Abstract
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.
Highlights
Today, due to short product lead times, small-quantity batch production, diversification of consumer needs, and irregular demand fluctuations, manufacturing companies are trying to achieve innovation, such as flexible and predictive production, in contrast to the mass production that is a typical manufacturing method [1,2]
Cyber-physical production systems (CPPS)-related studies are conducted on high-level abstract models, this study describes more specific architectural frameworks, use cases, usable software, and analytical methodologies
The quality of metal casting is mainly affected by the variable corresponding to mold temperature
Summary
Due to short product lead times, small-quantity batch production, diversification of consumer needs, and irregular demand fluctuations, manufacturing companies are trying to achieve innovation, such as flexible and predictive production, in contrast to the mass production that is a typical manufacturing method [1,2]. In terms of technologies that support manufacturing innovation, information and communication technologies (ICT) including enterprise resource planning, manufacturing execution systems (MES), and programmable logic controller automated factories significantly improve productivity. Smart factories allow the collection of massive amounts of in-plant data through real-time synchronization of the factory components and information systems, and they improve quality and productivity through smart and flexible responses to abnormal situations that occur in a plant [5,6,7]. CPS, CPPS, IoT, big data, and AI are the core technologies in smart factories, most studies have examined high-level reference models [11,12,13,14], including the architecture of the smart factories and CPS.
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