2/29/2024 0 Comments Metal punching net exporterRefined copper imports fell by 500,000 tonnes to 1.83 million tonnes in the January-July period. If China is experiencing a raw materials squeeze and falling zinc production, as the bull storyline runs, it's not yet evident from the country's zinc trade.Ĭhina's latest trade figures on copper offered little in the way of fresh insight but rather an extension of the twin trends of lower Chinese imports of refined metal and higher imports of raw materials. But the year-to-date total of 248,000 tonnes is still 20 percent off last year's pace.Ĭoncentrate imports are up 26 percent at 1.46 million tonnes this year, largely because of a close to 50 percent surge from Peru. Graphic on refined zinc imports: tmsnrt.rs/2gaVIrbīulls will take heart from July's refined metal imports of 68,000 tonnes, the highest monthly tally since March 2016. The zinc market is still waiting for the sort of import surge being experienced by sister metal lead. North Korean imports were 13,000 tonnes (bulk weight) in July - evidence that China's ban on imports has yet to bite. Imports of mined concentrates are tracking last year's levels, with North Korea remaining the second-largest supplier behind Russia. Cumulative imports of 59,000 tonnes in the January to July period are the highest since 2009. This year, however, it is importing what are, by the standards of this market, significant tonnages. The presented paper reveals that neural networks can accurately quantify the relationship between characteristic parameters of punching force curves and the mentioned me-chanical material properties.This is because of both solid demand for the battery input and to booming production in the DRC, up 27 percent to almost 40,000 tonnes in the first half of this year.Īmong the traditional industrial metals, the big Chinese trade surprise this year has been unglamorous lead.Ĭhina was a consistent net exporter of refined lead in the 2013-2016 period. Additionally, tensile tests were performed for these sheet metal materials to determine ultimate tensile strengths (Rm), yield strengths (Rp0.2, Re), uniform strains (Ag), elongations at break (At) and strain hardening exponents (n). Punching force curves were experimentally deter-mined for the sheet metal materials DP1200, DP1000, DP800, DP600, HX380LA, DC03 and DX54. As data basis for the adopted neural network, force curves were measured during punching of various sheet metal materials using a punching tool equipped with a direct force measurement device. Using software systems Python and Tensor-Flow, an artificial neural network was first set up to determine mechanical material parameters (out-put data) from punching force curves (input data). In this context, presented paper deals with a novel AI-based method for the direct determination of ma-terial parameters from measured punching force curves. The component quality achievable by these processes is strongly dependent on the properties of the sheet metal material, so that a permanent digital recording of material data offers high potential for monitoring each component produced. The ongoing digitization of production processes provides new possibilities and potentials for process monitoring of forming and stamping processes.
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