About Instruments Today No. 221
Chih-Chun Cheng, Wen-Nan Cheng, Yu-Sheng Chiu, Wei-Jen Cheng
Paving by the development and improvement of technologies, “manufacturing” in these days requires more than automation. No matter equipment abnormal detection, component life cycle prediction, or IIoT in machine tool manufacturing, sensors are crucial and play an indispensable role. For understanding why and how abnormal vibrations are excited during the machine tools’operation, accelerator is necessary to capture vibration signals for analysis; for realizing how temperature variation influence the thermal deformation of machining tools, displacement and temperature sensors are required for establishing the thermal displacement prediction model to compensate the displacement error. This paper will introduce two generally used sensors mentioned in the above applications-accelerators and temperature sensors, and the circumstance of how they play their roles in machine tool state monitoring.
The Critical Technologies of Industry 4.0 and Smart Manufacturing: Industrial Internet of Things and Artificial Intelligence
Che-Lun Hung, Chih-Hung Chang, Wan-Ju Lin
Industry 4.0 and smart manufacturing have been the trend in development of contemporary industry chain. Two important technologies, Industrial Internet of Thing (IIOT) and Artificial Intelligence (AI), play as key roles in this trend. IIOT collects huge amount of data from a variety of sensors equipped in manufacturing environment and tools. AI is used to analyze these data to understand the status of manufacturing environment and tools to improve the production efficiency, reduce the production costs, and enhance manufacturing safety. Benefits of integrating IIOT and AI will increase dramatically.
Wei-chen Lee, Chunhui Chung, Meng-Kun Liu, Chung-Hsien Kuo, Tien-Ruey Hsiang, Chao- Lung Yang, Nai-Wei Lo
With the advocate of Industry 4.0, the wave of smart manufacturing and the smart factory has swept through the industry in Taiwan. It is expected that the smart factories can automatically respond to the change of the environment, and collect massive information generated in the factories for data analytics. To achieve this, how to connect various machines in a machine shop is a top priority. In this research, we used various communication methods to connect the machines on the shop floor, monitored them during the production process, collected their information, and then uploaded to the cloud server. Regarding the application of the data collected, we used the online measurement data for error compensation. The experimental results showed that the proposed method reduced the standard deviation by at least 30%.
Guo-Rong Chen, Chin-Yuan Chang, Ching-Hung Lee
During the process to machining a product, machining parameters have huge impacts on the quality of the product. We need to adjust to a proper parameter set to obtain a desired quality. This parameter tuning task often relying on a few experienced engineers or an elaborate operating manual. Otherwise, it is only possible to find the desired parameter set through try-and-error experiments. With the advent of the artificial intelligent (AI) era, we have implement an automatic parameter tuning method with reinforcement learning to obtain a desired machining quality. The method also offers a way to choose the quality preferences under multiple performance indexes, and the result is validated on a hole drilling electric discharge machining machine.
Hung-Hsiu Yu, Chin-Chi Hsiao
The robot controller is the brain of the robot. Its function is to issue and transmit action instructions. Its core is control algorithm and application software development. Therefore, the robot controller, which is the heart of the robot system, should require more attention. In this article, introduction to the latest international robotic news and important specifications of a robot controller is addressed. This provides an overall review of the development of industrial robots for readers who are interested in this filed.
Yi-Synan Lu, Te-Wei Liu, Mao-Qi Hong, Meng-Shiun Tsai
In general, CNC machine tools aim at achieving high speed and high precision. There are many factors in affecting machining accuracy and the influence of thermal displacement accounts for more than 50% of the overall errors. Therefore, predicting thermal displacement with compensation algorithm becomes one of the important factors for high precision of machine tools. This paper first measures the temperature changes of each casting components and the relative displacement between the tool tip point and the platform under different spindle speed conditions. Then a sorting and screening mechanism of the thermometer is established to determine the key temperature measurement points. Finally, the thermal displacement compensation model of the machine spindle is developed by using the neural network. It is used to verify the thermometer sequencing and screening mechanism established in this paper, and it is proved that the mechanism can improve the prediction accuracy of the thermal displacement compensation model.
Techniques of Intelligent Manufacturing for Difficult-to-cut Material Applied in Components of Aerospace Industry
Jeng-Nan Lee, Min-Jhang Shie
Titanium alloys and nickel-base super alloys are widely in the manufacture of components for aircraft turbine engines because of their ability to retain high strength at elevated temperatures. Because of its high strength, poor thermal diffusion and work hardening, the cutting of these alloys results in the life of tools and efficiency of works for the worse. So the encountered great difficulties in machining difficult-to-cut materials must be solved. To development and application of key technologies be quickly, and create new cutting technology, so as to meet the demand of aerospace industry. The paper will use the development and manufacture of aerospace components with difficult-to-cut material as examples to introduce five-axis machining, hybrid process, rotary ultrasonic-assisted machining process, and on-line measurement basic technique. The research and development of the intelligent manufacturing technologies for aerospace industry also be discussion.
Ming-Fu Chen, Yi-Hao Lin, Chih-Wen Chen, Po-Jui Chen, Yu-Hsin Lin, Wen-Hao Cho, Jian-Lin Chen, Chi-Chung Kei
This measuring equipment can provide the electrical measurement with high throughput for wafer-based gas sensors to evaluate the quality and performance of sensor chips before packaging processes. Thus the experimental period can be reduced significantly and the production efficiency can be enhanced. The equipment integrated: (1) a chamber-based testing and gas supply system to provide required ingredients and concentrations of test gases; (2) an automatic optical registration system; (3) a linear probes device; (4) a chuck table integrated with a heating control module, to evaluate and classify gas sensors efficiently. For a 6"-wafer with chip size of 1 X 1mm (about 17.6 K chips), electric measurement could be completed within 30 minutes by using linear probing device with 10 sets of probes. The measuring efficiency was at least up to 10 times greater than the one of one by one testing for packaged sensors.