Selective laser melting (SLM) additive manufacturing (AM) exhibits uncertainties, where variations in build quality are present despite utilizing the same optimized processing parameters. In this work, we identify the sources of uncertainty in SLM process by in-situ characterization of SLM dynamics induced by small variations in processing parameters. We show that variations in the laser beam size, laser power, laser scan speed, and powder layer thickness result in significant variations in the depression zone, melt pool, and spatter behavior. On average, a small deviation of only ~5% from the optimized/reference laser processing parameter resulted in a ~10% or greater change in the depression zone and melt pool geometries. For spatter dynamics, small variation (10 μm, 11%) of the laser beam size could lead to over 40% change in the overall volume of the spatter generated. The responses of the SLM dynamics to small variations of processing parameters revealed in this work are useful for understanding the process uncertainties in the SLM process.
With the application of biomimetic shark skin microstructures with hydrophobicity in microfluidics, sensors and self-cleaning materials, microstructure processing methods are increasing. The preparation process has higher requirements for processing cost and efficiency. In this paper, linear low-density polyethylene (LLDPE) hydrophobic films were prepared with the help of melt fracture phenomenon. The equipment is a self-made single screw extruder. By adjusting the process parameters, the biomimetic shark skin structured LLDPE films with good hydrophobic property can be obtained. The surface microstructure shape of the product is related to kinds of additive, die temperature and screw speed. When AC5 was selected as an additive, the optimal processing parameter was found to be 160 °C die temperature and 80 r/min screw speed. A contact angle of 133° was obtained in this situation. In addition, the influences of die temperature and screw speed on the size of shark skin structure were also systematically investigated in this paper. It was found that the microstructure surface with hierarchical roughness had a better hydrophobic property.
This study examined the uniformity of illuminance field distributions of light guide plates (LGPs). First, the authors designed microstructural patterns on the surface of an LGP. Then, a mold of the LGP with the optimal microstructural design was fabricated by a photolithography method. Micro-injection molding (μIM) was used to manufacture the molded LGPs. μIM technology can simultaneously manufacture large-sized wedge-shaped LGPs and micro-scale microstructures. Finally, illuminance values of the field distributions of the LGPs with various microstructures were obtained through optical field measurements. This study compared the illuminance field distributions of LGPs with various designs and structures, which included LGPs without and those with microstructure on the primary design and the optimal design. The average illuminance of the LGP with microstructures and the optimal design was roughly 196.1 cd/m2. Its average illuminance was 1.3 times that of the LGP without microstructures. This study also discusses illuminance field distributions of LGPs with microstructures that were influenced by various μIM process parameters. The mold temperature was found to be the most important processing parameter affecting the illuminance field distribution of molded LGPs fabricated by μIM. The molded LGP with microstructures and the optimal design had better uniformity than that with microstructures and the primary design and that without microstructures. The uniformity of the LGP with microstructures and the optimal design was roughly 86.4%. Its uniformity was nearly 1.65 times that of the LGP without microstructures. The optimized design and fabrication of LGPs with microstructure exhibited good uniformity of illuminance field distributions.
In order to obtain the desired mechanical properties of products, an innovative method of loading parameter designs for acquiring the desired grain refinement is proposed, and it has been applied in the compression process of Ni80A superalloy. The deformation mechanism maps derived from processing maps based on the Dynamic Materials Model (DMM) theory were constructed, since the critical indicator values corresponding to dynamic recrystallization (DRX) and dynamic recovery (DRV) mechanisms were determined. The processing-parameter domains with DRX mechanisms were separated from the deformation mechanism map, while such domains were chaotic and difficult to apply in innovative parameter loading path design. The speed-loading path derived from strain rate-loading path in a compression process was pursued. The grain refinement domains are discretized into a finite series of sub-domains with clear processing parameters, and the optimal strain rate of each sub-domain is determined by step-by-step finite element simulation. A 3D response surface of the innovative optimal loading path of strain rate was fitted by interpolating methods. Finally, the isothermal compression experiments for Ni80A superalloy were conducted, and the microstructure observations indicated that the desired grain refinement was achieved. This innovative method of parameter loading path design contributes to the microstructure adjustment of the alloys with DRX mechanism.
