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      Analysis of a pneumatic seeder equipped with an improved planting disc

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          Abstract

          The article analyzes the results of field experiments of a pneumatic seeder that accurately plants a bare cotton seed equipped with an improved planting disc. The type and parameters of a pneumatic seeding machine that precisely slots and plants bare seeds suitable for the harsh soil and climatic conditions existing in the world have been studied through field trials to ensure that an economic effect can be achieved with adequate research. The experiments were carried out during the spring sowing period. In addition, the parameters of an improved pneumatic seeder drive that accurately shows bare cotton seed have been scientifically substantiated. The article presents conclusions based on the results obtained.

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          Application of a neural network for predicting cutting surface quality of punching processes based on tooling parameters

          A Schenek, M Görz, K R Riedmüller … (2023)
          Punching represents one of the most frequently used manufacturing processes in the sheet metal processing industry. Thereby, one of the most important quality criteria for such punching processes constitutes the geometric shape of the cutting surface. High cutting surface qualities are usually characterized by the lowest possible edge draw in, a high clean cut, as well as a small fracture surface and a low burr height. In this respect, conventional punching processes can only produce clean cut proportions (CCP) up to 20-50% of the sheet thickness. By means of the so called “concave punch nose design” developed at the Institute for Metal Forming Technology, the geometry of conventional punches could be optimized in such a way that the clean-cut proportion along the cutting surface is significantly increased. The findings reported about in this paper show that the quality parameters of the sheared component edges thereby show highly nonlinear relationships to the tooling and sheet metal material parameters used. In order to quantify these effects, the data from several numerical punching simulations is used to pretrain an artificial neural network (ANN). Input data for the neural network included features of the punch geometry, the size of the clearance, the sheet thickness and the material data of the semi-finished product. The output of the neural network is precise predictions of the achievable cutting surface quality parameters. The experimentally validated findings presented in this paper show that the process design for novel punching processes such as cutting with concave punch nose design is possible, when using a modern machine learning approach.
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            Experiments and simulations of the drawing envelope of commercially pure aluminum

            Adrian J. Carter, Kelin Chen, Yannis P. Korkolis (2023)
            The range of blank-holding force (BHF) for which deep drawing of AA1100-H24 is successful is investigated using a combination of experiments and simulations. The experiments involve circular blanks of three different diameters: 35 mm, 37 mm and 40 mm. The thickness of the blanks is 0.51 mm. These are drawn with a punch of 20 mm diameter, i.e., at drawing ratios of 1.75, 1.85 and 2.0, respectively, using a custom, modular forming apparatus where the BHF can be controlled at will, between 0 and 2,400 N. The experiments are performed in an Instron 8872 servo-hydraulic frame that allows precise measurements of the punch force and displacement. The working envelopes are determined experimentally in the space of BHF and draw depth, which can be categorized into three regimes: wrinkling, safe and tearing. It is found that grey zones exist between the three regimes. These experiments are then simulated in Abaqus/Standard, using two types of elements: axisymmetric and shell. In the latter case, plastic anisotropy is introduced, using the Yld2000-2D anisotropic yield function. Wrinkling and tearing failure are triggered by inducing suitable geometric imperfections. It is shown that the models are able to reproduce the experiments well. This serves as a verification of the modelling framework, which can then be used for the simulations of more complex forming processes.
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              Wastewater treatment in Central Asia: A review of papers from the Scopus database published in English of 2000−2020

              J. Musirmonov, L. Gafurova, O. Ergasheva … (2023)
              Today the world is facing a major problem of getting rid of wastewater safely so that it does not disrupt the environment. This study analyzed global research trends through wastewater treatment technologies from 2000 to 2020 using Scopus databases. The objective of this article is to carry out a bibliometric analysis regarding the production and impact of scientific research related to wastewater treatment indexed in the Scopus databases. The purpose is to know key aspects such as the progression of production over the two decades, maturity in research, coverage of the subject, identifying the most discussed topics and as a result of identifying the gaps, the most relevant authors and the core of journals through which knowledge in this area is disseminated, as well as its impact through the analysis of citations. Scopus revealed a total of 44 journal articles, 19 conference proceedings, and 1 review paper during this period, with the keywords’ wastewater, treatment. As per the analysis, Kazakhstan and Uzbekistan painted the greatest publications connected with wastewater.
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                Author and article information

                Journal
                Title: BIO Web of Conferences
                Abbreviated Title: BIO Web Conf.
                Publisher: EDP Sciences
                ISSN (Electronic): 2117-4458
                Publication date Created: 2024
                Publication date (Electronic): April 26 2024
                Publication date (Print): 2024
                Volume: 105
                Page: 01024
                Article
                DOI: 10.1051/bioconf/202410501024
                SO-VID: 8deefafb-80b0-4462-b05d-87a76ee64c10
                Copyright © © 2024
                License:

                https://creativecommons.org/licenses/by/4.0/

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