This paper describes the implementation and optimization of a dual-confocal autofocusing system that can easily describe a real-time position by measuring the response signal (i.e., intensity) of the front and the rear focal points of the system. This is a new and systematic design strategy that would make it possible to use this system for other applications while retrieving their characteristic curves experimentally; there is even a good chance of this technique becoming the gold standard for optimizing these dual-confocal configurations. We adopt two indexes to predict our system performance and discover that the rear focal position and its physical design are major factors. A laboratory-built prototype was constructed and demonstrated to ensure that its optimization was valid. The experimental results showed that a total optical difference from 150 to 400 mm significantly affected the effective volume of our designed autofocusing system. The results also showed that the sensitivity of the dual-confocal autofocusing system is affected more by the position of the rear focal point than the position of the front focal point. The final optimizing setup indicated that the rear focal length and the front focal length should be set at 200 and 100 mm, respectively. In addition, the characteristic curve between the focus error signal and its position could successfully define the exact position by a polynomial equation of the sixth order, meaning that the system can be straightforwardly applied to an accurate micro-optical auto-focusing system.

Electric discharge machining (EDM) is a type of high-precision machining usually applied to hard-material machining for mold manufacturing and in the aerospace industry. Longer process times typically reduce facility efficiency. The use of electrochemistry machining (ECM) can overcome this challenge to efficiently machine large workpieces. Some industries have adopted and combined these two processes for Inconel 718 material machining. However, the use of coordinate-measuring machine times to determine the machining accuracy of these two processes is difficult. This study matched process features by analyzing the electric driving pulses of ECM and EDM. Fitting intelligent sensing signals that respond to dimensional measurements can be used to analyze electrical pulse signals. For analyzing a cross-process model using extracted key features of the process, our feedback-based system determines lower machining measurement errors and improves geometric size. Finally, the processing time of experiments can be reduced by 80%, and our proposed model has a prediction accuracy of approximately 0.01 ${\mathrm{mm}}^2$.

In the electrical discharge machining process, preliminary research has been able to effectively estimate machining accuracy in response to its long machining history and high discharge frequency characteristics. However, when processing abnormalities occur, it is difficult to identify them since the electrical discharge process contains multiple processing parameters, which increases the cost of repair or loss afterwards. Therefore, the question concerning how to monitor the abnormality of the discharge process in real time represents the main purpose of this research. This research develops an EDM process abnormal diagnosis system. First, the data are stored in a circular array to speed up the processing time, and the coefficient of variation feature is added, which has effectively extracted the abnormal characteristics. In terms of diagnostic methods, the composite voting model established by neural networks, random forests, and XGB-RF (extreme gradient boosting applying RF) can provide robust diagnostic results. Finally, through the Node-RED webpage and MQTT agreement, it can provide the ability to monitor machine abnormalities in real time. Through refinement and optimization of the previous research results, this study took the electrical discharge machining diamond grinding wheel as an example, and developed a warning that can be issued within 3 min when abnormalities (abnormal patterns such as polycrystalline diamond high protrusions) occur, with an accuracy of 93% and a false positive rate. The abnormal diagnosis ability is less than 0.2%. Therefore, the online abnormality monitoring system developed by this research institute will be able to provide online abnormality diagnosis for electrical discharge machining.

In the era of Industry 4.0, equipment maintenance is shifting toward data-driven strategies. Traditional methods rely on usage time or cycle counts to estimate component lifespan. This often causes early replacement of parts, leading to increased production costs. This study focuses on the cutter system of a roller laminator used in printed circuit board (PCB) manufacturing. An accelerometer is used to collect vibration signals under normal and abnormal states. Fast Fourier transform (FFT) is used to convert time-domain data into the frequency domain, then key statistical features from critical frequency bands are extracted as independent variables. The study applies logistic regression (LR), random forest (RF), and support vector machine (SVM) for predictive modeling of the cutting tool’s condition. The results show that the prediction accuracies of these models are 87.55%, 93.77%, and 94.94%, respectively, with SVM performing the best.