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.

Homogenization of the interfacial bonding of compound-cast AA7075/6060 bilayer billets by co-extrusion

A process chain of compound casting and co-extrusion of AA7075/6060 bilayer billets is introduced to manufacture hybrid components with strength in the core and good corrosion-resistance in the shell. Using optimized compound casting parameter, metallurgical bonding between the shell AA6060 and the core AA7075 can be achieved through remelting and recrystallization of the substrate AA7075. The locally unequal thermal conditions at the interface induces partially weak bonding. The bonding strength in greater distance from the casting gate is generally lower. Hot extrusion is applied to improve the interfacial bonding. Comparisons of the microstructure and the shear strength between as-cast billet and extrudate present the homogenization of the interfacial bonding through the process chain.

Establishment of an IoT-based smart factory and data analysis model for the quality management of SMEs die-casting companies in Korea

In this research, an Internet of things–based smart factory was established for a die-casting company that produces automobile parts, and the effect of casting parameters on quality was analyzed using data collected from the system. Most of the die-casting industry in Korea consists of small- and medium-sized enterprises with inferior finances and skeptical views about the establishment of a smart factory. In response, the Korean government is providing various types of support to spread the implementation of smart factories for small- and medium-sized enterprises. Although small- and medium-sized enterprises have become more active in establishing smart factories according to the government policies, the effect of smart factories requires real-time monitoring. A monitoring system has been built but the data collected are not being utilized properly. Therefore, it is necessary to establish a system suitable for the die-casting environment and data analysis purposes and to utilize it to enable the analysis of data. To this end, we established to smart factory that provides data based on the Internet of things. Among the data collected, casting parameter data were analyzed through a data mining technique to establish a relationship between casting parameters and the quality of production. It is expected that a method of systematic implementation will be provided to die-casting companies that want to build smart factories in the future and that a plan for managing casting parameter by-product will be established. In addition, algorithms that can solve the problem of multi-collinearity among the casting parameters and aid in the development of new products are needed to detect optimum casting parameters.

An analysis of brass casting parameter process using sand casting mold

This research tried to analyze the existing casting process ofganto recycled brass by varying the liquid pressure parameters ofdifferent induction metals, followed by the pouring of liquid metal intothe mold. The method used in this research was descriptive qualitativewith aesthetic analysis. As a result, metal casting can be defined as theprocess of molten metal, poured into a mold, then allowed to be cooland freeze. In conclusion, Ganto is one of the objects of brass castingthat continues to produce the brass handicrafts in Jorong Kapalo Koto,especially the production center of Zikri ganto.

Simulation of Thermal Stress and Hot Tearing in Engine Block Casting

Using finite element method, a numerical simulation is presented for engine block casting. The filling process analysis provides an accurate initial temperature field to the thermal stress and hot tearing investigations; meanwhile, it is useful in prediction of cooling shut. The thermal stress distribution after solidification of the casting is solved. To assess the hot tearing, the RGD criterion is adopted. Both hot tearing indicator and thermal stress can be used to estimate the susceptibility of hot tearing. The results can offer a reference to casting parameter design for engine block.