The importance of cutting forces in machining has been emphasized for monitoring and optimizing cutting conditions, leading to various method to detecting cutting forces researched. Cutting forces can be directly measured using dynamometer or indirectly estimated using AE sensors and accelerometers, etc. However, these external sensors demand high costs and have accuracy limitations due to environment issues. To compensate for these drawbacks, utilizing internal signals of machine tool has been developed. Among these, using internal electrical signals of machine tool is representative. In commercial machine tools, cutting forces are often estimated through current measurements. However, due to the characteristics of the spindle motor, electrical properties such as slip, power factor, and efficiency vary with the load, resulting in relatively lower accuracy. This study introduces current-based method considering characteristics of motor and power-based method for estimating cutting forces and compare accuracy of those methods with the measurements from dynamometer respectively.

Digital twin technology offers the advantage of monitoring the status of equipment, systems, and more in a virtual environment, allowing validation through simulation. This technology has found numerous applications in the industrial robotics field, driven by recent advancements in the manufacturing industry. Consequently, predicting machining quality using digital twin technology is imperative for ensuring high-quality processed goods. In this study, we developed a digital twin program based on a cutting-force physical model and created a performance enhancement module that allows the visualization of material removal for user convenience. The predicted cutting forces from both conventional CNC and the physical model demonstrate a high accuracy of within 2%. Within the digital twin environment, the error rate for the robotic drilling process is 13.5%. Building upon this, we developed and validated a module for material removal visualization, aiming to increase convenience for on-site operators.

With the advent of the 4th industrial revolution, advanced digital manufacturing technologies are actively developed to strengthen manufacturing competitiveness. Smart factories require a real-time digital twin including a Cyber-Physical System (CPS) of machines and processes and intelligent technologies based on the CPS. To predict machining quality and optimize machines and processes, it is necessary to analyze the cutting force during machining. Therefore, for real-time digital twin, a fast cutting force simulation model that receives information such as the positions of the feed axes in short time intervals from the CNC and calculates the cutting force until the next information is input is required. This paper proposes a voxel-based fast cutting force simulation in NC milling for real-time digital twin. The proposed simulation model quickly calculates the cutting force by using only information of voxel elements removed by each tool edge without complicated Cutter-Workpiece Engagement (CWE) and chip thickness calculations in previous studies. To verify the performance of the developed simulation, experimental machining was performed and the measured cutting force and simulated cutting force were compared. It was demonstrated that the proposed model can successfully predict the cutting force 3.5 times faster than the actual process.

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.

As products life cycles are becoming shorter, the reduction of die and mold manufacturing cost and time is becoming more crucial in the machinery, automotive, and electronics industries. Over the past decades, many initiatives have been made to develop high performance free-machining steels without significant degradation of mechanical properties. To develop a modified AISI P20 free-machining steel, we studied the effects of B, N, and S additives on the variations of the cutting forces and metal structures such as grain size, density, and distribution of free-machining inclusions. From a set of experiments, it was observed that an appropriate addition of B and N additives reduces the resulting cutting force by approximately 6.3% and delays the tool wear progress. During the solidification B and N additives form hBN precipitates, with a layered and planar structure, within the steel matrix. The hBN precipitates’ weak shear strength results in lowering the required milling force. It is also confirmed that machinability is prominently improved when a large number of microsized hBN precipitates are distributed uniformly in the steel matrix. This study could contribute to the development of high performance BN-added free-machining steels for die and mold applications.

Heat treated die steels are durable and resistant to abrasion. However, machining them is not very efficient. To improve the machinability using the end-milling process for high hardness die steels, we proposed an end-mill shape through analysis of the cutting force and simulation. In this study, we determined the important factors affecting the cutting force among several elements of end-mill shape using the customized cutting simulator and the design of experiments (DOE) technique. After the selecting the effective factors based on the simulation and DOE results, various end-mills were fabricated by adjusting the parameters. In the experiment, the cutting force between 1 pass and 40 pass were measured and the average value compared with each end-mill shape. Edge radius, radial relief angle and axial relief angle were selected as a key parameters and optimized by measuring the cutting force through repeated and well controlled experiments. In conclusion, the effective factors were confirmed and we could now determine the optimum shape of end-mill to minimize the cutting force for high hardness die steels.

This paper is a study of the machining characteristics, cutting force and surface roughness of a turning center by laserassisted machining. The laser-assisted machining (LAM) is an effective method to improve the machinability of difficult-tocut materials. The LAM has recently been studied for various machining processes, but the research on the threedimensional and turning-center machining is still insufficient. In this study, a machining experiment of the turning-center process was performed by the laser-assisted machining with Inconel 718. Before the machining experiment, performed to thermal analysis was for a selected to effective depth of cut. The cutting force and surface roughness were compared and analyzed. The machining experiment confirmed that the machinability was improved in the LAM.