5,251 research outputs found

    Learning to classify software defects from crowds: a novel approach

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    In software engineering, associating each reported defect with a cate- gory allows, among many other things, for the appropriate allocation of resources. Although this classification task can be automated using stan- dard machine learning techniques, the categorization of defects for model training requires expert knowledge, which is not always available. To cir- cumvent this dependency, we propose to apply the learning from crowds paradigm, where training categories are obtained from multiple non-expert annotators (and so may be incomplete, noisy or erroneous) and, dealing with this subjective class information, classifiers are efficiently learnt. To illustrate our proposal, we present two real applications of the IBM’s or- thogonal defect classification working on the issue tracking systems from two different real domains. Bayesian network classifiers learnt using two state-of-the-art methodologies from data labeled by a crowd of annotators are used to predict the category (impact) of reported software defects. The considered methodologies show enhanced performance regarding the straightforward solution (majority voting) according to different metrics. This shows the possibilities of using non-expert knowledge aggregation techniques when expert knowledge is unavailable

    Automatic Change-based Diagnosis of Structures Using Spatiotemporal Data and As- Designed Model

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    abstract: Civil infrastructures undergo frequent spatial changes such as deviations between as-designed model and as-is condition, rigid body motions of the structure, and deformations of individual elements of the structure, etc. These spatial changes can occur during the design phase, the construction phase, or during the service life of a structure. Inability to accurately detect and analyze the impact of such changes may miss opportunities for early detections of pending structural integrity and stability issues. Commercial Building Information Modeling (BIM) tools could hardly track differences between as-designed and as-built conditions as they mainly focus on design changes and rely on project managers to manually update and analyze the impact of field changes on the project performance. Structural engineers collect detailed onsite data of a civil infrastructure to perform manual updates of the model for structural analysis, but such approach tends to become tedious and complicated while handling large civil infrastructures. Previous studies started collecting detailed geometric data generated by 3D laser scanners for defect detection and geometric change analysis of structures. However, previous studies have not yet systematically examined methods for exploring the correlation between the detected geometric changes and their relation to the behaviors of the structural system. Manually checking every possible loading combination leading to the observed geometric change is tedious and sometimes error-prone. The work presented in this dissertation develops a spatial change analysis framework that utilizes spatiotemporal data collected using 3D laser scanning technology and the as-designed models of the structures to automatically detect, classify, and correlate the spatial changes of a structure. The change detection part of the developed framework is computationally efficient and can automatically detect spatial changes between as-designed model and as-built data or between two sets of as-built data collected using 3D laser scanning technology. Then a spatial change classification algorithm automatically classifies the detected spatial changes as global (rigid body motion) and local deformations (tension, compression). Finally, a change correlation technique utilizes a qualitative shape-based reasoning approach for identifying correlated deformations of structure elements connected at joints that contradicts the joint equilibrium. Those contradicting deformations can help to eliminate improbable loading combinations therefore guiding the loading path analysis of the structure.Dissertation/ThesisDoctoral Dissertation Civil and Environmental Engineering 201

    Surface defect machining : a new approach for hard turning

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    Hard turning is emerging as a key technology to substitute conventional grinding processes, mainly on account of lower equipment cost, short setup time, and a reduced number of process steps. This is, however, being impeded by a number of challenges required to be resolved, including attainable surface roughness, surface deteriorations, surface residual stresses and metallurgical transformations on the machined steel surface (white layer). In this thesis, a novel approach named Surface Defect Machining (SDM) is proposed as a viable solution to resolve a large number of these issues and to improve surface finish and surface integrity. SDM is defined as a process of machining, where a workpiece is first subjected to surface defects creation at a depth less than the uncut chip thickness; either through mechanical and/or thermal means; then followed by a normal machining operation so as to reduce the cutting resistance. A comprehensive understanding of SDM is established theoretically using finite element method (FEM). Also, an experimental study has been carried out for extensive understanding of the new technique. A good agreement between theoretical and experimental investigations has been achieved. The results show very interesting salient features of SDM, providing favourable machining outcomes. These include: reduced shear plane angle, reduced machining forces, lower residual stresses on the machined surface, reduced tool-chip interface contact length and increased chip flow velocity, as well as reductions in overall temperature in the cutting zone and changing the mechanism of chip morphology from jagged to discontinuous. However, the most prominent outcome is the improved attainable surface roughness. Furthermore, SDM shows the ability to exceed the critical feed rate and achieve an optical surface finish upto 30 nm. A scientific explanation of the improved surface roughness suggests that during SDM, a combination of both the cutting action and the rough polishing action help to improve the machined surface. Based on these findings, it is anticipated that a component machined using the SDM method should exhibit improved quality of the machined surface, which is expected to provide tremendous commercial advantages in the time to come

    Unveiling the prospects of point-of-care 3D printing of Polyetheretherketone (PEEK) patient-specific implants

