Virtual manufacturing: prediction of work piece geometric quality by considering machine and set-up

Lien vers la version éditeur: http://www.tandfonline.com/doi/full/10.1080/0951192X.2011.569952#.U4yZIHeqP3UIn the context of concurrent engineering, the design of the parts, the production planning and the manufacturing facility must be considered simultaneously. The design and development cycle can thus be reduced as manufacturing constraints are taken into account as early as possible. Thus, the design phase takes into account the manufacturing constraints as the customer requirements; more these constraints must not restrict the creativity of design. Also to facilitate the choice of the most suitable system for a specific process, Virtual Manufacturing is supplemented with developments of numerical computations (Altintas et al. 2005, Bianchi et al. 1996) in order to compare at low cost several solutions developed with several hypothesis without manufacturing of prototypes. In this context, the authors want to predict the work piece geometric more accurately by considering machine defects and work piece set-up, through the use of process simulation. A particular case study based on a 3 axis milling machine will be used here to illustrate the authors’ point of view. This study focuses on the following geometric defects: machine geometric errors, work piece positioning errors due to fixture system and part accuracy

Symmetry enriched U(1) quantum spin liquids

We classify and characterize three dimensional $U(1)$ quantum spin liquids (deconfined $U(1)$ gauge theories) with global symmetries. These spin liquids have an emergent gapless photon and emergent electric/magnetic excitations (which we assume are gapped). We first discuss in great detail the case with time reversal and $SO(3)$ spin rotational symmetries. We find there are 15 distinct such quantum spin liquids based on the properties of bulk excitations. We show how to interpret them as gauged symmetry-protected topological states (SPTs). Some of these states possess fractional response to an external $SO(3)$ gauge field, due to which we dub them "fractional topological paramagnets". We identify 11 other anomalous states that can be grouped into 3 anomaly classes. The classification is further refined by weakly coupling these quantum spin liquids to bosonic Symmetry Protected Topological (SPT) phases with the same symmetry. This refinement does not modify the bulk excitation structure but modifies universal surface properties. Taking this refinement into account, we find there are 168 distinct such $U(1)$ quantum spin liquids. After this warm-up we provide a general framework to classify symmetry enriched $U(1)$ quantum spin liquids for a large class of symmetries. As a more complex example, we discuss $U(1)$ quantum spin liquids with time reversal and $Z_2$ symmetries in detail. Based on the properties of the bulk excitations, we find there are 38 distinct such spin liquids that are anomaly-free. There are also 37 anomalous $U(1)$ quantum spin liquids with this symmetry. Finally, we briefly discuss the classification of $U(1)$ quantum spin liquids enriched by some other symmetries.Comment: 24 pages + appendices + reference

Minimising defect formation in sand casting of sheet lead: a DoE approach

Sand casting of lead sheet is a traditional manufacturing process used up to the present due to the special features of sand cast sheet such as their attractive sheen. Similarly to any casting process, sand casting of lead sheet suffers from the presence of surface defects. In this study, a surface defect type, hereby referred to as ‘grooves’, has been investigated. The focus has been laid on the identification of the main factors affecting defect formation in this process. Based on a set of screening experiments performed using Scanning Electron Microscopy (SEM) as well as the existing literature, a number of factors affecting the formation of such defects was identified and their corresponding significance was estimated using the Analysis of Variance (ANOVA) technique. The obtained results suggest that the most significant factor affecting defect formation in sand casting of lead sheet is the composition of the moulding mixture. Defect formation was also proven to be dependent on the sand grain fineness, the quality of the melt and some of the interactions between the aforementioned process parameters. Finally, an optimal set of process parameters leading to the minimisation of surface defects was identified

