Carbon Fiber Reinforced Plastics (CFRP) is a material developed for its high strength and light weight in a broad variety of industries including aerospace, automotive, and leisure. Due to the rapid molding cycle time, high-pressure resin transfer molding (HP-RTM) processes are prone to molding defects and susceptible to various process variables such as the resin injection rate, pressure and temperature in the mold, vacuum, end-gap, pressing force, and binder. In recent years, process monitoring technology with various sensors has been applied to stabilize the HP-RTM process and control process variables. The field-programmable gate array (FPGA) based embedded monitoring system proposed in this study enabled high-speed real-time signal processing with multiple sensors, namely pressure, temperature, and linear variable differential transformer (LVDT), and proved feasibility in the field. In the HP-RTM process, the impregnation and curing of the resin were predicted from the cavity pressure and temperature variations during the injection and curing stages. In addition, the thickness of the CFRP specimen was deduced from the change in the end-gap through the detection of the LVDT signal. Therefore, the causes of molding defects were analyzed through process monitoring and the influence of molding defects on the molding quality of CFRP was investigated.

The Kyropoulos (Ky) and Czochralski (Cz) methods of crystal growth are used for large-diameter single crystals. The seeding process in these methods must induce initial crystallization by initiating contact between the seed crystals and the surface of the melted material. In the Ky and Cz methods, the seeding process lays the foundation for ingot growth during the entire growth process. When any defect occurs in this process, it is likely to spread to the entire ingot. In this paper, a vision system was constructed for auto seeding and for observing the surface of the melt in the Ky method. An algorithm was developed to detect the time when the internal convection of the melt is stabilized by observing the shape of the spoke pattern on the melt material surface. Then, the vision system and algorithm were applied to the growth furnace, and the possibility of process automation was examined for sapphire growth. To confirm that the convection of the melt was stabilized, the position of the island (i.e., the center of a spoke pattern) was detected using the vision system and image processing. When the observed coordinates for the center of the island were compared with the coordinates detected from the image processing algorithm, there was an average error of 1.87 mm (based on an image with 1024 × 768 pixels).

Machining of difficult-to-cut materials such as titanium alloys, stainless steel, Inconel, ceramic, glass, and carbon fiber-reinforced plastics used in the aerospace, automobile, and medical industries is being actively researched. One non-traditional machining method involves the use of an abrasive waterjet, in which ultra-high-pressure water and abrasive particles are mixed and then ejected through a nozzle, and the thin jet stream cuts materials. The nozzle greatly affects the machining quality, as does the cutting tool of general machining, so it is very important to monitor the nozzle condition. If the nozzle is damaged or worn, or if the bore size increases or the bore becomes clogged with abrasive, the material may not be cut, or the surface quality of the cut may deteriorate. Here, we develop a nozzle monitoring system employing an acoustic emission sensor that detects the nozzle condition in real time.

With the increasing demand to find new energy resources instead of using fossil fuels, for the protection of the environment, one of most attractive areas in renewable energy is hydrogen. Hydrogen gas has high energy efficiency, generates the least greenhouse gases and produces no noise. Moreover, overland transportation industries have been researching and developing hydrogen gas storage systems worldwide. Such a manner of fuel system consists of a hydrogen gas tank, high pressure regulator and solenoid valve, etc. In this paper, a test bed is suggested for ultra-high pressure systems integrating a valve and regulator with precision control. We carried out performance tests by applying voltage with wide ranges of input pressure, response time and output flow rate and pulsation repetition tests considering increases in temperature, etc. Moreover, the results indicated good potential for application in fuel charging and transporting commercial hydrogen vehicles.

In manufacturing automation, accurately determining the optimal tool replacement timing is critical yet challenging. Tool condition monitoring (TCM) has been widely studied to address this issue. Cutting force is a key parameter for evaluating tool wear, but conventional force sensors are costly and difficult to implement. This study proposes a cost-effective alternative by estimating cutting forces using spindle vibration data through a long short-term memory (LSTM)-based machine learning model. First, the correlation between cutting force and tool wear is analyzed to emphasize the need for accurate force estimation. Then, vibration data collected from the spindle are used to train an LSTM model, which is effective for time-series data processing. The model is trained with vibration signals from various machining positions, with structured time-series datasets improving performance and generalization. Experimental results show that the developed model accurately estimates cutting forces using short segments of vibration data from a single tool revolution. Additionally, the observed relationship between cutting force and tool wear remains consistent across different machining conditions. This study validates real-time cutting force estimation via spindle vibration monitoring and suggests its potential for tool wear prediction. The proposed method offers a practical, low-cost solution for improving tool condition monitoring in automated machining.

Cutting force is a critical indicator reflecting the interaction between the cutting tool and the workpiece in machining processes. Conventional measurement methods using dynamometers are accurate but costly and challenging for real-time applications. This study proposes a novel transfer learning-based method for estimating milling cutting forces using only spindle vibration signals without direct force sensors. The proposed approach consists of two stages: First, an autoencoder is trained with measured cutting force data to construct a latent feature space. Second, a target encoder aligns spindle vibration signals to this latent space, allowing the decoder to reconstruct estimated cutting forces. To reflect machining parameters into the learning model, the input dataset was constructed by integrating material type, cutting speed, and cutting direction as additional inputs into each model’s inputs. Experiments were conducted on Ti-6Al-4V and STS316L workpieces under various machining conditions. Under normal conditions, the proposed method achieved an average Pearson correlation coefficient (PCC) of 0.9213 ($F_x$) and 0.9072 ($F_y$). Under abnormal transient conditions, robust performance was maintained, with PCC values of 0.8573 ($F_x$) and 0.9202 ($F_y$). The results demonstrate that the proposed method can effectively monitor cutting forces and reflect changes across a variety of machining environments using only vibration signals.