Three-dimensional (3D) printing continuous carbon fiber-reinforced polylactic acid (PLA) composites offer excellent tensile mechanical properties. The present study aimed to research the effect of process parameters on the tensile mechanical properties of 3D printing composite specimens through a series of mechanical experiments. The main printing parameters, including layer height, extrusion width, printing temperature, and printing speed are changed to manufacture specimens based on the modified fused filament fabrication 3D printer, and the tensile mechanical properties of 3D printing continuous carbon fiber-reinforced PLA composites are presented. By comparing the outcomes of experiments, the results show that relative fiber content has a significant impact on mechanical properties and the ratio of carbon fibers in composites is influenced by layer height and extrusion width. The tensile mechanical properties of continuous carbon fiber-reinforced composites gradually decrease with an increase of layer height and extrusion width. In addition, printing temperature and speed also affect the fiber matrix interface, i.e., tensile mechanical properties increase as the printing temperature rises, while the tensile mechanical properties decrease when the printing speed increases. Furthermore, the strengthening mechanism on the tensile mechanical properties is that external loads subjected to the components can be transferred to the carbon fibers through the fiber-matrix interface. Additionally, SEM images suggest that the main weakness of continuous carbon fiber-reinforced 3D printing composites exists in the fiber-matrix interface, and the main failure is the pull-out of the fiber caused by the interface destruction.

Tool wear monitoring is necessary for cost reduction and productivity improvement in the machining industry. Machine learning has been proven to be an effective means of tool wear monitoring. Feature engineering is the core of the machining learning model. In complex parts milling, cutting conditions are time-varying due to the variable engagement between cutting tool and the complex geometric features of the workpiece. In such cases, the features for accurate tool wear monitoring are tricky to select. Besides, usually few sensors are available in an actual machining situation. This causes a high correlation between the hand-designed features, leading to the low accuracy and weak generalization ability of the machine learning model. This paper presents a tool wear monitoring method for complex part milling based on deep learning. The features are pre-selected based on cutting force model and wavelet packet decomposition. The pre-selected cutting forces, cutting vibration and cutting condition features are input to a deep autoencoder for dimension reduction. Then, a deep multi-layer perceptron is developed to estimate the tool wear. The dataset is obtained with a carefully designed varying cutting depth milling experiment. The proposed method works well, with an error of 8.2% on testing samples, which shows an obvious advantage over the classic machine learning method.

Titanium alloys are widely used in the manufacture of aircraft and aeroengine components. However, tool wear is a serious concern in milling titanium alloys, which are known as hard-to-cut materials. Trochoidal milling is a promising technology for the high-efficiency machining of hard-to-cut materials. Aiming to realize green machining titanium alloy, this paper investigates the effects of undeformed chip thickness on tool wear and chip morphology in the dry trochoidal milling of titanium alloy Ti–6Al–4V. A tool wear model related to the radial depth of cut based on the volume of material removed (VMR) is established for trochoidal milling, and optimized cutting parameters in terms of cutting speed and axial depth of cut are selected to improve machining efficiency through reduced tool wear. The investigation enables the environmentally clean rough machining of Ti–6Al–4V.