Currently digital transformation has a huge impact on human lives. Digital transformation does not just mean a transformation of a (non-) physical element to a digitally identifiable element. It focuses on the utilization of digital technology for transforming (improving) procedures or routines of business and operation. The manufacturing industry has been adopting the most recent digital technology, and lots of digital data are being created. To utilize the stored data, data analysis is essential. Because the manufacturing data is created in a different format at every manufacturing step, the integration of the data is always the bottleneck of the data analysis. Querying of the right data at the proper time is fundamental for high-level data analysis. The digital thread is introduced to provide the inter-reference of digital data based on a context. This paper proposes a digital thread framework for the machining process. The context of the proposed framework consists of the questions of how the product will be machined, how it is (was) being produced, and how it was made. A prototype software was developed to verify the proposed framework by implementing the creating, storing, and querying modules for simulation, monitoring, and inspection data.

A virtual machine tool, a computer simulation model of the machine motion and cutting process with a level of accuracy and consistency that can replace an accurate machine tool, is one of the critical digital transformation technologies in the manufacturing industry. During the machine development phase, cost and time can be reduced by evaluating machining efficiency and quality through virtual prototyping. In the machine application phase, virtual machine tools can be used to accurately assess the condition of equipment and processes by analyzing actual data combined with simulated data. This paper introduces a virtual machine tool system that can analyze the behavior of an accurate machine tool by integrating physical models of structure, numerical controller, and cutting process. The key features of the virtual machine tool, synchronous machining simulation, machining stability detection, machining error estimation, and part program optimization, were evaluated through various machining tests with a vertical 3-axis milling machine.

Among the monitoring technologies in the metal-cutting process, tool wear is the most critical monitoring factor in real machining sites. Extensive studies have been conducted to monitor equipment breakdown in real-time. For example, tool wear prediction studies using cutting force signals and deducting force coefficient values from the cutting process. However, due to many limitations, those wearable monitoring technologies have not been directly adopted in the field. This paper proposes a novel tool wear predictor using the cutting force coefficient with various cutting tools, and its validity evaluates through cutting tests. Tool wear prediction from the cutting force coefficient should conduct in real-time for adoption in real machining sites. Therefore, a real-time calculation algorithm of the cutting force coefficient and a tool wear estimation method proposes, and they compare with actual tool wear in cutting experiments for validation. Validation cutting tests are conducted with carbon steel and titanium, the most commonly used materials in real cutting sites. In future work, validation will be conducted with different materials and cutting tools, considering the application in real machining sites.

The cutting force signal has traditionally served as a reference in conducting the monitoring studies using a variety of sensors to identify the cutting phenomena. There have been continuing studies on how to monitor the cutting force indirectly. It is because it is easier to access when considering an application to the actual machining site. This paper discusses a method of indirectly monitoring the cutting force using the feed drive current to analyze the change in the trend of the cutting force over the lapse of machining time. This enables the analysis of the cutting force by separating it in the X and Y axes of the machining plane. To increase the discrimination of the signal related to the actual cutting phenomenon from the feed drive current signal, a bandpass filter was applied based on the tooth passing frequency. The relationship between the feed drive current and the cutting force analyzed from the machining signal of actual machining conditions was applied to convert the feed drive current into the cutting force. It has been verified through experiments that the cutting load can be estimated with markedly high accuracy as a physical quantity of force from the feed motor current.