In numerical control (NC)-based machining, NC data-based tool paths affect both quality and productivity. NC data are generated according to cutting conditions. However, NC data causing excessive cutting load can accelerate tool wear and even result in tool damage. In the opposite case, increasing machining time can affect productivity. NC data can influence surface quality from the perspective of cutting dynamics according to machine tool–material–tool combination. There have been a lot of studies on tool-path optimization. However, it is impossible to perfectly predict cutting dynamics such as tool wear, material non-uniformity, chatter, and spindle deformation. In fact, such prediction-based tool-path optimization can cause errors. Therefore, this study attempts to synchronize spindle load and NC data and uniformize the machining load through the analysis of stored data using digital-twin technology, which stores and manages machining history. Uniformizing machining load can reduce rapid traverse in the event of no load, feed rate in an overload area, and shock on a tool when the tool and material are met by adding approach feed. Analyzing results of the attempts proposed in this paper, the chatter was completely removed in the machining with D100 and D16, although some chatter remained in the machining with D25 and D16R3 tools. In addition, the processing time could be reduced from a minimum of 7% to a maximum of 50% after optimization.

In a cyber-physical system, enterprise applications, control functions, and physical manufacturing assets are interconnected to seamlessly exchange information. However, the software and hardware often differ in terms of their data interfaces, data formats and semantics. Conventional enterprise applications usually employ file-based interfaces (e.g., Excel); control applications always support Ethernet-based protocols (e.g., OPC UA). Thus, data interoperability is currently lacking. The asset administration shell (AAS) is a new approach for achieving data interoperability across the automation pyramid as the AASX data format represents AAS information and supports data communication via OPC UA. In this study, we first provide insights into the AASX format and give exemplary applications on AASX; we then present an AASX-based solution for bidirectional data exchange between enterprise and control applications. In this solution, the conversion between AASX and Excel spreadsheets is completed to enable interoperable data exchange. Finally, a data exchange scenario for motor control is constructed to validate the effectiveness of the solution. The result shows no data were lost during the two-way conversions.

In many small- and medium-sized enterprises (SMEs), defective products are still manually verified in the manufacturing process. Recently, image classification applying deep learning technology has been successful in classifying images of defective and intact products, although there are few cases of utilizing it in practice. SMEs have limited resources; therefore, it is crucial to make careful decisions when applying new methods. We investigated sample size sensitivity to determine the stable performance of deep learning models when applied to the real world. A simple sequential model was constructed, and the dataset was reconstructed into several sizes. For each case, we observed its statistical indicators, such as accuracy, recall, precision, and F1 score, on the same test dataset. Additionally, the loss, accuracy, and AUROC values for the validation dataset were investigated during training. As a result of the conducted research, we were able to confirm that, with 1000 data points or more, the accuracy exceeded 97%. However, more than 5000 cases were required to achieve stability in the model, which had little possibility of overfitting.

The process of assembling the engines used in medium and large ships appears to be a continuous production process, but it is not the process through which numerous workers perform work with automated equipment or simple assembly work, but rather a process through which skilled workers, who can effectively perform various tasks, perform multiple aspects of the assembly process. Due to this characteristic, it was difficult to assess the work process rate during the engine assembly process through real-time analysis of the process because it was difficult to shorten the lead time or improve the process, and most of stages of the process depend on manual reports or operators’ work data input. Therefore, in this study, we developed and applied a system capable of recognizing the current process rate by identifying specific engine parts using YOLO.

The proliferation of 5G networks has revolutionized wireless communication by delivering enhanced speeds, ultra-low latency, and widespread connectivity. However, in heterogeneous cloud radio access networks (H-CRAN), efficiently managing inter-cell interference while ensuring energy conservation remains a critical challenge. This paper presents a novel energy-efficient, dynamic enhanced inter-cell interference coordination (eICIC) scheme based on deep reinforcement learning (DRL). Unlike conventional approaches that focus primarily on optimizing parameters such as almost blank subframe (ABS) ratios and bias offsets (BOs), our work introduces the transmission power during ABS subframes (TPA) and the channel quality indicator (CQI) threshold of victim user equipments (CTV) into the optimization process. Additionally, this approach uniquely integrates energy consumption into the scheme, addressing both performance and sustainability concerns. By modeling key factors such as signal-to-interference-plus-noise ratio (SINR) and service rates, we introduce the concept of energy-utility efficiency to balance energy savings with quality of service (QoS). Simulation results demonstrate that the proposed scheme achieves up to $70\%$ energy savings while enhancing QoS satisfaction, showcasing its potential to significantly improve the efficiency and sustainability of future 5G H-CRAN deployments.