Artificial neural networks as alternative tool for minimizing error predictions in manufacturing ultradeformable nanoliposome formulations

This work was aimed at determining the feasibility of artificial neural networks (ANN) by implementing backpropagation algorithms with default settings to generate better predictive models than multiple linear regression (MLR) analysis. The study was hypothesized on timolol-loaded liposomes. As tutorial data for ANN, causal factors were used, which were fed into the computer program. The number of training cycles has been identified in order to optimize the performance of the ANN. The optimization was performed by minimizing the error between the predicted and real response values in the training step. The results showed that training was stopped at 10?000 training cycles with 80% of the pattern values, because at this point the ANN generalizes better. Minimum validation error was achieved at 12 hidden neurons in a single layer. MLR has great prediction ability, with errors between predicted and real values lower than 1% in some of the parameters evaluated. Thus, the performance of this model was compared to that of the MLR using a factorial design. Optimal formulations were identified by minimizing the distance among measured and theoretical parameters, by estimating the prediction errors. Results indicate that the ANN shows much better predictive ability than the MLR model. These findings demonstrate the increased efficiency of the combination of ANN and design of experiments, compared to the conventional MLR modeling techniques.     

[数字孪生驱动的智能制造]基于文献计量的数字孪生研究进展分析

伴随物联网技术、云计算及机器学习等新一代信息技术的迅速发展，数字孪生技术已逐渐成为新的研究热点。基于文献计量法，对1994年以来全球486篇数字孪生相关论文的研究领域、国家与地区、论文发布期刊、关键词、研究作者及高被引论文等模块展开详细分析。结果表明，数字孪生作为一个新的研究课题，其技术挖掘性强，已在制造工程、计算机科学及电子工程等领域得到了广泛应用。在智能制造刚性需求的驱动下，数字孪生技术在未来具有非常好的理论研究和技术应用前景。     

Optical in-process height measurement system for process control of laser metal-wire deposition

We propose an in-process height measurement system for a weld bead and feedback control system for wire-feeding speed for high-quality laser deposition. Metal additive manufacturing, especially laser metal-wire deposition, is effective for complex shape fabrication and repair processing. However, we must control the gap between a weld bead and a feed wire in an optimal range for high-quality deposition. Conventionally, the Z-stage pitch for multi-layer deposition must be precisely adjusted by each deposition shape. In this paper, we design an in-process height measurement system that is integrated in a laser processing head, which measures the weld bead height by a line section method. We decreased the influence of the intense thermal radiation generated from a melt pool by inserting the band-pass filter of the line beam's wavelength in the imaging system and optimizing its line laser power. Consequently, our system can measure the weld bead height near the melt pool, which is 4 mm in front of it. Next we show that our proposed system can measure the weld bead height during wire-laser metal deposition with 50-μm accuracy by comparing its value to the true value. Finally, we achieved a cylinder shape deposition of 50-mm height, regardless of the Z-stage pitch and the cylinder diameter of the multi-layer deposition, by controlling the wire-feeding speed based on the measured weld bead height.     

Parametric study for tensile properties of molded high-density polyethylene for applications in additive manufacturing and sustainable designs

Test specimens following ASTM D638 standards are frequently used to measure the tensile properties of reinforced and unreinforced polymers machined with traditional machining and emerging manufacturing methods (additive manufacturing/3D printing). However, designs of large engineering structures may rely on mechanical properties based on ASTM D3039 for fiber-reinforced polymer composites. This parametric study examines the scaling effects present in uniaxial tensile test specimens of molded high-density polyethylene (HDPE), with geometries ranging from Types I to IV of ASTM D638 to ASTM D3039. HDPE is a thermoplastic polymer that is recyclable, can be 3D-printed, and has a wide range of engineering applications, from bottles to pipes to radiation protection shielding. The mechanical properties test results for the molded HDPE samples are validated using a Monte Carlo simulation to estimate uncertainties for the probability distribution of maximum stress at the yield point. A Finite Element study based on the empirical model shows how the proposed approach can be adopted for design purposes. The results of this work are a useful tool to enhance confidence in the tensile mechanical properties of ASTM D638 Types II and IV geometries as statistically similar to those of ASTM D3039 samples, impacting engineering designs with traditional and emerging manufacturing methods.     

The influence of processing conditions on the mechanical properties of poly(aryl-ether-ketone)/polybenzimidazole blends

Interest in developing high-performance blends for niche applications has grown significantly in efforts to meet ever-increasing harsh environment demands. In this work, four model poly(aryl-ether-ketone)/polybenzimidazole (PAEK/PBI) blends were chosen to study the influence of premixing methods, processing, and matrix polymers, on their mechanical properties. Among the model poly(ether ether ketone) (PEEK) and PBI blends, mechanical properties are greatly enhanced by melt premixing. The molding process mainly affects the matrix crystallinity, which in turn greatly influences fracture toughness of the blend. Poly(ether ketone ketone) (PEKK) and PBI blend exhibits a slightly lower tensile strength and fracture toughness than PEEK/PBI due to the differences in inherent properties of PEEK and PEKK matrices and their interfacial interaction with PBI. The processing−structure–property relationship of PAEK/PBI blends is established to help guide optimal design of high-performance polymer blends for structural applications. © 2020 Wiley Periodicals, Inc. J. Appl. Polym. Sci. 2020 , 137, 48966.     

Compressive behavior and energy absorption of polymeric lattice structures made by additive manufacturing

Frontiers of Mechanical Engineering - Lattice structures have numerous outstanding characteristics, such as light weight, high strength, excellent shock resistance, and highly efficient heat...     

Lean production system design for fishing net manufacturing using lean principles and simulation optimization

Value stream mapping (VSM) is a useful tool for describing the manufacturing state, especially for distinguishing between those activities that add value and those that do not. It can help in eliminating non-value activities and reducing the work in process (WIP) and thereby increase the service level. This research follows the guidelines for designing future state VSM. These guidelines consist of five factors which can be changed simply, without any investment. These five factors are (1) production unit; (2) pacemaker process; (3) number of batches; (4) production sequence; and (5) supermarket size. The five factors are applied to a fishing net manufacturing system. Using experimental design and a simulation optimizing tool, the five factors are optimized. The results show that the future state maps can increase service level and reduce WIP by at least 29.41% and 33.92% respectively. For the present study, the lean principles are innovatively adopted in solving a fishing net manufacturing system which is not a well-addressed problem in literature. In light of the promising empirical results, the proposed methodologies are also readily applicable to similar industries.     

偶联反应器格栅的制作和组装工艺

格栅支持板是换热器中的主要部件,尤其是大尺寸的格栅支持板,以前国内没有先例,如何编制好它的制作工艺和程序,并且在实际过程中得到很好的执行,遇到问题及时调整处置,对它的加工、组装、和整体安装有着特别重要的作用。