A multi-sensor based online tool condition monitoring system for milling process

Tool condition monitoring has been considered as one of the key enabling technologies for manufacturing optimization. Due to the high cost and limited system openness, the relevant developed systems have not been widely adopted by industries, especially Small and Medium-sized Enterprises. In this research, a cost-effective, wireless communication enabled, multi-sensor based tool condition monitoring system has been developed. Various sensor data, such as vibration, cutting force and power data, as well as actual machining parameters, have been collected to support efficient tool condition monitoring and life estimation. The effectiveness of the developed system has been validated via machining cases. The system can be extended to wide manufacturing applications

Integrated Sensing-Communication-Computation for Edge Artificial Intelligence

Edge artificial intelligence (AI) has been a promising solution towards 6G to empower a series of advanced techniques such as digital twin, holographic projection, semantic communications, and auto-driving, for achieving intelligence of everything. The performance of edge AI tasks, including edge learning and edge AI inference, depends on the quality of three highly coupled processes, i.e., sensing for data acquisition, computation for information extraction, and communication for information transmission. However, these three modules need to compete for network resources for enhancing their own quality-of-services. To this end, integrated sensing-communication-computation (ISCC) is of paramount significance for improving resource utilization as well as achieving the customized goals of edge AI tasks. By investigating the interplay among the three modules, this article presents various kinds of ISCC schemes for federated edge learning tasks and edge AI inference tasks in both application and physical layers

Layered Antiferromagnetism Induces Large Negative Magnetoresistance in the van der Waals Semiconductor CrSBr

The recent discovery of magnetism within the family of exfoliatable van der Waals (vdW) compounds has attracted considerable interest in these materials for both fundamental research and technological applications. However current vdW magnets are limited by their extreme sensitivity to air, low ordering temperatures, and poor charge transport properties. Here we report the magnetic and electronic properties of CrSBr, an air-stable vdW antiferromagnetic semiconductor that readily cleaves perpendicular to the stacking axis. Below its N\'{e}el temperature, $T_N = 132 \pm 1$ K, CrSBr adopts an A-type antiferromagnetic structure with each individual layer ferromagnetically ordered internally and the layers coupled antiferromagnetically along the stacking direction. Scanning tunneling spectroscopy and photoluminescence (PL) reveal that the electronic gap is $\Delta_E = 1.5 \pm 0.2$ eV with a corresponding PL peak centered at $1.25 \pm 0.07$ eV. Using magnetotransport measurements, we demonstrate strong coupling between magnetic order and transport properties in CrSBr, leading to a large negative magnetoresistance response that is unique amongst vdW materials. These findings establish CrSBr as a promising material platform for increasing the applicability of vdW magnets to the field of spin-based electronics

Distributed deep learning enabled prediction on cutting tool wear and remaining useful life

To optimise the utilisation cost of cutting tools, it is imperative to develop an online system to efficiently and accurately predict tool wear conditions and remaining useful lives (RULs). With this aim, a novel system is proposed based on deep learning algorithms distributed over an edge-cloud computing architecture. The system is innovative in the following aspects: (i) a lightweight convolutional neural network-random forest (CNN-RF) model is designed to be executed on an edge device to assess tool wear conditions efficiently, which supports severe tool resilience and tool replacement when necessary; (ii) a convolutional neural network-long short-term memory (CNN-LSTM) model is designed to be executed on a cloud to process long-term signals to predict the RUL of the cutting tool, which supports fine-tuning tool parameters dynamically; (iii) a signal compression mechanism is developed to condense the signals of tooling conditions into 2D images so the signal volumes transferred over the network are minimised and signal security is improved. Experiments were performed in a real-world machining workshop for research methodology validation. It showed that the prediction accuracies for tool wear and RUL achieved 90.6% and 93.2%, respectively, and the volume of signals transferred over the network was reduced by 89.0%. The experiments and benchmarks with comparative algorithms demonstrated that the system and its methodology exhibited great potential to reinforce cutting tool optimisation for real-world applications

Construção de um protótipo de Data Warehouse como suporte ao sistema de informação numa instituição de ensino superior

