporary manufacturing and provides guidance for enhancing quality assurance and machining process efficiency.

Titre de l'écran-titre (visionné le 5 juin 2023) / La fabrication additive (impression 3D) par fusion sélective au laser (FSL) permet de concevoir des pièces de géométries complexes en utilisant une large gamme de poudres métalliques. Cependant, la performance mécanique de ces pièces est affectée par trois facteurs : les porosités, l'anisotropie de la microstructure et les contraintes résiduelles (CR). À ce jour, l'inspection aux ultrasons pour le contrôle immédiat de la qualité des pièces n'a pas fait l'objet d'une étude systématique et complète. Ce projet de recherche a permis de développer des méthodologies aux ultrasons permettant de révéler et de quantifier la présence de défauts dans les pièces métalliques produites par fusion sélective au laser. D'abord, des échantillons d'acier (316L) et de titane (Ti-6Al-4V) de différentes densités, tailles de porosité et orientations granulaires ont été fabriqués en variant la vitesse et l'espacement entre les passes du laser. Ensuite, des échantillons porteurs de différents niveaux de contraintes internes ont été fabriqués en faisant varier la température du lit de déposition. Finalement, tous les échantillons ont été analysés aux ultrasons (vélocité, atténuation et fréquence de référence), ont fait l'objet d'une étude métallographique complète et ont été analysés par rayons X (niveau de CR). Les résultats de ces travaux ont démontré que l'analyse par ultrasons des pièces imprimées par FSL en acier et en titane était possible, et ce avec des fréquences ultrasonores allant de 5 MHz à 20 MHz. L'approche systématique utilisée a permis d'identifier et de quantifier les caractéristiques ultrasonores qui sont modifiées par les défauts métallurgiques présents dans des pièces issues de FSL. De plus, une corrélation a été établie entre les signatures ultrasonores et la masse volumique des échantillons. Cette étude permet de confirmer l'intérêt d'intégrer des pièces étalons issues de la fabrication additive dans le processus d'inspection aux ultrasons de pièces d'acier inoxydable et de titane issues elles aussi de procédés de fabrication additive. / The process of additive manufacturing (3D printing) by selective laser melting (SLM) enables the design of geometrically complex parts from a wide variety of alloys. However, their mechanical performance is impacted by the three following factors: porosity, microstructure anisotropy, and residual stress. Today, ultrasonic inspection for in-situ quality control of 3D-printed parts has not been subject to a systematic and comprehensive study. This project allows the development of a methodology using ultrasounds that reveals and quantifies metallurgic flaws inside parts manufactured by SLM. At first, samples of steel (316L) and titanium (Ti-6Al-4V) were produced by SLM at various densities, pore sizes, and grain orientations. These characteristics were obtained by varying the manufacturing speed and space between each path of the laser. Then, samples with different residual stress levels were produced by varying the build platform temperature. Finally, all the samples were analyzed with ultrasounds (velocity, attenuation, frequency), and were subject to a complete metallographic and X-ray study (residual stress). The results showed that the ultrasonic inspection of steel and titanium SLM parts was possible by using inspection frequencies between 5 MHz and 20 MHz. This systematic method revealed and quantified the ultrasonic characteristics affected by the metallurgic flaws contained in the SLM samples. Correlations between ultrasonic signature and density of samples were revealed. Overall, this study confirms the relevance of preliminary studies with standards made of metal parts produced by additive manufacturing, for the development of ultrasound-based nondestructive inspection procedures adapted to 3D-printed metal parts.

