• Proceedings of the Korean Society of Precision Engineering Conference
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• 1996.04a
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• pp.610-614
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• 1996

As the run out error and misalignment of ball screw connected directly to guide table largely affect the motion accuracy of guideway, floating coupling that releases the table from screw nut except feed and rotational direction is needed todecrease its influences. The purpose of this study is to propose a practical model floating coupling of ball serew for high precision feeding system. The straightness, dynanic characteristics and micro step response of hydrostatic guideway, mounted with three types of coupling fixed type, leaf spring type and hydrostatic type, are tested and compared. From the resuts of experiments, it is proved that a hydrostatic type floating coupling is superior to other couplings and is available to high precision feeding system with ball screw.

• The Journal of the Convergence on Culture Technology
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• v.10 no.3
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• pp.801-807
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• 2024

Linear motion robots are devices that perform functions such as transferring parts or positioning devices, and require high precision. In companies that develop linear robot application systems, human workers are in charge of quality control and fault diagnosis of linear robots, and the result and accuracy of a fault diagnosis varies depending on the skill level of the person in charge. Recently, there have been many attempts to utilize artificial intelligence to diagnose faults in industrial devices. In this paper, we present a system that automatically diagnoses linear rail and ball screw misalignment of a linear robot using transfer learning. In industrial systems, it is difficult to obtain a lot of learning data, and this causes a data imbalance problem. In this case, a transfer learning model configured by retraining an established model is widely used. The information obtained by using an acceleration sensor and torque sensor was used, and its usefulness was evaluated for each case. After converting the signal obtained from the sensor into a spectrogram image, the type of abnormality was diagnosed using an image recognition artificial intelligence classifier. It is expected that the proposed method can be used not only for linear robots but also for diagnosing other industrial robots.

• Proceedings of the Korean Society of Tribologists and Lubrication Engineers Conference
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• 1998.10a
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• pp.343-349
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• 1998

A modeling of machined geometry and cutting force was proposed for the case of round shape machining, and the effects of internally machined profile are analyzed and its realiability was verified by the experiments of roundness tester, especially in boring operation in lathe. Also, harmonic cutting force model was proposed with the parameter of specific cutting force, chip width and chip thickness, and in this study, we can see that bored workpiece profile was also mapped into cutting force signal with this model. In general, we can calculated the theoretical lobe profile with arbitrary multilobe. But in real experiments, the most frequently measured numbers are 3 and 5 lobe profile in experiments. With this results, we can predict that these results may be applied to round shape machining such as drilling, boring, ball screw and internal grinding operation with the same method.

• International Journal of Internet, Broadcasting and Communication
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• v.16 no.3
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• pp.121-138
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• 2024

Artificial intelligence is crucial to manufacturing productivity. Understanding the difficulties in producing disruptions, especially in linear feed robot systems, is essential for efficient operations. These mechanical tools, essential for linear movements within systems, are prone to damage and degradation, especially in the LM guide, due to repetitive motions. We examine how explainable artificial intelligence (XAI) may diagnose wafer linear robot linear rail clearance and ball screw clearance anomalies. XAI helps diagnose problems and explain anomalies, enriching management and operational strategies. By interpreting the reasons for anomaly detection through visualizations such as Class Activation Maps (CAMs) using technologies like Grad-CAM, FG-CAM, and FFT-CAM, and comparing 1D-CNN with 2D-CNN, we illustrates the potential of XAI in enhancing diagnostic accuracy. The use of datasets from accelerometer and torque sensors in our experiments validates the high accuracy of the proposed method in binary and ternary classifications. This study exemplifies how XAI can elucidate deep learning models trained on industrial signals, offering a practical approach to understanding and applying AI in maintaining the integrity of critical components such as LM guides in linear feed robots.