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    Additive manufacturing (AM) or three-dimensional (3D) printing is rapidly gaining acceptance in the healthcare sector. With the availability of low-cost desktop 3D printers and inexpensive materials, in-hospital or point-of-care (POC) manufacturing has gained considerable attention in personalized medicine. Material extrusion-based [Fused Filament Fabrication (FFF)] 3D printing of low-temperature thermoplastic polymer is the most commonly used 3D printing technology in hospitals due to its ease of operability and availability of low-cost machines. However, this technology has been limited to the production of anatomical biomodels, surgical guides, and prosthetic aids and has not yet been adopted into the mainstream production of patient-specific or customized implants. Polyetheretherketone (PEEK), a high-performance thermoplastic polymer, has been used mainly in reconstructive surgeries as a reliable alternative to other alloplastic materials to fabricate customized implants. With advancements in AM systems, prospects for customized 3D printed surgical implants have emerged, increasing attention for POC manufacturing. A customized implant may be manufactured within few hours using 3D printing, allowing hospitals to become manufacturers. However, manufacturing customized implants in a hospital environment is challenging due to the number of actions necessary to design and fabricate the implants. The focus of this thesis relies on material extrusion-based 3D printing of PEEK patient-specific implants (PSIs). The ambitious challenge was to bridge the performance gap between 3D printing of PEEK PSIs for reconstructive surgery and the clinical applicability at the POC by taking advantage of recent developments in AM systems. The main reached milestones of this project include: (i) assessment of the fabrication feasibility of PEEK surgical implants using material extrusion-based 3D printing technology, (ii) incorporation of a digital clinical workflow for POC manufacturing, (iii) assessment of the clinical applicability of the POC manufactured patient-specific PEEK scaphoid prosthesis, (iv) visualization and quantification of the clinical reliability of the POC manufactured patient-specific PEEK cranial implants, and (v) assessment of the clinical performance of the POC manufactured porous patient-specific PEEK orbital implants. During this research work, under the first study, we could demonstrate the prospects of FFF 3D printing technology for POC PEEK implant manufacturing. It was established that FFF 3D printing of PEEK allows the construction of complex anatomical geometries which cannot be manufactured using other technologies. With a clinical digital workflow implementation at the POC, we could further illustrate a smoother integration and faster implant production (within two hours) potential for a complex-shaped, patented PEEK patient-specific scaphoid prosthesis. Our results revealed some key challenges during the FFF printing process, exploring the applicability of POC manufactured FFF 3D printed PEEK customized implants in craniofacial reconstructions. It was demonstrated that optimal heat distribution around the cranial implants and heat management during the printing process are essential parameters that affect crystallinity, and thus the quality of the FFF 3D printed PEEK cranial implants. At this stage of the investigation, it was observed that the root mean square (RMS) values for dimensional accuracy revealed higher deviations in large-sized cranial prostheses with “horizontal lines” characteristics. Further optimization of the 3D printer, a layer-by-layer increment in the airflow temperature was done, which improved the performance of the FFF PEEK printing process for large-sized cranial implants. We then evaluated the potential clinical reliability of the POC manufactured 3D printed PEEK PSIs for cranial reconstruction by quantitative assessment of geometric, morphological, and biomechanical characteristics. It was noticed that the 3D printed customized cranial implants had high dimensional accuracy and repeatability, displaying clinically acceptable morphologic similarity concerning fit and contours continuity. However, the tested cranial implants had variable peak load values with discrete fracture patterns from a biomechanical standpoint. The implants with the highest peak load had a strong bonding with uniform PEEK fusion and interlayer connectivity, while air gaps and infill fusion lines were observed in implants with the lowest strength. The results of this preclinical study were in line with the clinical applicability of cranial implants; however, the biomechanical attribute can be further improved. It was noticed that each patient-specific reconstructive implant required a different set of manufacturing parameters. This was ascertained by manufacturing a porous PEEK patient-specific orbital implant. We evaluated the FFF 3D printed PEEK orbital mesh customized implants with a metric considering the design variants, biomechanical, and morphological parameters. We then studied the performance of the implants as a function of varying thicknesses and porous design constructs through a finite element (FE) based computational model and a decision matrix based statistical approach. The maximum stress values achieved in our results predicted the high durability of the implants. In all the implant profile configurations, the maximum deformation values were under one-tenth of a millimeter (mm) domain. The circular patterned design variant implant revealed the best performance score. The study further demonstrated that compounding multi-design computational analysis with 3D printing can be beneficial for the optimal restoration of the orbital floor. In the framework of the current thesis, the potential clinical application of material extrusion-based 3D printing for PEEK customized implants at the POC was demonstrated. We implemented clinical experience and engineering principles to generate a technical roadmap from preoperative medical imaging datasets to virtual surgical planning, computer-aided design models of various reconstructive implant variants, to the fabrication of PEEK PSIs using FFF 3D printing technology. The integration of 3D printing PEEK implants at the POC entails numerous benefits, including a collaborative team approach, quicker turnaround time of customized implants, support in pre-surgical and intraoperative planning, improved patient outcomes, and decreased overall healthcare cost. We believe that FFF 3D printing of customized PEEK implants could become an integral part of the hospitals and holds potential for various reconstructive surgery applications
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