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This dissertation presents an automated detection algorithm that identifies severe external defects on the surfaces of barked hardwood logs and stems. The defects detected are at least 0.5 inch in height and at least 3 inches in diameter, which are severe, medium to large in size, and have external surface rises. Hundreds of real log defect samples were measured, photographed, and categorized to summarize the main defect features and to build a defect knowledge base. Three-dimensional laser-scanned range data capture the external log shapes and portray bark pattern, defective knobs, and depressions. The log data are extremely noisy, have missing data, and include severe outliers induced by loose bark that dangles from the log trunk. Because the circle model is nonlinear and presents both additive and non-additive errors, a new robust generalized M-estimator has been developed that is different from the ones proposed in the statistical literature for linear regression. Circle fitting is performed by standardizing the residuals via scale estimates calculated by means of projection statistics and incorporated in the Huber objective function to bound the influence of the outliers in the estimates. The projection statistics are based on 2-D radial-vector coordinates instead of the row vectors of the Jacobian matrix as proposed in the statistical literature dealing with linear regression. This approach proves effective in that it makes the GM-estimator to be influence bounded and thereby, robust against outliers. Severe defects are identified through the analysis of 3-D log data using decision rules obtained from analyzing the knowledge base. Contour curves are generated from radial distances, which are determined by robust 2-D circle fitting to the log-data cross sections. The algorithm detected 63 from a total of 68 severe defects. There were 10 non-defective regions falsely identified as defects. When these were calculated as areas, the algorithm locates 97.6% of the defect area, and falsely identifies 1.5% of the total clear area as defective. / Ph. D.

Yes / We propose a method to automatically parameterize noise in cardiac x-ray image sequences. The aim was to provide context-sensitive imaging information for use in regulating dose control feedback systems that relates to the experience of human observers. The algorithm locates and measures noise contained in areas of approximately equal signal level. A single noise metric is derived from the dominant noise components based on their magnitude and spatial location in relation to clinically relevant structures. The output of the algorithm was compared to noise and clinical acceptability ratings from 28 observers viewing 40 different cardiac x-ray imaging sequences. Results show good agreement and that the algorithm has the potential to augment existing control strategies to deliver x-ray dose to the patient on an individual basis. / This work has been performed in the project PANORAMA, funded by grants 335 from Belgium, Italy, France, the Netherlands, United Kingdom, and the ENIAC Joint Undertaking.

This research study examined the potential application of a color-based machine vision system under development at Virginia Tech for lumber processing in the furniture rough mill. The evaluation was done by conducting a yield study using 134 red oak boards. ROMI-RIP, a rip-first simulation program by Thomas (1995), was used to simulate yields for both the manually digitized lumber data and the machine vision scanned lumber data. The color-based machine vision system was evaluated by comparing the optimum yield obtainable when using lumber data derived from the automatic scanning system to: (1) observed yield from an existing state-of-the-art rip-first rough mill and (2) the optimum yield from manually digitized lumber data. Overall, the color-based machine vision system resulted in about 17 percent lower yield than was measured in the rough mill and 20 percent lower than the optimum, based on manually digitized lumber data. An analysis of the yield percentage point difference between the machine vision-based yields and optimal yields indicates: (1) approximately 11.5 yield points were lost due to errors in defect detection accuracy, (2) 7.3 yield points were lost due to errors in the machine vision material handling system, and (3) 1.3 yield points were lost due to data digitization and truncation errors. Since material handling, data digitization, and truncation problems are solvable with current technologies, future research should focus on developing systems that can improve the accuracy of feature recognition in lumber. / Master of Science

