Compared with other additive manufacturing processes, the metal-based additive manufacturing (MAM) can build higher precision and higher density parts, and have unique advantages in the applications ...to automotive, medical, and aerospace industries. However, the quality defects of builds, such as dimensional accuracy, layer morphology, mechanical and metallurgical defects, have been hindering the wide applications of MAM technologies. These decrease the repeatability and consistency of build quality. In order to overcome these shortcomings and to produce high-quality parts, it is very important to carry out online monitoring and process control in the building process. A process monitoring system is demanded which can automatically optimize the process parameters to eliminate incipient defects, improve the process stability and the final build quality. In this paper, the current representative studies are selected from the literature, and the research progress of MAM process monitoring and control are surveyed. Taking the key components of the MAM monitoring system as the mainstream, this study investigates the MAM monitoring system, measurement and signal acquisition, signal and image processing, as well as machine learning methods for the process monitoring and quality classification. The advantages and disadvantages of their algorithmic implementations and applications are discussed and summarized. Finally, the prospects of MAM process monitoring researches are advised.
•Conducted a thorough investigation of MAM processes monitoring methods.•Proposed a framework of survey from measurement to control.•Discussed in-depth on signal processing and feature extraction approaches for MAM process monitoring.•Summarized and advised the prospects of data-oriented MAM process monitoring researches.
Understanding precipitation behaviors of laser directed energy deposition (L-DED) Nb-Si-based in-situ composite is significant to tailor the microstructure and performances. Here, FCC-Ti and α-Nb5Si3 ...precipitates are formed in Nb-based solid solution(Nbss) of as-built Nb-24Ti-15Si alloy, and the orientation relationships (ORs) between precipitates and Nbss have been determined with coherent interfaces among them. With Zr or (Zr+Cr) addition, only γ-Nb5Si3 precipitate is formed in the Nbss. The ORs of 12¯16γ//011Nbss and (101¯0)γ//(01¯1)Nbss and semi-coherent interface is formed between γ-Nb5Si3 precipitate and Nbss in as-built Nb-24Ti-15Si-2Zr alloy, while ORs of 0001γ//111Nbss and (011¯0)γ//(11¯0)Nbss are appear in as-built Nb-24Ti-15Si-2Zr-5Cr alloy with near coherent interface. The supersaturation of alloying elements in Nbss during the near-rapid solidfication of L-DED supplies the chemical driving force and the thermal cycles in L-DED process provide the thermal driving force for the formation of precipitates.
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•A time-optimal motion planning method for robot machining sculpture surface is provided.•The constraints for the cutting tool are considered and the highest order of constraints in the joints space ...have up to three.•All the constraints are simplified and transformed to the tool path parameter space.•A one-way searching numerical integration approach is proposed to the time optimal control problem with constraints only in the tool path parameter space.
A time-optimal motion planning method for robotic machining of sculptured surfaces is reported in this paper. Compared with the general time-optimal robot motion planning, a surface machining process provides extra constraints such as tool-tip kinematic limits and complexity of the curved tool path that also need to be taken into account. In the proposed method, joint space and tool-tip kinematic constraints are considered. As there are high requirements for tool path following accuracy, an efficient numerical integration method based on the Pontryagin maximum principle is adopted as the solver for the time-optimal tool motion planning problem in robotic machining. Nonetheless, coupled and multi-dimensional constraints make it difficult to solve the problem by numerical integration directly. Therefore, a new method is provided to simplify the constraints in this work. The algorithm is implemented on the ROS (robot operating system) platform. The geometry tool path is generated by the CAM software firstly. And then the whole machine moving process, i.e. the feedrate of machining process, is scheduled by the proposed method. As a case study, a sculptured surface is machined by the developed method with a 6-DOF robot driven by the ROS controller. The experimental results validate the developed algorithm and reveal its advantages over other conventional motion planning algorithms for robotic machining.
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•Ray-tracing method coupled with the VOF method to track laser multi-reflection.•Surface morphology of single tracks on bare plates with different scan strategies.•Surface morphology ...of single tracks on a powder layer with different scan speeds.•Simulation and quantitative experimental validation of surface morphology.