Computer Vision Based Robotic Polishing Using Artificial Neural Networks

Polishing is a highly skilled manufacturing process with a lot of constraints and interaction with environment. In general, the purpose of polishing is to get the uniform surface roughness distributed evenly throughout part’s surface. In order to reduce the polishing time and cope with the shortage of skilled workers, robotic polishing technology has been investigated. This paper studies about vision system to measure surface defects that have been characterized to some level of surface roughness. The surface defects data have learned using artificial neural networks to give a decision in order to move the actuator of arm robot. Force and rotation time have chosen as output parameters of artificial neural networks. Results shows that although there is a considerable change in both parameter values acquired from vision data compared to real data, it is still possible to obtain surface defects characterization using vision sensor to a certain limit of accuracy. The overall results of this research would encourage further developments in this area to achieve robust computer vision based surface measurement systems for industrial robotic, especially in polishing proces

An investigation into non-destructive testing strategies and in-situ surface finish improvement for direct metal printing with SS 17-4 PH : a thesis presented in partial fulfilment of the requirements for the degree of Doctor of Philosophy in Engineering at Massey University, Albany, New Zealand

Figure 1.1 is re-used under an Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0) licenceAdditive Manufacturing (AM) technologies have the potential to create complex geometric parts that can be used in high-end product industries, aerospace, automotive, medical etc. However, the surface finish, part-to-part reliability, and machine-to-machine reliability has made it difficult to qualify the process for load dependent structures. The improvement of surface finish on metal printed parts, is a widely sought solution by these high-end industries and non-destructively characterizing the mechanical aptitude of metal printed parts, would pave the way for quality assessment strategies used to certify additively manufactured parts. This thesis examines the capability of laser polishing and non-destructive testing technologies and methods to address these difficulties. This research study presents an investigation into quality management strategies for Direct Metal Printing (DMP) with powdered Stainless Steel 17-4 PH. The research aim is split into two key categories: to improve the surface finish of metal additive manufactured parts and to non-destructively characterize the impact of defects (metallurgical anomalies) on the mechanical properties of the printed part. To improve surface finish of a printed part, a novel methodology was tested to laser polish the Laser-Powder Bed Fusion (L-PBF) parts during print with the built-in laser. Numerous technologies for non-destructive testing techniques already exist, and in the duration of this doctoral study various technologies were explored. However, the final solution focuses on layer-wise capture with a versatile low-cost imaging system, retrofitted within the DMP machine, to capture each layer following the lasering process. In addition, the study also focuses on progressing the characterization of data (images), using a combination of image processing, 3D modelling and Finite-Element-Analysis to create a novel strategy for replicating the as-built specimen as a computer-aided design model and performing simulated fatigue failure analysis on the part. This thesis begins with a broadened justification of the research need for the solutions described, followed by a review of literature defining existing techniques and methods pertaining to the solutions, with validation of the research gap identified to provide novel contribution to the metal additive manufacturing space. This is followed by the methodologies developed, to firstly, control the laser parameters within the DMP and examine the influence of these parameters using surface profilometry, scanning electron microscopy and mechanical hardness testing. The control variables in this methodology combines laser parameters (laser power, scan speed and polishing iterations) and print orientation (polished surface angled at 0º, 20º, 40º, 60º, 80º and 90º degree increments from the laser), using several Taguchi designs of experiments and statistical analysis to characterize the experimental results. The second methodology describes the retrofitted imaging system, image processing techniques and analysis methods used to reconstruct the 3D model of a standard square shaped part and one with synthesized defects. The method explores various 2D to 3D extrusion-based techniques using a combination of code-based image processing (Python 3, OpenCV and MATLAB image processing toolbox) and ready-made software tools (Solidworks, InkTrace, ImageJ and more). Finally, the new research findings are presented, including the results of the laser polishing study demonstrating the successful improvement of surface finish. The discussion surrounding these results, highlights the most effective part orientation for laser polishing the outline of an AM part and the most effective laser parameter combination resulting in the most significant improvement to surface finish (roughness and profile height variation). Summarily, the best improvement in surface roughness was achieved with the <80 angled surface with the laser speed, laser power and polishing iterations set to 500mm/s, 30W, 3 respectively. The sample set total average measured a 16.7% decrease in Ra. NDT digital imaging, thermal imaging and acoustic technologies were considered for defect capture in metal AM parts. The solution presented is primarily focused on the expansion of research to process digital images of each part layer and examine strategies to move the research from a data capture stage to a data processing strategy with quantitative measurement (FEA analysis) of the printed part’s mechanical properties. In addition, the results discuss a method to create feedback to the DMP to selectively melt problematic areas, by re-creating the sliced part layers but removing the well-melted areas from the laser scanning pattern. The methods and technological solutions developed in this research study, have presented novel data to further research these methods in the pursuit of quality assurance for AM parts. The work done has paved the way for more the research opportunities and alternative methods to be explored that complement the methods detailed here. For example, using a combination of in-situ laser polishing, followed by post-processing the AM specimens in an acid-based chemical bath. Alternatively, further exploring acoustic NDT techniques to create an in-built acoustic-based imaging device within the AM machine. Finally, this thesis cross-examines the work done to answer the research questions established at the start of the thesis and verify the hypotheses stated in the methods chapter