Uma das dificuldades que se verifica na extracção de informação numa organização é a falta de integração dos dados existentes dispersos em diversos formatos: ficheiros de processadores de texto, folhas de cálculo, bases de dados, entre outras fontes. A partir deste problema, este trabalho propõe a estruturação de um modelo de Data Warehouse com o objectivo de organizar, armazenar e integrar as informações provenientes de outros formatos e sistemas, numa única base de dados para uma futura utilização no suporte à tomada de decisão. Existem, neste momento, na comunidade de Data Warehousing duas principais abordagens, uma preconizada por William H. Inmon, mais centrada nos dados, e outra por Ralph Kimball, mais centrada no projecto. Assim, com a metodologia proposta foi desenvolvido um caso de estudo com a finalidade de verificar e avaliar a aplicabilidade da metodologia no Instituto Politécnico de Tomar; ABSTRACT: One difficulty that exists in the extraction of information in organizations is the lack of integration of existing data scattered in various formats: word processing files, spreadsheets, databases, among other sources. From this problem, this paper proposes to structure a model of Data Warehouse in order to organize, store and integrate information from other systems and formats in a single database for future use in supporting decision making. There are at present in the community of Data Warehousing two main approaches, one advocated by William H. Inmon, more data-centric, and one by Ralph Kimball, more focused on the project. So with the proposed methodology was developed a case study in order to verify and evaluate the applicability of the methodology at the Polytechnic Institute of Tomar

Evaluation of meat as a first complementary food for breastfed infants: impact on iron intake: Nutrition Reviews©, Vol. 66, No. S1

The rationale for promoting the availability of local, affordable, non-fortified food sources of bioavailable iron in developing countries is considered in this review. Intake of iron from the regular consumption of meat from the age of 6 months is evaluated with respect to physiological requirements. Two major randomized controlled trials evaluating meat as a first and regular complementary food are described in this article. These trials are presently in progress in poor communities in Guatemala, Pakistan, Zambia, Democratic Republic of the Congo, and China

Propagation of Non-Linear Lamb Waves in Adhesive Joint with Micro-Cracks Distributing Randomly

With the advantages of uniform stress transfer and weight reduction, adhesive joints are widely used in engineering. The propagation of non-linear Lamb waves in an adhesive joint with micro-cracks distributing in a random way is systematically investigated by using the numerical simulation method in this paper. A finite element model of the tri-layer adhesive structure with micro-cracks distributing randomly is established, and the Lamb wave mode pair with a matching condition of the phase velocity is chosen to examine the interaction of the micro-cracks with Lamb waves. The results show that the micro-cracks within the adhesive layer will lead to the generation of second harmonics. We also find that the Acoustic Non-linearity Parameters (ANP) increase with the propagation distance in the micro-crack damage zone and the density of the micro-cracks. However, ANPs are less concerned with the friction coefficients of the surface of micro-cracks. This numerical research reveals that non-linear Lamb waves can be employed to effectively characterize the micro-cracks related damages within an adhesive joint

Ultrasonic echo detection technology for coating defects on submarine pipeline

In view of the characteristics of strong penetration and extremely sensitive to delamination of materials, ultrasonic wave has unique advantages in coating defect detection. In order to quickly and accurately detect the defects of the outer coating of submarine oil and gas pipelines, and make up for the weakness of the existing coating detection technologies for oil and gas pipeline, such as low reliability, vulnerable to external interference, inaccurate positioning, etc., a pipeline coating defect detection method using ultrasonic pulse echo was proposed. Through ANSYS modeling and simulation calculation, the attenuation characteristics of reflected echo between intact coating interface and defective coating interface were compared, and obvious differences can be seen in the amplitude of echo after multiple reflections. The fifth echo signal was selected as the basis for determining the coating defects, and the reliability of simulation results was verified by designed test. This study shows that the method has high sensitivity to accurately detect coating defects. The operation is simple and reliable, not affected by the submarine complex environment, and meets the needs of continuous detection, so it can guarantee the safe and efficient operation of submarine oil and gas pipelines