Diamond impregnated tools are considered which are used to machine concrete. During their application, the bonding as well as the diamonds need to wear down in a certain way to gain a sharp tool. ...This required wear is called self-sharpening and means a continuous exposure of new diamonds. Within the development phase of diamond tools, time and cost intensive testing is necessary for the assessment of the tool performance. Hence, an extrapolation based on a minimal amount of testing is desirable to forecast the tool lifetime. A further reduction of the development and testing cost can be achieved by reducing the data needed to forecast the tool performance. Within this paper, the development of a statistical model is shown which was used to forecast the lifetime of the single diamonds on the tool. The statistical analysis is based on single segment tests which were carried out with different segment specification. During the tests, the exposed and broken out diamonds were counted to serve as the necessary input data for the statistical analysis. The counting of the diamonds on the segment was done in two different ways: based on the 2-dimensional microscopic pictures made after every minute of drilling and based on the 3-dimensional surface measurements made after every 5 min of drilling. It turns out that these two approaches of the wear analysis provide similar results.
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•Tool wear analysis based on single segment tests with varying segment specification.•Two approaches to measure tool wear: 2D microscopic picture vs 3D surface measurement.•Development of a statistic model to forecast expected lifetime of single diamonds.•Essentially, no difference in statistical results based on 2- and 3-dimensional data.•The length of inspection intervals can be extended by factor 5 for used “segment/concrete”-combinations.
Paediatric obstructive sleep disordered breathing (OSDB) has a considerable impact on cardiovascular physiology, but the consequences on children's basal metabolism and response to exercise are far ...from being known. The objective was to propose model estimations for paediatric OSDB metabolism at rest and during exercise. A retrospective case-control analysis of data from children submitted to otorhinolaryngology surgery was performed. The heart rate (HR) was measured, while oxygen consumption (VO
) and energy expenditure (EE) at rest and during exercise were obtained using predictive equations. The results for the patients with OSDB were compared with controls. A total of 1256 children were included. A total of 449 (35.7%) had OSDB. The patients with OSDB showed a significantly higher resting heart rate (94.55 ± 15.061 bpm in OSDB vs. 92.41 ± 15.332 bpm in no-OSDB, p = 0.041). The children with OSDB showed a higher VO
at rest (13.49 ± 6.02 mL min
kg
in OSDB vs. 11.55 ± 6.83 mL min
kg
in no-OSDB, p = 0.004) and a higher EE at rest (67.5 ± 30.10 cal min
kg
in OSDB vs. 57.8 + 34.15 cal min
kg
in no-OSDB, p = 0.004). At maximal exercise, patients with OSDB showed a lower VO
max (33.25 ± 5.82 mL min
kg
in OSDB vs. 34.28 ± 6.71 in no-OSDB, p = 0.008) and a lower EE (166.3 ± 29.11 cal min
kg
in OSDB vs. 171.4 ± 33.53 cal min
kg
in no-OSDB, p = 0.008). The VO
/EE increment with exercise (Δ VO
and Δ EE) was lower in OSDB for all exercise intensities (p = 0.009). This model unveils the effect of paediatric OSDB on resting and exercise metabolism. Our findings support the higher basal metabolic rates, poorer fitness performance, and cardiovascular impairment found in children with OSDB.
Electrical discharge machining (EDM) involves the generation of micro-plasmas subjected to high temperature and pressure to promote the material removal. Hence, to understand the material removal ...mechanism it is of great importance the knowledge of the interaction plasma-solid. Knowing how physical and chemical properties of materials affect heat transfer at the electrode surface, how this eventually affects electrical properties of the plasma channel over the discharge time are key issues to achieve a better understanding of this machining technology. This research attempts to provide some answers to these issues by means of single plasma discharge tests under laboratory-controlled conditions carried out on pure and low-alloyed materials in favour of comprehensiveness and forthcoming numerical modelling. These results demonstrate that material eroded volume is correlated with process operating parameters and that crater morphology has presented a more regular shape in pure metals than in engineering materials. The machinability index of the materials under study has been determined by calculations of the eroded volume and electrical power measures. Further to the low predictability of the models presented in literature, it was also proposed a basic conceptual model referring to the morphology of the eroded craters.
Recently, there has been a significant amount of attention towards computer vision algorithms, particularly those that focus on semantic segmentation applications. This is due to the availability of ...big data to train models, as well as the computational ability of these algorithms. Among the various computer vision applications, the U-Net has gained popularity due to its reliable accuracy, simplicity in construction, and ease of application.Despite the advantages of this network structure, there are still some unclear aspects within the U-Net that have not been significantly covered in literature - to the best of our knowledge -. This study seeks to clarify and explain these ambiguous points and construct different architectures to demonstrate their pros and cons. Finally, a series of experiments were carried out on natural leather samples from MVTec AD to confirm our findings. The outcomes highlight our discoveries and provide a framework for determining the fine-tuning parameters of U-Net.