A THREE DIMENSIONAL (3D) VISION BASED DEFECT INSPECTION SYSTEM FOR GLUING APPLICATION

A Robot Vision System (RVS) is an adaptive and dynamic system that caters to a wide range of jobs where each involves a set of operations required to be done at a predetermined workstation. This research is focused on the development of a vision system to be integrated with KUKA arm robot. Pyramid object is used as a complimentary of the windscreen car as a model. It developed using plain cardboard with dimension of 15cm x 15cm. 2D matching application introduced to identify the characteristic of the object used in the system using CCD camera. Object used must be trained in training phase to create object template and used again in recognition phase for object classification. Then, two CCD cameras are used; placed at the top and front of the object to extract object’s edge location using Harris Point. Data extracted from it are used to find 3D coordination of each edge. Equation of straight line mostly used in this method to identify x, y and z coordinates. Data obtained from the system then used to give instruction to KUKA arm robot for gluing purposes. Pixel coordinates must be converted to robot coordinates for easier understanding by the robot. Three types of defect are trained as model templates and save to the memory known as bumper, gap and bubble defect. Each defect has special characteristic. Inspection system developed to identify problems occurs in gluing process. Template matching method used to call model trained in training phase to identify the uncertainties. Each defect occurs comes with its coordinate’s information for correction. Correction of defect consists of two phase; 1st CoD where correction is completed in first time and 2nd CoD where correction still need to be completed after the first correction. Data for all the process are recorded to prove that this algorithm made improvement with the previous research