Quality inspections have seen varying degrees of automation depending on the complexity of the task and the environment. Especially in later phases of multi-stage manufacturing processes, such as final assembly in automotive industries, quality inspections are largely manual to this day. Today, emerging technologies offer both pressures and tools to increase automation. However, the current state of the research field is lacking in studies that help guide companies toward implementation. Thus, quality managers at final assembly for Scania's truck coachwork factory in Oskarshamn (MC) stipulated a thesis assignment to explore how inspections in their final assembly workshop could be automated. This assignment constitutes the purpose of this thesis project - to provide an exploratory study into existing and emerging technologies that enable automation of quality inspections at MC. This was eventually delimited to exploring automated visual inspection technologies. In order to better understand Scania's inspection and manufacturing system, a series of interviews and shadowings were undertaken with appropriate respondents. From these, we were able to extract seven inspection system requirements, most important were the ability to (1) handle high variability, (2) add new inspections fast, (3) inspect in direct flow and (4) inspect inside and outside of the truck coach without disassembly. Then, a thorough and comprehensive review of 559 active inspections allowed us to categorize and map the nature of inspections at MC. In our literature review, a model for a general quality inspections was found, which was used to guide and ground our proposals and recommendations as well as provided intuitive illustration. Further, two paradigms emerged as most interesting for this project: machine vision and deep learning. A theoretical comparison of the two suggested that the more traditional, rule-based machine vision algorithms would struggle in accommodating the requirements previously found. However, we could infer that deep learning would be highly suitable with respect to MC's requirements and inspections. A prototype deep learning inspection system gave further validation toward our speculations that deep learning offered the greatest potential for automation in complex environments such as MC's. Although this thesis was created for Scania as a primary customer, important theoretical and practical contributions were developed for a more general audience. Firstly, the exploration into new avenues for automation that overcome their traditional limitations were provided; something that is of high current import given the trends toward more complex manufacturing settings. Practically, we provide some guidance to industries that find themselves in similar situations to Scania - employing complex manufacturing systems or having complex products - where our findings can give insights in regards to modern automation challenges and solutions. / Kvalitetsinspektioner har automatiserats i variarande grad beroende på uppgiftens och omgivningens komlexitet. I synnerhet i de senare stadierna av flerstegsproduktioner, exempelvis slutmontering i fordonstillverkningsindustrin, består manuella inspektioner i stor utsträckning. Den snabba tekniska utvecklingen som har skett nyligen avger både ett tryck och skapar verktyg för att utöka automatiseringen. Dessvärre erbjuder dagslägets forskning föga stöd till företag gällande storskalig implementering av automatiserade kvalitetsinspektionssystem. Därför skapade kvalitetschefer på Scanias lastbilshyttmonteringsfabrik i Oskarshamn (MC) ett uppdrag att utforska hur deras inspektioner skulle kunna automatiseras. Detta uppdrag utgjorde syftet i vårt examensarbete: att utföra en explorativ studie inom befintliga och nya tekniker som möjliggör automatisering av MCs kvalitetsinpspektioner, vilket senare avgränsades till undersökandet av visuella kvalitetsinspektioner. För att tillgodogöra oss en djupare förståelse för Scanias inspektions- och produktionssytem utfördes en serie intervjuer och skuggningar med kunniga respondenter. Datan som erhölls utgjorde grunden i en nulägesanalys, från vilken sju systemkrav för ett inspektionssystem på MC kunde extraheras. De viktigaste av dessa var förmågan att (1) klara av hög variation, (2) addera nya inspektionspunkter snabbt, (3) kontrollera i direktflödet och (4) kontrollera innan- och utanför lastbilshytten. Vidare gjordes en omfattande genomgång av 559 aktiva inspektionspunkter vilket resulterade i en kategorisering och kartläggning av inspektioner på MC. I vår genomgång av relevant vetenskaplig litteratur hittades en generell modell för kvalitetskontroll som användes för att illustrera och teroretiskt förankra rekommendationer för ett automatiskt inspektionssystem. Vidare urskiljdes två intressanta områden i forskningen, machine vision och deep learning. En teoretisk jämförelse av traditionella regelbaserade machine vision algoritmer med deep learning erhöll att den förstnämnda är mindre lämpad för Scania med hänsyn till de krav som tagits fram. Deep learning å andra sidan, erbjuder många fördelar i relation till dessa. Genom en relativt enkel process kunde en deep learning baserad prototyp utvecklas. Prototypen påvisade goda resultat och gav vidare validering av vår spekulation att deep learning är ett lämpligt verktyg för automatisering i komplexa miljöer.Trots att detta examensarbete hade Scania som huvudsaklig uppdragsgivare så gjordes viktiga teoretiska och praktiska bidrag. En utforskning av i nya möjligheter för automatisering som kan överkomma begränsningarna av traditionell automatisering framtogs, vilket anses som både aktuellt och av vikt för samtiden där trender går mot mer dynamiska produktionssystem. Vad gäller praktiska bidrag så utgör denna rapport en sammanställning av råd till företag som befinner sig i liknande sitser som Scania - som använder komplexa produktionssystem eller har komplexa produkter - där våra resultat kan ge insikt gällande svårigheter och lösningar för modern automatisering.