Natural leather is a product made from animal skin which is treated through chemical procedure to preserve it. It is used in the manufacture of clothing, bags, furniture, automobile material, among ...others. Because of its capital value in industry, it is important to ensure its quality. Traditional inspection by human experts is expensive, time-consuming, and subjective to human errors. Consequently, automatic leather inspection has become an essential part of any production system as it rejects nonconformities, ensures product quality, reduces operating costs, and shortens production cycle times. This paper presents an artificial intelligent model using computer vision for the autonomous inspection of natural leather. Due to its good results in similar problems, the YOLO algorithm was chosen. More specifically, a comparison of the Small, Medium, Large, and Extra-Large models of YOLOv5 in leather defect detection was performed. We used images of leather with and without defects from the MVTec Anomaly Detection dataset. After training, the models were analyzed and compared based on some performance metrics. All models showed a great ability to detect defects in the dataset used.
A rápida evolução tecnológica e os consequentes desafios em matéria de proteção de dados pessoais levaram à necessidade de rever o quadro normativo, razão pela qual surge o RGPD. Desta forma, e ...considerando o novo regime, torna-se premente analisar os seus impactos na geolocalização no contexto laboral. Atéà data de entrada em vigor do RGPD, a discussão centrava-se na consideração ou não da geolocalização como um meio de vigilância à distância. Agora, com o RGPD, importa indagar sobre a admissibilidade ou não do consentimento como requisito de legitimidade de tratamento de dados, a aplicação dos artigos 20º e 21º do CT e os novos procedimento a observar.
The food chain is a large contributor to environmental pollution, especially greenhouse gas emissions, strongly associated with the consumption of animal-based proteins. The understanding of the ...negative environmental impacts of dietary habits by the population is of the utmost importance to provide the means to effect change to more sustainable eating patterns. The main purpose of this study was to assess the carbon footprint of animal protein consumption in Portugal, while also evaluating six mitigation scenarios aiming to lower greenhouse gas emissions through strategic changes to the animal protein consumption of current dietary habits. Overall, the carbon footprint associated with animal protein consumption is 2.63 kg CO2 eq/(cap⋅day) nationally and 28.4 t CO2 eq/month for the faculty canteen. Meat is by far the largest contributor to the carbon footprint in both cases, with beef being its “hotspot”. All scenarios showed significant reduction potentials, with values ranging from 16% (lower value for both the national case and the faculty canteen) to 71% (faculty canteen). In sum, substantial carbon footprint reductions can be attained if policymakers support the implementation of effective measures to promote a shift in the current animal protein consumption towards more sustainable eating habits.
Fused filament fabrication (FFF) is an extrusion-based process that allows quick and inexpensive part production, practically without any geometric limitations, offering flexibility, promoting ...reduction in costs and lead-time in an industrial scenario. Being one of the most widespread additive manufacturing techniques, the process has evolved introducing new and advanced materials (e.g. high-performance polymers and composites). Despite its advantages, the process is vastly overlooked due to its high level of anisotropy, poor surface roughness and lack of geometric accuracy caused by the layer thickness. To reduce this effect, a sequence of laborious manual operations can be performed, which may result in time-consuming and inaccurate results. Therefore, efforts have been made towards the development of hybrid manufacturing technologies by combining FFF process and subtractive equipment, aiming to solve these limitations. In this work, two complementary methodologies analysing the behaviour of FFF PA12 and short fibre–reinforced PA12 printed parts when subjected to a subtractive approach are presented. The first experimental plan took into account the final surface roughness (
R
a
and
R
z
) via full factorial design of experiments (DOE) and analysis of variance (ANOVA) considering the influence of distinct printing orientations, two types of cutting tools and machining parameters such as, cutting speed, feed and cutting depth. An analysis on tool wear and SEM microscopy to the machined surface was also performed. The second approach was carried out via Taguchi and ANOVA, considering the first experimental approach results. Thus, milling parameters were the focus, evaluating the final material surface roughness, being now monitored the cutting forces and tool wear analysis in order to understand their influence on the final results. It is shown that it is possible to machine PA12-based FFF printed parts without any major problems such as layer delamination. A decrease in
R
a
,
t
of 1931% to 0.99
μ
m for PA12CF and 2255% for PA12 to 0.96
μ
m was achieved, proving the overall machinability of the materials. It was found that PA12 creates higher levels of cutting loads and increased tool wear, thus indicating that short fibre presence improves the material machinability, while parameters such as building orientation do not possess any influence on the final surface roughness.
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