3D inspection methods for specular or partially specular surfaces

Deflectometric techniques are a powerful tool for the automated quality control of specular or shiny surfaces. These techniques are based on using a camera to observe a reference pattern reflected on the surface under inspection, exploiting the dependence of specular reflections on surface normals to recover shape information from the acquired images. Although deflectometry is already used in industrial environments such as the quality control of lenses or car bodies, there are still some open problems. On the one hand, using quantitative deflectometry, the normal vector field and the 3D shape of a surface can be obtained, but these techniques do not yet take full advantage of their local sensitivity because the achieved global accuracies are affected by calibration errors. On the other hand, qualitative deflectometry is used to detect surface imperfections without absolute measurements, exploiting the local sensitivity of deflectometric recordings with reduced calibration requirements. However, this qualitative approach requires further processing that can involve a considerable engineering effort, particularly for aesthetic defects which are inherently subjective. The first part of this thesis aims to contribute to a better understanding of how deflectometric setups and their calibration errors affect quantitative measurements. Different error sources are considered including the camera calibration uncertainty and several non-ideal characteristics of LCD screens used to generate the light patterns. Experiments performed using real measurements and simulations show that the non-planarity of the LCD screen and the camera calibration are the dominant sources of error. The second part of the thesis investigates the use of deep learning to identify geometrical imperfections and texture defects based on deflectometric data. Two different approaches are explored to extract and combine photometric and geometric information using convolutional neural network architectures: one for automated classification of defective samples, and another one for automated segmentation of defective regions in a sample. The experimental results in a real industrial case study indicate that both architectures are able to learn relevant features from deflectometric data, enabling the classification and segmentation of defects based on a dataset of user-provided examples.Teknika deflektometrikoak tresna baliotsuak dira gainazal espekular edo distiratsuen kalitate kontrol automatikoa gauzatzeko. Teknika hauetan, kamera bat erabiltzen da ikuskatu beharreko gainazalean islatutako erreferentziazko patroi bat behatzeko, eta isladapen espekularrek gainazalen bektore normalengan duten menpekotasuna ustiatzen dute irudietatik informazio geometrikoa berreskuratzeko. Zenbait industria-aplikaziotan deflektometria jada erabiltzen bada ere –adibidez, betaurrekoen edo autoen karrozerien kalitate kontrolean-, oraindik badaude hobetu beharreko hainbat esparru. Batetik, deflektometria kuantitatiboak aukera ematen du gainazal baten bektore-eremu normala eta 3D forma lortzeko, baina gaur egun teknika hauek ez dute beren sentsibilitate lokal guztia aprobetxatzen kalibrazio-akatsek zehaztasun globalean duten eraginagatik. Bestetik, deflektometria kualitatiboa neurketa absoluturik egin gabe gainazal akatsak antzemateko erabili daiteke, kalibrazio-eskakizun murriztuekin sentsibilitate lokala ustiatuz. Hala ere, teknika horiek algoritmoen garapenean esfortzu handia ekar dezakeen prozesamendu bat eskatzen dute, bereziki bere baitan subjektiboak diren akats estetikoetarako. Hala ere, teknika horiek algoritmoen garapenean esfortzu handia ekar dezakeen prozesamendu bat eskatzen dute, bereziki bere baitan subjektiboak diren akats estetikoetarako. Tesi honen lehen zatiaren helburua adkizizio sistema osatzen duten gailuek eta horien kalibrazioek neurketa kuantitatiboei nola eragiten dieten hobeto ulertzen laguntzea da. Hainbat errore-iturri hartzen dira kontuan, besteak beste kameraren kalibrazioaren ziurgabetasuna, eta argi-patroiak sortzeko erabilitako LCD pantailen zenbait ezaugarri ez-ideal. Neurketa errealetan eta simulazioetan egindako esperimentuek erakusten dute LCD pantailaren deformazioak eta kameraren kalibrazioak eragindako erroreak direla neurketen akats eta ziurgabetasun iturri nagusiak. Tesiaren bigarren zatian, datu deflektometrikoetatik abiatuz, inperfekzio geometrikoak eta testura-akatsak identifikatzeko ikaskuntza sakoneko metodoen erabilera ikertzen da. Helburu honekin, irudietatik informazio fotometrikoa eta geometrikoa atera eta konbinatzen duten sare neuronal konboluzionaletan oinarritutako bi arkitektura proposatzen dira: bata, lagin akastunak automatikoki sailkatzeko; eta, bestea, laginetako eremu akastunak automatikoki segmentatzeko. Automobilgintza industriako kasu praktiko baten lortutako emaitzek erakusten dute erabilitako arkitekturek datu deflektometrikoetatik ezaugarri esanguratsuak ikas ditzaketela, erabiltzaileak emandako adibide multzo batean oinarrituta gainazal akatsak sailkatu eta segmentatzea ahalbidetuz.Las técnicas deflectométricas son una herramienta valiosa para automatizar el control de calidad de superficies especulares o reflectantes. Estas técnicas se basan en el uso de una cámara para observar un patrón de referencia reflejado en la superficie bajo inspección, explotando la dependencia de los reflejos especulares en la normal de la superficie para recuperar información geométrica a partir de las imágenes adquiridas. Aunque la deflectometría ya se usa en algunas aplicaciones industriales, tales como el control de calidad de lentes o carrocerías de coches, todavía hay algunos problemas abiertos. Por un lado, la deflectometría cuantitativa permite obtener el campo vectorial normal y la forma 3D de una superficie, pero a día de hoy no es capaz de aprovechar al máximo su sensibilidad local ya que la precisión global se ve afectada por errores de calibración. Por otro lado, la deflectometría cualitativa se utiliza para detectar imperfecciones de la superficie sin mediciones absolutas, explotando la sensibilidad local de la deflectometría con requisitos de calibración reducidos. Sin embargo, estos métodos requieren un procesamiento adicional que puede implicar un esfuerzo considerable en el desarrollo de algoritmos, particularmente para defectos estéticos que son inherentemente subjetivos. La primera parte de esta tesis tiene como objetivo contribuir a una mejor comprensión de cómo el sistema de adquisición y su calibración afectan a las mediciones cuantitativas. Se consideran dife-rentes fuentes de error, incluida la incertidumbre de calibración de la cámara y varias características no ideales de las pantallas LCD utilizadas para generar los patrones de luz. Los experimentos realizados con mediciones reales y simulaciones indican que los errores inducidos por la deformación de la pantalla LCD y la calibración de la cámara son las principales fuentes de error e incertidumbre. La segunda parte de la tesis investiga el uso del aprendizaje profundo para identificar imperfecciones geométricas y defectos de textura a partir de datos deflectométricos. Se adoptan dos enfoques diferentes para extraer y combinar información fotométrica y geométrica utilizando sendas arquitecturas basadas en redes neuronales convolucionales: una para la clasificación automatizada de muestras defectuosas y otra para la segmentación automatizada de regiones defectuosas en una muestra. Los resultados experimentales en un caso de estudio industrial real indican que ambas arquitecturas pueden aprender características relevantes de los datos deflectométricos, permitiendo la clasificación y segmentación de defectos en base a un conjunto de datos de ejemplos proporcionados por el usuario