Master of Science / Department of Diagnostic Medicine/Pathobiology / Douglas A. Powell / The World Health Organization estimates that up to 30% of individuals in developed countries become ill from food or water each year, with up to 70% of these illnesses estimated to be linked to food service. Restaurant inspections aim to prevent restaurant-associated foodborne outbreaks while enhancing consumer confidence in the safety of food prepared in these establishments. Inspection disclosure systems have been developed as a tool for consumers and incentive for foodservice operators. Disclsosure systems are common in developed countries; however, they are inconsistent. Previous research has not determined the best format to disclose inspection results to the public while providing incentives for operators. This research aimed to develop a consistent, compelling and trusted disclosure system for New Zealand. The research evaluated existing disclosure systems operating internationally and nationally. The national review interviewed with Environmental Health Officers (EHOs)(n=8), operators (n=109) and consumers (n=244), and findings were used for card designs. Cards were evaluated internally (n=43), then by 11 focus groups (n=68). Two cards, letter and gauge, were introduced to food premises (n=371) in six districts for three months for evaluation. Operators (n=269) and consumers (n=991) were interviewed to determine which design best communicated inspection results. The majority of operators indicated they had not received consumer feedback about the card, and half felt the card was something consumers would use. Less than half of consumers indicated they noticed cards prior to entering a premises; from these data it appeared the letter attracted more initial attention (78% of respondents) than the gauge (45%). Consumers indicated card placement was an important factor in noticing cards. Nearly all interviewed consumers indicated they expected cards at restaurants, take-aways and fish n’ chips shops. When asked which card they preferred, 58% (n=38) of operators with the gauge preferred the letter; and 79% (n=47) of operators with the letter preferred the letter. Consumer preference was for the letter, with 88% (n=133) of those in gauge districts preferring the letter, and 72% (n=161) of those in letter districts preferring the letter. Based on these data the letter card was recommended for a national inspection disclosure system for New Zealand.

Typescripe (photocopy). / Digitized by Kansas Correctional Industries

Vi har valt att undersöka om oljebolagens krav är högre än hamnstatens eftersom vi under vår tid till sjöss har kommit i kontakt med ämnet ett flertal gånger. Ombord i fartygen pratas det mycket om inspektioner och de anses många gånger vara onödigt petiga. Resultatet i vår studie har visat att inspektionerna medför en ökning av säkerheten och medvetenheten av risker som kan föreligga. Vi redogör även för om det finns en möjlighet att slå samman inspektionerna för att reducera påfrestningen hos besättningen. För att få en inblick i organisationernas arbete har vi valt att göra en kvalitativ studie, grundad på strukturerade intervjufrågor som har ställts till oljebolag, ombordbefäl och inspektörer från Transportstyrelsen. Vid framställningen av resultatet transkriberade vi intervjuerna och delade sedan upp materialet i kategorier för att läsaren på ett enkelt sätt skall kunna följa studien från början till slut. / We have chosen to investigate if the oil companies requirements are higher than the Port States. During our time at sea as cadets we have been in contact with inspections several times. The crew members are often talking about these inspections and experience them as to critical. The results of our study has shown that the inspections are increasing the safety and awareness of risks that may exist. We will also describe wether there is a possibility to merge the inspections to reduce fatigue among the crew. To get an insight in the activities of the organisations we have decided to found our study based on interviews with oil companies, officers onboard and inspectors from Transportstyrelsen. During the process to complete the result we transcribed the interview material and divided it into categories, this will make it easier for the reader to follow our study.

This thesis focuses on algorithms solving the on-line Bin-Covering problem, when the items are generated from a known, stationary distribution. We introduce the Prospect Algorithm. The main idea behind the Prospect Algorithm is to use information on the item distribution to estimate how easy it will be to fill a bin with small overfill as a function of the empty space left in it. This estimate is then used to determine where to place the items, so that all active bins either stay easily fillable, or are finished with small overfill. We test the performance of the algorithm by simulation, and discuss how it can be modified to cope with additional constraints and extended to solve the Bin-Packing problem as well. The Prospect Algorithm is then adapted to achieve perfect packing, yielding a new version, the Prospect+ Algorithm, that is a slight but consistent improvement. Next, a Markov Decision Process formulation is used to obtain an optimal Bin-Covering algorithm to compare with the Prospect Algorithm. Even though the optimal algorithm can only be applied to limited (small) cases, it gives useful insights that lead to another modification of the Prospect Algorithm. We also discuss two relaxations of the on-line constraint, and describe how algorithms that are based on solving the Subset-Sum problem are used to tackle these relaxed problems. Finally, several practical issues encountered when using the Prospect Algorithm in the real-world are analyzed, a computationally efficient way of doing the background calculations needed for the Prospect Algorithm is described, and the three versions of the Prospect Algorithm developed in this thesis are compared.

published_or_final_version / Mechanical Engineering / Doctoral / Doctor of Philosophy