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Today, manufacturing industries are trying to improve their competitiveness by combining manufacturing per se with information technology. Virtual reality is being used in product development processes in manufacturing enterprises as a helpful technology to achieve rapid consolidation of information and decision-making through visualization and experience. In this article, 154 articles relevant to virtual reality's application to manufacturing were surveyed and analyzed. For this, (1) an analysis map was created, based on a virtual reality technology classification and the new product development process; (2) the articles investigated were located on the map; and (3) bibliometric analyses were carried out. Trends in past and present research were examined and future virtual reality research directions and application plans for manufacturing enterprises are discussed.

The prediction of internal defects of metal casting immediately after the casting process saves unnecessary time and money by reducing the amount of inputs into the next stage, such as the machining process, and enables flexible scheduling. Cyber-physical production systems (CPPS) perfectly fulfill the aforementioned requirements. This study deals with the implementation of CPPS in a real factory to predict the quality of metal casting and operation control. First, a CPPS architecture framework for quality prediction and operation control in metal-casting production was designed. The framework describes collaboration among internet of things (IoT), artificial intelligence, simulations, manufacturing execution systems, and advanced planning and scheduling systems. Subsequently, the implementation of the CPPS in actual plants is described. Temperature is a major factor that affects casting quality, and thus, temperature sensors and IoT communication devices were attached to casting machines. The well-known NoSQL database, HBase and the high-speed processing/analysis tool, Spark, are used for IoT repository and data pre-processing, respectively. Many machine learning algorithms such as decision tree, random forest, artificial neural network, and support vector machine were used for quality prediction and compared with R software. Finally, the operation of the entire system is demonstrated through a CPPS dashboard. In an era in which most CPPS-related studies are conducted on high-level abstract models, this study describes more specific architectural frameworks, use cases, usable software, and analytical methodologies. In addition, this study verifies the usefulness of CPPS by estimating quantitative effects. This is expected to contribute to the proliferation of CPPS in the industry.

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