Thesis (MScEng (Process Engineering))--University of Stellenbosch, 2010. / Thesis presented in partial fulfilment of the requirements for the degree MASTER OF SCIENCE IN ENGINEERING (EXTRACTIVE METALLURGICAL ENGINEERING) in the Department of Process Engineering at the University of Stellenbosch / ENGLISH ABSTRACT: Flotation is an important processing step in the mineral processing industry wherein valuable minerals are extracted. Flotation is a difficult process to control due to its complexity, meaning that the reversal of series of changes will not necessarily bring the process back to its original state. Expert knowledge is incorporated in flotation control through operator experience and intervention, which is subject to many challenges, creating the need for improvement in control. The performance of a flotation cell is often determined by evaluating froth appearance. The application of image analysis to capture, evaluate and monitor froth appearance poses multiple benefits such as consistent and reliable froth appearance evaluation. The objective for this study was to conduct a laboratory study for the collection of froth images with the purpose of evaluating the feasibility of using image information to predict platinum froth grade. Laboratory test work was performed according to a fractional factorial experimental design. Six variables were considered: air flowrate, pulp level and collector, activator, frother and depressant dosages. The laboratory study results were quantified by assay analysis. Analysis of variance only revealed the significant effect of pulp height and collector addition on flotation performance. Data pre-processing revealed information regarding feature correlations and variance contributions. Data analysis from captured images achieved reliable froth grade predictions using random forest classification and artificial neural network (ANN) regression techniques. Random forest classification accuracies of 86.8% and 75.5% were achieved for the following respective datasets: image data of each individual experiment (average of all experiments) and all image data. The applied ANN models achieved R2 values 0.943 and 0.828 for the same 2 datasets. An industrial case study was done wherein a series of step changes in air flowrate was made on a specific flotation cell. The limited industrial case study results supported laboratory study results. Multiple linear regression performed very well, reaching Rª values up to 0.964. Neural networks achieved slightly better with R2 values of up to 0.997. Based on the findings, the following main conclusions were drawn from this study: - Reliable predictions using classification and regression models on image data were proved possible in concept by the laboratory study, and supported by results from an industrial case study on a narrow system. The following main recommendations were made for further investigation: - Research over a larger range of operating conditions is needed to find a more comprehensive solution. - Investigations should be conducted to determine hardware requirements and specifications in terms of minimum resolution, lighting requirements, sampling frequency and data storage. Software requirements, specifications and maintenance challenges should also be investigated for implementation purposes once a more comprehensive solution has been found. - Strategies in terms of camera placement and model building will need to follow, giving special attention to a strategy to handle ore composition change. / AFRIKAANSE OPSOMMING: Flotasie is ‘n belangrike proses in die mineraal proseseringsbedryf vermoeid met die ontginning van waardevolle minerale. Die proses is moelik om te beheer vanweë sy kompleksiteit, wat verwys na die onvermoë om die proses terug te bring na sy oorspronklike toestand deur ‘n reeks veranderinge om te keer. In die algemeen word spesialis kennis deel van prosesbeheer deur die toepassing van operateurs se ervaring en ingryping, wat opsigself verskeie uitdagings bied wat die behoefte aan verbeterde beheertoestelle en strategieë daarstel. Die werkverrigting van flotasieselle word gereeld beoordeel op grond van die voorkoms van die skuim. Die gebruik van beeldverwerking om dié inligting vas te vang vir monitering en evaluering doeleindes hou verskeie voordele in, bv. konsikwente en betroubare evaluasie van die skuimvoorkoms. Die doelwitte vir hierdie studie was om ‘n laboratorium studie te loods vir die opname van skuimbeelde, met die doel om die bruikbaarheid van beeldinligting vir die voorspelling van die flotasieprodukkwaliteit, te ondersoek. Die laboratorium gevallestudie is uitgevoer aan die hand van ‘n fraksionele faktoriale eksperimentele ontwerp. Ses