Mechanical properties and microstructure are investigated for a martensitic stainless steel (AISI 420) fabricated by selective laser melting (SLM) in three build directions. The tensile specimens built by SLM are classified into three groups. Group A is horizontally built in the thickness direction, Group B is horizontally built in the width direction, and Group C is vertically built in the length direction. The loading direction in tensile test is parallel to the build direction of Group C, but perpendicular to that of Groups A and B. Experimental results indicate build direction has significant effects on the residual stress, hardness, and tensile properties of SLM builds. Microstructural analyses indicate the as-fabricated SLM AISI 420 builds exhibit elongated cells and acicular structures which are composed of martensite and retained austenite phases growing along the build direction. Such anisotropy in the microstructure leads to anisotropic mechanical properties as Group C specimens (length direction) exhibit greater yield stress, ultimate tensile stress, and elongation than the specimens of Groups A (thickness direction) and B (width direction). The residual compressive stress in the gauge section also contributes to the superior tensile properties of Group C (length direction), as compared to Groups A (thickness direction) and B (width direction), which exhibit residual tensile stress in the gauge section.

The purpose of this study is to investigate the effects of laser spot size on the mechanical properties of AISI 420 stainless steel, fabricated by selective laser melting (SLM), process. Tensile specimens were built directly via the SLM process, using various laser spot diameters, namely 0.1, 0.2, 0.3, and 0.4 mm. The corresponding volumetric energy density (E_V) is 80, 40, 26.7, and 20 J/mm³, respectively. Experimental results indicate that laser spot size is an important process parameter and has significant effects on the surface roughness, hardness, density, tensile strength, and microstructure of the SLM AISI 420 builds. A large laser spot with low volumetric energy density results in balling, un-overlapped defects, a large re-heated zone, and a large sub-grain size. As a result, SLM specimens fabricated by the largest laser spot diameter of 0.4 mm exhibit the roughest surface, lowest densification, and lowest ultimate tensile strength. To ensure complete melting of the powder and melt pool stability, E_V of 80 J/mm³ proves to be a suitable laser energy density value for the given SLM processing and material system.

This paper presents the studies of stick-slip friction, presliding displacement and its influence on hunting. Experimental studies reveal that presliding displacement could affect the stability of hunting. A modified Coulomb friction model integrating presliding displacement in the microsliding regime is proposed to demonstrate such effect. Finally, step responses obtained from experiments and from the modified model are compared. These comparisons yield the conclusion that the transition of friction between the sticking state and the sliding state is smooth and continuous, not abrupt. Such a smooth transition of friction is critical to the studies of systems performing high-speed cyclic motion.

Kerf width is one of the most important quality items in cutting of thin metallic sheets. The aim of this study was to develop a convolutional neural network (CNN) model for analysis and prediction of kerf width in laser cutting of thin non-oriented electrical steel sheets. Three input process parameters were considered, namely, laser power, cutting speed, and pulse frequency, while one output parameter, kerf width, was evaluated. In total, 40 sets of experimental data were obtained for development of the CNN model, including 36 sets for training with k-fold cross-validation and four sets for testing. Compared with a deep neural network (DNN) model and an extreme learning machine (ELM) model, the developed CNN model had the lowest mean absolute percentage error (MAPE) of 4.76% for the final test dataset in predicting kerf width. This indicates that the proposed CNN model is an appropriate model for kerf width prediction in laser cutting of thin non-oriented electrical steel sheets.