This article discusses the results of research aimed at solving the problem of increasing the operational durability of dressed astrakhan fur in a fur coat. To solve it, an approach was chosen based ...on an understanding of the physicochemical processes occurring in the material under the influence of operational factors. The use of the developed method, which gives an unambiguous assessment of the material, made it possible to investigate the dependence of the durability of the material on the parameters of the technological process of its manufacture.
The primary aim of this research is to propose a revised triple sampling (TS)
chart, where the derivations of new formulae for computing the average run length of the triple sampling (TS)
chart ...correctly are provided. The secondary aim is to develop the revised TS
chart with estimated process parameters. The revised TS
charts are compared with the double sampling (DS)
, two stage adaptive sample size (AS
2
)
and three stage adaptive sample size (AS
3
)
charts when process parameters are known and estimated using the average run length (ARL), average number of observations to signal (ANOS), average of the average run lengths (AARL), standard deviation of the average run lengths (SDARL), average of the average number of observations to signal (AANOS) and standard deviation of the average number of observations to signal (SDANOS) criteria, where the revised TS
charts are found to be superior. Additionally, a table giving the minimum number of Phase-I samples for estimating the process mean so that the revised TS
chart with estimated process parameters has the desired in-control AARL and AANOS performances is provided.
•To have consistent results an average surface Sa was selected and measured as a target.•Artificial neural network was used as an accurate tool for characterizing the interaction and effect of ...parameters on the surface of SLM parts.•The effect of SLM process parameters and heat treatment on the values of average surface were modeled accurately.
In this paper, we propose a model to predict the average surface roughness (Sa) and analyse the effect of related process parameters on laser powder bed fusion (LPBF) selective laser melting (SLM) of Ti-6Al-4 V. The additive manufacturing (AM) process has various independent parameters that affect the quality of the produced parts and is complex to analyse. Although the process parameters can be selected separately in LPBF, they do however affect each other. Therefore, large adjustments of process parameters is not possible due to the negative effect they have on each other which can lead to problems such as cracks, balling, unmelted powders, porosity, and distortion. A range of process parameters using Taguchi L25 design of experiment (DOE) with five repetitions for each sample has been selected. Then, an artificial neural network (ANN) is applied to the model to predict the value of (arithmetical mean height)/(average surface roughness) (Sa). The selected processing parameters are laser power, scan speed, hatch spacing, laser pattern increment angle, and heat treatment (HT) condition. The present work revolves around ANN modeling and using a wide parameter range and a large number of test samples under ASTM standards as well as adding HT to the DOE to analyse the simultaneous effect of HT and changing process parameters on surface characteristics. A large and precise data set with high generality and reliability obtained by 3750 profilometries on 125 samples. The contribution of this paper is using ANN as an accurate tool in surface modeling and characterizing the effective parameters on the surface of LPBF parts. The existence phenomena and governing factors were explained by introducing new parametric mechanisms in rheology of melting pool. In AM of metals, the variation of average roughness in overlap of hatches can be 5–7 times higher than the centre of the track. Therefore, Sa was selected to have consistency in the measured roughness values. Results showed heat treatment above beta phase transus leads to a local flow of material at the surface causing an increase of Sa. The ranking of influential factors on Sa from the highest to the lowest was found to be: heat treatment > laser power > scan pattern angle > hatch space > scan speed.
Fused deposition modelling is a rapidly growing additive manufacturing technology due to its ability to build functional parts having complex geometries. The mechanical properties of a built part ...depend on several process parameters. The aim of this study is to characterize the effect of build orientation, layer thickness and feed rate on the mechanical performance of PLA samples manufactured with a low cost 3D printer. Tensile and three-point bending tests are carried out to determine the mechanical response of the printed specimens.
Due to the layer-by-layer process, 3D printed samples exhibit anisotropic behaviour. Upright orientation shows the lowest mechanical properties. On the other hand, on-edge and flat orientation show the highest strength and stiffness. From a layer thickness and feed rate point of view, it is observed that ductility decreases as layer thickness and feed rate increase. In addition, the mechanical properties increase as layer thickness increases and decrease as the feed rate increases for the upright orientation. However, the variations in mechanical properties with layer thickness and feed rate are of slight significance for on-edge and flat orientations, except in the particular case of low layer thickness. Finally, the practicality of the results is assessed by testing an evaluation structure.
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•The effect of 3D printing process parameters on the mechanical performance of PLA samples is assessed.•On-edge oriented samples show the optimal mechanical performance.•Ductility decreases as layer thickness and feed rate increase.•Low layer thickness and high feed rate values are recommended for the optimal mechanical performance.
Three dimensional (3D) food printing is being widely investigated in food sector recent years due to its multiple advantages such as customized food designs, personalized nutrition, simplifying ...supply chain, and broadening of the available food material.
Currently, 3D printing is being applied in food areas such as military and space food, elderly food, sweets food. An accurate and precise printing is critical to a successful and smooth printing. In this paper, we collect and analyze the information on how to achieve a precise and accurate food printing, and review the application of 3D printing in several food areas, as well as give some proposals and provide a critical insight into the trends and challenges to 3D food printing.