Methods of assessing structural integrity for space shuttle vehicles

A detailed description and evaluation of nondestructive evaluation (NDE) methods are given which have application to space shuttle vehicles. Appropriate NDE design data is presented in twelve specifications in an appendix. Recommendations for NDE development work for the space shuttle program are presented

In-situ monitoring of laser powder bed fusion applied to defect detection

Additive manufacturing technologies, particularly laser powder bed fusion (LPBF), have received much attention recently due to their numerous advantages over conventional manufacturing methods. However, the use of LPBF is still quite restricted, mainly due to two factors: its typically low productivity, which makes the technology less competitive in applications with moderate to high production volumes, and its limited reliability, particularly relevant for applications where high performance is required from the materials.The issue of low productivity is addressed in this thesis by adjusting the main LPBF process parameters. An equation for the build rate was formulated based on these parameters, determining their contributions and enabling strategies for build rate maximization. The changes in microstructure and defect populations associated with increasing productivity were determined.The reliability issue was explored by investigating defect formation, detectability and mitigation, since a major factor compromising reliability and materials’ performance is the presence of defects. Internal defects were deliberately created in LPBF-manufactured material to assess their detectability via in-situ monitoring. Two main routes of deliberate defect formation have been identified while preserving defect formation mechanisms; therefore, this thesis can be divided into two parts according to the approach employed to create defects.Defects are generated systematically if suboptimal process parameters are employed. The types, quantities, and sizes of defects in nickel-based alloy Hastelloy X resulting from varying processing conditions were thoroughly characterized. Analyzing data obtained from in-situ monitoring made it possible to distinguish virtually defect-free material from defective material.Defects are generated stochastically due to the redeposition of process by-products on the powder bed. With the aid of in-situ monitoring data, the presence of these defects can be inferred from the detection of the process by-products responsible for their formation. The comparison of data obtained in-situ with data obtained through ex-situ material characterization allowed determining how precisely detections corresponded to actual defects. The impact of these defects on the mechanical properties of Hastelloy X was assessed. A couple of in-process mitigation strategies were investigated, and their performances were evaluated. By establishing means to use LPBF process monitoring to distinguish high-quality from defective material and detect random, unavoidable defects, this thesis enables the prediction of LPBF material quality. It creates conditions necessary for the first-time-right production of defect-free material at increased build rates