veranderlikes was ondersoek naamlik, lugvloeitempo, pulphoogte en versamelaar aktiveerder en depressant toevoeging. Die studie se resultate is gekwantifiseer deur die analise van die skuim inhoud. ‘n Analise van variansie het slegs die invloed van pulphoogte en versamelaartoevoeging op die flotasievertoning uitgelig. Data voorverwerking het inligting uitgelig rondom die veranderlikes se verhouding met mekaar. Data analise metodes, naamlik lukrake klassifiseringswoude en neurale netwerk regressie, is toegepas op die versamelde beelddata en het belowende resultate gelewer. Lukrake klassifiseringswoude het klasse gedentifiseer met akkuraathede van 86.8% en 75.5% vir die volgende onderskeie datastelle: individuele eksperimente se beeld data (gemiddeld oor alle eksperimentele lopies), alle beelddata as een stel. Die neurale netwerke het Rª waardes van 0.943 rn 0.828 gelewer vir dieselfde 2 datastelle. Die beperkte nywerheidsgevallestudie het verandering in lugvloeitempo toegelaat vir ‘n enkele flotasie sel. Die resultate het die bevindinge van die laboratorium gevallestudie gesteun. Veelvoudige lineere regressie het Rª waardes van tot en met 0.964 gelewer. Neurale netwerke het daarop verbeter met waardes tot en met 0.997. Die volgende hoof gevolgtrekkinge was duidelik vanuit die resultate: - Betroubare voorspellings was moontlik met die toepassing van klassifikasie en regressie modelle op die laboratorium studie data. Die resultate is ondersteun deur soortgelyke resultate van die beperkte nywerheidsgevallestudie. Die volgende hoof aanbevelings was gemaak vir verdere navorsing: - Navorsing oor ‘n wyer reeks proseskondisies is nodig om ‘n meer omvattende oplossing te vind. - ‘n Ondersoek moet geloods word om die hardeware vereistes en spesifikasies in terme van die minimum beeld resolusie, beligting vereistes, monsterneming tempo en die berging van data te bepaal. Sagteware vereistes, spesifikasies en instandhouding uitdagings moet ook ondersoek word vir implementasie doeleindes sodra ‘n meer omvattende oplossing gevind is. - Strategieë in verband met die plasing van kamers en die ontwikkeling van modelle is nodig, waarin spesiale aandag gegee moet word om die probleem van veranderende ertssamestelling op te los.

Mainly used for decorative purpose in the textile industry, lace is a type of lightweight, openwork fabric. The process of lace manufacturing is complex but much of it has been highly automated with the advancement of modern technology. One exception is the lace cutting operation which is used to cut the wide lace webs (as wide as 3.8 m) knitted from automatic knitting machines into individual lace breadths. Currently, lace cutting IS carried out by skilled operators or a low speed mechanical cutting system, leading to high cost and increased product lead times. Therefore the lace cutting operation has become a bottleneck of the whole process oflace manufacturing and its automation is highly desired. Based on the combination of machine vision and laser cutting technology, two automatic lace cutting systems have been developed in Loughborough University, which have fully demonstrated the feasibility of replacing the slow and expensive traditional lace cutting methods. However, the edge quality of the lace cut by these systems is not satisfactory enough to meet the requirements of demanding lace markets. In this thesis, based on the investigation of the effect of handling tension on lace cutting edge quality and the microstructure of lace, a strategic lace cutting solution has been presented. The cutting strategy is aimed at tensioning and exposing the loop thread by strategically tensioning and cutting individual threads. The loop thread is considered critical to cutting lace with a high quality finish. To automatically implement the cutting strategy, a machine vision system has been developed. An automatic lace transport and tensioning rig has been designed and manufactured. The long term aim of this rig is to be able to transport and tension lace continuously for lace cutting and apply localised tension on individual threads with the vision system providing feedback for tension control. The work in this thesis has been limited to manual adjustment of the rig to prove the initial ideas for this concept. An integrated vision guided, pulsed laser cutting system for lace cutting has been developed, based on which two types of representative lace have been cut. According to the assessment results of using a combination of user trials, microscopic and newly developed measurement techniques, the lace cut by this newly developed system has shown significant improvement in cutting edge quality, when compared to the lace cut by the previous laser cutting systems.