Monitoring of tool wear in machining process has found its importance to predict tool life, reduce equipment downtime, and tool costs. Traditional visual methods require expert experience and human resources to obtain accurate tool wear information. With the development of charge-coupled device (CCD) image sensor and the deep learning algorithms, it has become possible to use the convolutional neural network (CNN) model to automatically identify the wear types of high-temperature alloy tools in the face milling process. In this paper, the CNN model is developed based on our image dataset. The convolutional automatic encoder (CAE) is used to pre-train the network model, and the model parameters are fine-tuned by back propagation (BP) algorithm combined with stochastic gradient descent (SGD) algorithm. The established ToolWearnet network model has the function of identifying the tool wear types. The experimental results show that the average recognition precision rate of the model can reach 96.20%. At the same time, the automatic detection algorithm of tool wear value is improved by combining the identified tool wear types. In order to verify the feasibility of the method, an experimental system is built on the machine tool. By matching the frame rate of the industrial camera and the machine tool spindle speed, the wear image information of all the inserts can be obtained in the machining gap. The automatic detection method of tool wear value is compared with the result of manual detection by high precision digital optical microscope, the mean absolute percentage error is 4.76%, which effectively verifies the effectiveness and practicality of the method.

The hybrid process integrates two or more different processes, such as additive and subtractive manufacturing, which have gained appreciable consideration in recent years. The deformation of hybrid manufacturing is an essential factor affecting machining quality. The purpose of this paper is to study the effect of milling on stress release and surface deformation of additive manufacturing (AM) specimens in the process of additive and subtractive hybrid manufacturing (ASHM) of 316L stainless steel thin-walled parts, so as to effectively improve the forming quality of thin-walled parts manufactured by the combined processing of ASHM. To this end, a series of experiments were carried out to study the relationship between stress distribution and thermal stress deformation of 316L stainless steel thin-walled parts prepared by LMD, and the changes of stress and deformation of these thin-walled parts after subsequent milling. An infrared camera and laser distance sensor were used to record the temperature field data and deformation data to analyze the influence factors of temperature and stress on the machining results. Then, the finite element software was used to simulate the stress and deformation of the thin-walled parts in the additive manufacturing process and the subsequent milling process. Meanwhile, the model was verified through the experiments. In addition, the relationship between the milling force and the milling parameters of the AM parts was studied by orthogonal test and regression analysis.

Carbon nanotube (CNT) film can be used as thin film electrodes and wearable electronic devices due to their excellent mechanical and electrical properties. The femtosecond laser has the characteristics of an ultra-short pulse duration and an ultra-high peak power, and it is one of the most suitable methods for film material processing. The ablation and patterning of CNT film are performed by a femtosecond laser with different parameters. An ablation threshold of 25 mJ/cm² was obtained by investigating the effects of laser pulse energy and pulse number on ablation holes. Raman spectroscopy and scanning electron microscope (SEM) were used to characterize the performance of the pattern groove. The results show that the oligomer in the CNT film was removed by the laser ablation, resulting in an increase in Raman G band intensity. As the laser increased, the ablation of the CNTs was caused by the energy of photons interacting with laser-induced thermal elasticity when the pulse energy was increased enough to destroy the carbon–carbon bonds between different carbon atoms. Impurities and amorphous carbon were found at and near the cut edge while laser cutting at high energy, and considerable distortion and tensile was produced on the edge of the CNTs’ groove. Furthermore, appropriate cutting parameters were obtained without introducing defects and damage to the substrate, which provides a practical method applied to large-area patterning machining of CNT film.

Coated cemented carbide currently represents most of all cutting tool inserts due to its unique combination of wear resistance and toughness. Surface texturing technology can give additional performance to reduce the tool’s wear and energy consumption. Using TiAlN-coated cemented carbide tools as the research object, the effects of femtosecond laser parameters (laser energy, scanning speed, scanning times) on the groove morphology of TiAlN-coated tools and the bonding state of coating and substrate were discussed. The study found that when the laser energy was 10 μJ, the scanning speed was 0.7 mm/s, and the number of scans was 5, the groove morphology was ideal, and the coating and the substrate combination remained unchanged. The influence of micro-groove texture on the tool wear mechanism was investigated by cutting test using the micro-groove texture tool produced by this group of process parameters. The flank wear value of inserts with micro-grooved texture decreased significantly by around 25%. This work provides practical data to support the femtosecond laser processing of TiAlN-coated tools. It is helpful to understand further the processing mechanism of a femtosecond laser on the micro-texture of coated tools.

Environmental enteric dysfunction (EED) is an elusive, inflammatory syndrome of the small intestine thought to be associated with enterocyte loss and gut leakiness and lead to stunted child growth. To date, the gold standard for diagnosis is small intestine biopsy followed by histology. Several putative biomarkers for EED have been proposed and are widely used in the field. Here, we assessed in a cross-sectional study of children aged 2–5 years for a large set of biomarkers including markers of protein exudation (duodenal and fecal alpha-1-antitrypsin (AAT)), inflammation (duodenal and fecal calprotectin, duodenal, fecal and blood immunoglobulins, blood cytokines, C-reactive protein (CRP)), gut permeability (endocab, lactulose-mannitol ratio), enterocyte mass (citrulline) and general nutritional status (branched-chain amino acids (BCAA), insulin-like growth factor) in a group of 804 children in two Sub-Saharan countries. We correlated these markers with each other and with anemia in stunted and non-stunted children. AAT and calprotectin, CRP and citrulline and citrulline and BCAA correlated with each other. Furthermore, BCAA, citrulline, ferritin, fecal calprotectin and CRP levels were correlated with hemoglobin levels. Our results show that while several of the biomarkers are associated with anemia, there is little correlation between the different biomarkers. Better biomarkers and a better definition of EED are thus urgently needed.