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.

Recently, manufacturing concepts, such as personalized production and distributed manufacturing, have attracted attention owing to the ongoing revolution in industrial technology. Connected micro smart factories in factory-as-a-service system with these new manufacturing paradigms and Industrial Internet of Things (IIoT) are inefficient in terms of cost and production. To solve these problems, a digital twin, which uses a digital representation of a process, with the same configuration of manufacturing elements, synchronized information, and functional units, was designed and implemented. The digital twin utilizes the latest information from the Internet to gather data from IIoT devices and interoperates in a variety of applications. In addition, it derives the components of a detailed design of the digital twin application, to which it performs procedure definition. This research differs from other digital twin studies that concentrate on the prognostic health management of only a single machine. This study could help managers organize the benefits of utilization through a digital twin based on a hierarchy as they could receive real-time monitoring of the present, tracking information from the past, and operational decision-making support for the future. In addition, the proposed application reduces the cost and production inefficiencies, ultimately resulting in the efficient operation of a manufacturing system.

Personalised production allows the supply chain (SC) to exist in various dynamic fluctuations within a make-to-order (MTO) environment. An SC for personalised production has redundant inventory and operation capacity; therefore, it requires a system that can achieve recoverability for operation resilience. Thus, a standalone cyber physical system (CPS) has limitation for SC control with MTO. To solve this problem, the CPS must be coordinated, and a systematic approach is required. This study proposes a cyber physical logistics system (CPLS) that is coordinated with the agent cyber physical production systems in a multi-level CPS structure. This multi-level architectural framework is designed to provide technical functionalities for resilient SC control. The service composition procedures of technical functionalities on the distributed digital twin (DT) simulation are divided into type and instance stages. The operation procedures of the DT application and technical functionality of the DT engine are suggested. The proposed CPLS has appropriate service composition and operation for the bullwhip and ripple effects, which are the two main SC-related problems. This study illustrates an early case of CPLS that can minimise differences among assets using distributed DT simulation; furthermore, the study establishes an SC and production plan based on the DT simulation results.

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.