Selective laser melting (SLM) is a promising additive manufacturing technology, which involves complex physics such as heat and mass transfer, phase transformation and molten pool flow. In this study, a three dimensional numerical model is developed to model the thermal-fluid flow and to predict the surface morphology for the SLM process. Particularly the laser ray tracing method is coupled with the VOF method to reproduce the multiple reflections of laser between the randomly packed powder particles and highly dynamic molten pool. Two sets of experiments are used to validate the model: 1) single tracks on a bare plate with different scan strategies, and 2) single tracks on a powder layer with different scan speeds. For the bare-plate single tracks, the width and surface elevation, including their variation along the track distance, are well reproduced in the simulations. For the powder-layer single tracks, the experimentally-observed balling, distributed and smooth tracks with varied scan speeds are all reproduced in the simulations. The results illustrate the complex flow pattern of the molten pool, particularly the effect of partially melted particles: 1) the semi-melted particles, drive the molten fluid flow from the molten pool center towards the unmelted particle, leading to the single track non-uniformity; 2) the near-fully melted particles, drive the molten fluid flow down into the melt pool, increasing the single track uniformity.
Laser powder bed fusion (L-PBF) is a metal additive manufacturing (AM) process with great potential in producing high performance metal components. Due to lack of stability and repeatability of the ...building process, its wide application in industry is limited. The process monitoring and control are import to ensure product quality. The size and shape of the melt pool are continuously changing during the L-PBF process, which may lead to the generation of defects. To represent the melt pool variations more accurately, a new motion feature is extracted and a classification model is constructed to identify the melting state. Firstly, a 36-dimensional motion feature is obtained by contour unwrapping with respect to the melt pool centroid. Subsequently, a sample dataset of melt pool image including four categories of melting states is established. Finally, a Gaussian process classification (GPC) model is constructed to identify the melting state based on motion feature. To verify the performance of GPC, it is also given that the recognition results based on support vector machine (SVM) model, multilayer perceptron (MLP) and long short-term memory (LSTM) neural network. The research results show that under the advantages of automatically optimizing hyperparameters and providing probability distribution information of melting state, the GPC model can still achieve a better recognition result. The overall recognition rate reaches 87.1%, and the melting state can be better identified. A novel in-situ monitoring idea is provided for the L-PBF in this research.
In selective laser melting (SLM), support structures play a critical role in successful printing. Despite its necessity, the removal of support structures after printing becomes a challenging task ...which is usually time-consuming and labour-intensive. Mechanical post-processing can be employed to facilitate the automatic removal of support structures with higher efficiency. However, the mechanical forces of a machining process such as milling can cause the cone supports to tilt, collapse and be pulled up. This paper presents a novel method to improve the machinability of cone support structures. In this method, the epoxy resin is filled into the gaps between the entire cone support structures to form a solid composite structure after the epoxy resin is fully cured. The relationship between the force components in various directions during support removal is theoretically analyzed. The method is studied in terms of cutting performance, cutting force and energy, tool wear, workpiece surface damage and corrosion behaviour. On top of rectifying the problems of resultant support tilting and collapsing, the cutting force, specific cutting energy, tool wear and damage to the workpiece surface are significantly reduced, and the corrosion behaviour of the samples is slightly improved due to better surface quality after support removal. A finite element model is constructed for analysis of the underlying mechanisms. This paper addresses the research gap on the corrosion behaviour of the workpiece after support removal, and the newly developed method can be further applied to the machining of other types of support structures in SLM.
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•A novel method is developed to solve the support tilting and collapsing problems during removal.•The force components in various directions during support removal are theoretically analyzed.•The cutting force, tool wear and damage to the workpiece surface are significantly reduced.•The corrosion behaviour of the samples sees signs of improvement.•A finite element method model is developed to reveal the underlying mechanisms.
Laser powder bed fusion (LPBF), one of the most powerful additive manufacturing techniques, has attracted broad interests from medical, aerospace and automotive industries. It is a powder bed fusion ...method, processing metallic and non-metallic materials in a layer-by-layer manner. However, such a layer based manufacturing process introduces internal defects such as pores into the final product, giving rise to negative effects on the mechanical performance. This paper is aimed towards providing a methodology for analyzing the effects of statistical pore characteristics on the mechanical behavior of the LPBF processed parts. Specifically, quantitative relationship among pore characteristics, such as statistical correlation between size and morphology/orientation of the pores, is established via X-ray computed tomography (XCT) for tensile specimens fabricated using a variety of energy input. The relationship among the pore features is compared before and after the tensile testing to gain deep insights on how the pore details affect the tensile behavior of the parts. Based on the quantitative correlation among the pore features, a micromechanical model is proposed to predict the mechanical properties of the materials, providing an alternative to the destructive tests for future LPBF users. The predicted results are benchmarked with classical analytical solutions, experimental data as well as reported numerical predictions.
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