The tool wear monitoring (TWM) system that could estimate tool wear conditions and predict remaining useful life (RUL) is important to meet the high precision requirement and improve productivity in automated machining. Due to its good properties in representing nonstationary and complex physical process, hidden semi-Markov Model (HSMM) is adapted to model the progressive tool wear in this paper. In order to describe the time-variant transition probability of tool wear states and the state duration dependency, the HSMM is improved by learning the duration parameters and RUL distribution database. The Forward algorithm is utilized for online tool wear estimation and remaining life prognosis, and an online implementation approach is developed to reduce computational cost. Experimental results show that the approach is effective and the proposed method of duration dependency modeling leads to more accurate TWM in high speed milling.

Spatter is an inherent, unpreventable, and undesired phenomenon in laser powder bed fusion (L-PBF) additive manufacturing. Spatter behavior has an intrinsic correlation with the forming quality in L-PBF because it leads to metallurgical defects and the degradation of mechanical properties. This impact becomes more severe in the fabrication of large-sized parts during the multi-laser L-PBF process. Therefore, investigations of spatter generation and countermeasures have become more urgent. Although much research has provided insights into the melt pool, microstructure, and mechanical property, reviews of spatter in L-PBF are still limited. This work reviews the literature on the in situ detection, generation, effects, and countermeasures of spatter in L-PBF. It is expected to pave the way towards a novel generation of highly efficient and intelligent L-PBF systems.

A method of hybrid convolutional neural networks (CNNs) was proposed for powder-bed fusion (PBF) process monitoring in this work. The proposed method can learn both the spatial and temporal representative features from the raw images automatically based on the advantages of the CNN architecture. The results demonstrated the superior performance of the proposed method compared with the traditional methods with handcrafted features. The overall detection accuracy of four process conditions, e.g., overheating, normal, irregularity and balling, can be up to 0.997. In addition, it is found the temporal information for PBF process monitoring by the vision detection of the process zone (including melt pool, plume and spatters) is significant. As the proposed method can save image processing steps, it simplifies the procedure on feature extraction. This makes it more suitable for online monitoring application.

Tool condition monitoring is essential to maintain the final product quality and machining efficiency of the manufacturing process. However, traditional physics-based and data-driven approaches have limitations either on prediction efficiency or performance generalization, due to the nature of the respective approaches. To address these issues, in this article, a physics-informed deep learning approach is developed, which integrates the tool wear mechanism into the data-driven model. First, some representative physical information is selected for the task learning. Then, four practical physics-informed methods are proposed to integrate various physical information into the data-driven models. Based on these physical constraints, a physics-informed deep learning model is specially designed for tool wear monitoring. Compared with previous studies, more diverse physical information can be effectively utilized to guide the hypothesis space, thereby improving the generality of the model. The effectiveness and feasibility of this model under various working conditions are verified in high-speed milling experiments. The results show that the wear prediction of the proposed approach is more accurate and consistent under unknown machining conditions.