To fully exploit the benefits of additive manufacturing (AM), an understanding of its processing, microstructural, and mechanical aspects, and their interdependent characteristics, is necessary. In certain instances, AM materials may be desired for applications where impact toughness is a key property, such as in gas turbine fan blades, where foreign or direct object damage may occur. In this research, the impact energy of a series of Ti-6Al-4V specimens produced via electron beam powder bed fusion (EBPBF) was established via Charpy impact testing. Specimens were produced with five different processing parameter sets, in both the vertical and horizontal build orientation, with microstructural characteristics of prior β grain area, prior β grain width, and α lath width determined in the build direction. The results reveal that horizontally oriented specimens have a lower impact energy compared to those built in the vertical orientation, due to the influence of epitaxial grain growth in the build direction. Relationships between process parameters, microstructural characteristics, and impact energy results were evaluated, with beam velocity displaying the strongest trend in terms of impact energy results, and normalised energy density exhibiting the most significant influence across all microstructural measurements.
This paper mainly presents a set of new Sapphire Backside Roughing technology. Presently, the associated Sapphire Backside Roughing technology is still concentrated on chemical etching, as its yield rate and efficiency are often limited by lattice structures, and the derived chemical waste fluid after etching is most likely to cause ecological contamination. In this research, refined abrasive jet processing technology is adopted, and in the meantime, the Taguchi experiment design method is taken for detailed experimental planning. Through processing parameter conditions and abrasive selection and development, proper surface roughing and processing uniformity are obtained so as to improve the various weak points of the abovementioned traditional etching effectively. It is discovered that abrasive blasting processing technology is, respectively, combined with wax-coated #1000 SiC particles and wax-coated #800 Zirconium particles to process the sapphire substrate with initial surface roughness 0.8–0.9 μmRa from the experiment. A 1.1–1.2 μmRa surface roughness effect can be achieved about two minutes later. The experimental results show that the actual degree of sapphire substrate surface roughing obtained in the AJM process depends on the gas pressure, impact angle, wax-coated abrasives, and additives. The new Sapphire Backside Roughing technology has high flexibility, which not only meets the requirements for sapphire surface roughing specification but can also effectively reduce the sapphire substrate roughing time and related cost.
AbstractUnderstanding and controlling the self-assembly of vertically oriented carbon nanotube (CNT) forests is essential for realizing their potential in myriad applications. The governing process–structure–property mechanisms are poorly understood, and the processing parameter space is far too vast to exhaustively explore experimentally. We overcome these limitations by using a physics-based simulation as a high-throughput virtual laboratory and image-based machine learning to relate CNT forest synthesis attributes to their mechanical performance. Using CNTNet, our image-based deep learning classifier module trained with synthetic imagery, combinations of CNT diameter, density, and population growth rate classes were labeled with an accuracy of >91%. The CNTNet regression module predicted CNT forest stiffness and buckling load properties with a lower root-mean-square error than that of a regression predictor based on CNT physical parameters. These results demonstrate that image-based machine learning trained using only simulated imagery can distinguish subtle CNT forest morphological features to predict physical material properties with high accuracy. CNTNet paves the way to incorporate scanning electron microscope imagery for high-throughput material discovery.
In this study, the effect of cutting parameters on kerf quality and surface roughness in laser cutting of Al 5083 alloyed material was investigated both experimentally and statistically (Taguchi & Gray Taguchi). Experimental design was determined using the Taguchi L[Formula: see text] (21x 3[Formula: see text] orthogonal array. Experiments were carried out by using two different gas pressures (GP) (8 and 10 bar), three different cutting speeds (CS) (3500, 4000 and 4500[Formula: see text]mm[Formula: see text][Formula: see text][Formula: see text]min[Formula: see text] and laser power (P) (2600, 3100 and 3600[Formula: see text]W) in the cutting of Al 5083 alloyed material. As a result of the study, top kerf width (TKW), bottom kerf width (BKW) and average surface roughness (Ra) were measured as output parameter values. Using the measured top and BKW results, kerf taper (KT) was calculated. The lowest values of TKW, BKW, KT and Ra were 2.219[Formula: see text]mm, 2.010[Formula: see text]mm, 0.984∘ and 2.394[Formula: see text][Formula: see text]m, respectively. To determine the optimum values of laser cutting parameters for minimum output parameters, signal-noise (S/N) ratio, variance and regression analysis, validity experiments and GRA methods were used. When S/N ratios were examined, the most ideal cutting parameters were determined as A1B3C1 for TKW and BKW, A2B1C3 for KT and A2B3C3 for Ra. When the variance results were examined, it was determined that the most effective processing parameter for TKW, BKW and KT was at 46.04%, 50.58% and 56.45% CS, respectively, and the most effective processing parameter for Ra was laser power with 46.57%. According to gray relationship analysis, optimum laser cutting parameters for the smallest values of all output parameters were determined as A1B3C1. According to gray relationship analysis, optimum laser cutting parameters for the smallest values of all output parameters were determined as A1B3C1.