Tool wear inspection of polycrystalline cubic boron nitride inserts

In industry, highly frequent inspection of tooling used to machine safety critical components is common place. Worn or damaged tools produce undesirable surface finishes leading often to early failure of the part due to fatigue crack growth. In the development stages of polycrystalline boron nitride tools, the tool wear inspection technique is an off-line run-to-failure method. This approach interrupts the cutting process intermittently, to measure the tool wear using optical and scanning microscopy. This method is time consuming and expensive, causing bottlenecks in production. The overall aim in industry is to develop an on-line, automated system capable of informing the operator of the tool’s imminent failure. This paper focuses on treating this process as a preventative maintenance problem by studying whether acoustic emission can be used as an indirect measurement of tool wear at any given time. Acoustic emission measurements taken from the machining process of face turning are investigated here. Basic analysis in the frequency domain using principle component analysis reveals a number of interesting insights into the process. Relationships between the sharpness of the tool and the magnitude of the frequencies suggests promising link between acoustic emission and tool wear

3D Finite Element Modelling of Cutting Forces in Drilling Fibre Metal Laminates and Experimental Hole Quality Analysis

Machining Glass fibre aluminium reinforced epoxy (GLARE) is cumbersome due to distinctively different mechanical and thermal properties of its constituents, which makes it challenging to achieve damage-free holes with the acceptable surface quality. The proposed work focuses on the study of the machinability of thin (~2.5 mm) GLARE laminate. Drilling trials were conducted to analyse the effect of feed rate and spindle speed on the cutting forces and hole quality. The resulting hole quality metrics (surface roughness, hole size, circularity error, burr formation and delamination) were assessed using surface profilometry and optical scanning techniques. A three dimensional (3D) finite-element (FE) model of drilling GLARE laminate was also developed using ABAQUS/Explicit to help understand the mechanism of drilling GLARE. The homogenised ply-level response of GLARE laminate was considered in the FE model to predict cutting forces in the drilling process

An objective comparison of detection and segmentation algorithms for artefacts in clinical endoscopy

We present a comprehensive analysis of the submissions to the first edition of the Endoscopy Artefact Detection challenge (EAD). Using crowd-sourcing, this initiative is a step towards understanding the limitations of existing state-of-the-art computer vision methods applied to endoscopy and promoting the development of new approaches suitable for clinical translation. Endoscopy is a routine imaging technique for the detection, diagnosis and treatment of diseases in hollow-organs; the esophagus, stomach, colon, uterus and the bladder. However the nature of these organs prevent imaged tissues to be free of imaging artefacts such as bubbles, pixel saturation, organ specularity and debris, all of which pose substantial challenges for any quantitative analysis. Consequently, the potential for improved clinical outcomes through quantitative assessment of abnormal mucosal surface observed in endoscopy videos is presently not realized accurately. The EAD challenge promotes awareness of and addresses this key bottleneck problem by investigating methods that can accurately classify, localize and segment artefacts in endoscopy frames as critical prerequisite tasks. Using a diverse curated multi-institutional, multi-modality, multi-organ dataset of video frames, the accuracy and performance of 23 algorithms were objectively ranked for artefact detection and segmentation. The ability of methods to generalize to unseen datasets was also evaluated. The best performing methods (top 15%) propose deep learning strategies to reconcile variabilities in artefact appearance with respect to size, modality, occurrence and organ type. However, no single method outperformed across all tasks. Detailed analyses reveal the shortcomings of current training strategies and highlight the need for developing new optimal metrics to accurately quantify the clinical applicability of methods

New Link Functions for Distribution–Specific Quantile Regression Based on Vector Generalized Linear and Additive Models

In the usual quantile regression setting, the distribution of the response given the explanatory variables is unspecified. In this work, the distribution is specified and we introduce new link functions to directly model specified quantiles of seven 1–parameter continuous distributions. Using the vector generalized linear and additive model (VGLM/VGAM) framework, we transform certain prespecified quantiles to become linear or additive predictors. Our parametric quantile regression approach adopts VGLMs/VGAMs because they can handle multiple linear predictors and encompass many distributions beyond the exponential family. Coupled with the ability to fit smoothers, the underlying strong assumption of the distribution can be relaxed so as to offer a semiparametric–type analysis. By allowing multiple linear and additive predictors simultaneously, the quantile crossing problem can be avoided by enforcing parallelism constraint matrices. This article gives details of a software implementation called the VGAMextra package for R. Both the data and recently developed software used in this paper are freely downloadable from the internet

Qualitative Study of Pediatric Early Warning Systems\u27 Impact on Interdisciplinary Communication in Two Pediatric Oncology Hospitals With Varying Resources

PURPOSE: Hospitalized pediatric oncology patients are at high risk of deterioration and require frequent interdisciplinary communication to deliver high-quality care. Pediatric early warning systems (PEWS) are used by hospitals to reduce deterioration, but it is unknown how these systems affect communication about patient care in high- and limited-resource pediatric oncology settings. METHODS: This qualitative study included semistructured interviews describing PEWS and subsequent team communication at 2 pediatric cancer centers, 1 in the United States and 1 in Guatemala. Participants included nurses, and frontline and intensive care providers who experienced recent deterioration events. Transcripts were coded and analyzed inductively using MAXQDA software. RESULTS: The study included 41 providers in Guatemala and 42 providers in the United States (33 nurses, 30 ward providers, and 20 pediatric intensive care providers). Major themes identified include hierarchy, empowerment, quality and method of communication, and trigger. All providers described underlying medical hierarchies affecting the quality of communication regarding patient deterioration events and identified PEWS as empowering. Participants from the United States described the algorithmic approach to care and technology associated with PEWS contributing to impaired clinical judgement and a lack of communication. In both settings, PEWS sparked interdisciplinary communication and inspired action. CONCLUSION: PEWS enhance interdisciplinary communication in high- and limited-resource study settings by empowering bedside providers. Traditional hierarchies contributed to negative communication and, in well-resourced settings, technology and automation resulted in lack of communication. Understanding contextual elements is integral to optimizing PEWS and improving pediatric oncology outcomes in hospitals of all resource levels