Data-Analytics-Based Factory Operation Strategies For Die-Casting Quality Enhancement

This paper proposes data-analytics-based factory operation strategies for the quality enhancement of die-casting. We first define the four main problems of die casting that result in lower quality: [P1] gaps between the input and output casting parameter values, [P2] occurrence of preheat shots, [P3] lateness of defect distinction, and [P4] worker-experience-based casting parameter tuning. To address these four problems, we derived seven tasks that should be conducted during factory operation: [T1] implementation of exploratory data analysis (EDA) for investigating the trends and correlations between data, [T2] deduction of the optimal casting parameter output values for the production of fair-quality products, [T3] deduction of the upper and lower control limits for casting parameter input–output gap management, [T4] development of a preheat shot diagnosis algorithm, [T5] development of a defect prediction algorithm, [T6] development of a defect cause diagnosis algorithm, and [T7] development of a casting parameter tuning algorithm. The details of the proposed data-analytics-based factory operation strategies with regard to the casting parameter input and output data, data preprocessing, data analytics method used, and implementation are presented and discussed. Finally, a case study of a die-casting factory in South Korea that has adopted the proposed strategies is introduced.

Roadmap Greenhouse Gas Neutral Sugar Industry in Germany – Pathways to Climate Neutrality 2050

To achieve greenhouse gas-neutral sugar production in Germany, a general realignment of energy production and energy sources is required. Three different solutions were investigated for this purpose. In two of the scenarios, beet pulp currently used for animal feed production would be used. They could be used as an energy source either by fermentation into biogas or as a solid fuel (in addition to wood). The third possible path would be to switch to largely electric factory operation using electrical energy from renewable sources.

Research on the E-Commerce Model of Small and Medium Enterprises

In this paper, the SME e-business financing for the study, analysis of business operation mode based e-commerce platform, focusing on big data platform based in the cloud as a means of e-business financing model, to a certain extent to solve the financing market asymmetric information problems in order to achieve the factory operation SME financing; while its limitations, namely the closed nature of the platform, the limited nature of the subject's participation as a breakthrough, build e-commerce platform as the core to the line a variety of financial non-financial large financial institutions to participate in party mode, to solve financing problems of SMEs. The end of the analysis of the profitability of the parties involved, as well as problems and deficiencies that may be encountered, verify its economic viability and future development of the platform to do a certain degree of outlook.