To realize an accurate and precise printing, three main aspects should be investigated considerably: material properties, process parameters, and post-processing methods. We emphasize that the factors below should be given special attention to achieve a successful printing: rheological properties, binding mechanisms, thermodynamic properties, pre-treatment and post-processing methods. In addition, there are three challenges on 3D food printing: 1) printing precision and accuracy 2) process productivity and 3) production of colorful, multi-flavor, multi-structure products. A broad application of this technique is expected once these challenges are addressed.
•Factors affecting 3D food printing precision were discussed.•Applications of 3D printing in food sector were reviewed.•Challenges to 3D food printing were proposed.
Current trends in pharmaceutical wet granulation have been shifted from traditional approach to systematic and modern approaches, enabling the production of highly engineered product. In this ...context, quality by design (QbD), modelling, and simulation gradually become important tools in the pharmaceutical industry. In this review, several aspects of high-shear wet granulation (HSWG), such as process and formulation variables are introduced and discussed. In addition, potential critical process parameters of various granulation technologies, such as HSWG, twin-screw granulation (TSG), fluid-bed granulation (FBG), and fluid-bed melt granulation, are introduced. The significance of various off-line and on-line/in-line tools for the characterization of granules and tablet attributes are also highlighted along with their application. Moreover, various mechanistic and semi-mechanistic models that are used to improve the granulation process have been summarized as the predictive models. Overall, this review may inform and guide formulation scientists about the systematic approaches of pharmaceutical wet granulation process to improve the formulation and process design.
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•Process and formulation parameters play a vital role on kinetics of wet granulation•Different wet granulation techniques are discussed and compared.•Importance of Quality by Design in wet granulation process was discussed•Modelling and simulation are important for process control of wet granulation•Granule attributes and their characterization tools were discussed
•Laser powder bed fusion Taguchi experiments were conducted with 316L stainless steel.•A machine learning framework, Kriging-WOA, was proposed for the optimization of process parameters.•Better ...surface roughness and dimensional accuracy were obtained after the optimization.•Confirmation experiments revealed the effectiveness and reliability of the built model.•The main effects and contribution rates of process parameters are analyzed.
Laser powder bed fusion (LPBF) is one of the most promising additive manufacturing technologies. It has been utilized in the high level and stringent requirements fields such as aerospace and biomedicine industries. However, compared to subtractive manufacturing, the relatively poor surface finish and dimensional accuracy of the LPBF part hamper its widespread applications. In this work, a data-driven framework is proposed to obtain optimal process parameters of LPBF to get satisfactory surface roughness and dimensional accuracy. The effects of key process parameters on the surface roughness and dimensional accuracy are analyzed. Specifically, a machine learning technique is defined to reflect the dimensional accuracy and the surface roughness of the as-built products under different combinations of process parameters. Considering the limited experimental data, a machine learning model is introduced to predict the surface roughness and dimensional accuracy in the whole process parameters space. Then the predicted value is considered as an objective value when using the whale optimization algorithm (WOA) to search the global optimal process parameters. In the verification experiments, LPBF parts with better surface finish and dimensional accuracy were obtained with optimized process parameters which indicates that the optimized results are consistent with the experimental results.
Based on the principles and metrics of green chemistry and sustainable development, biocatalysis is both a green and sustainable technology. This is largely a result of the spectacular advances in ...molecular biology and biotechnology achieved in the past two decades. Protein engineering has enabled the optimization of existing enzymes and the invention of entirely new biocatalytic reactions that were previously unknown in Nature. It is now eminently feasible to develop enzymatic transformations to fit predefined parameters, resulting in processes that are truly sustainable by design. This approach has successfully been applied, for example, in the industrial synthesis of active pharmaceutical ingredients. In addition to the use of protein engineering, other aspects of biocatalysis engineering, such as substrate, medium, and reactor engineering, can be utilized to improve the efficiency and cost-effectiveness and, hence, the sustainability of biocatalytic reactions. Furthermore, immobilization of an enzyme can improve its stability and enable its reuse multiple times, resulting in better performance and commercial viability. Consequently, biocatalysis is being widely applied in the production of pharmaceuticals and some commodity chemicals. Moreover, its broader application will be further stimulated in the future by the emerging biobased economy.
Abstract
The bushing is a kind of ring sleeve which acts as a liner outside the mechanical parts, which needs good strength, hardness and fatigue resistance. In this paper, the copper bushing was ...prepared by spinning forming method, and the process parameters of spinning were explored. According to the results of material thermal simulation and test, the conclusion is that the spinning process of copper bushing needs to be carried out in two passes by reverse spinning method. The thinning rate is 30% and 25% respectively. The gap between the mandrel and the roller is 10mm, the feed ratio is 1mm/r, and the spinning temperature is 250°C.
In order to manufacture structured surfaces, firstly, the abrasive stone model with staggered abrasive particles is established with mathematical methods. Secondly, the motion of abrasive particles ...on the abrasive stone is designed. Finally, the surface morphology of the workpiece is simulated. The simulated workpiece morphology under different machining parameters is compared, and the influence of different machining parameters on the workpiece surface morphology is obtained. The results indicate that the microstructured surface can be lapped by using the abrasive stone of staggered abrasive particles. As the amplitude and frequency of the abrasive stone increase, the number of protrusions increases, and the area of individual protrusions decreases.
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