Description
- 32-bit DSP digital control mode
- Low vibration, low noise, low power consumption
- Maximum output current 3A/phase
- Adopting CAN bus and supporting standard CANopen communication protocol, it can load up to 127 devices
- Supports three modes: protocol position control, speed control, and periodic position control
- Current setting, subdivision, motor start stop control, and real-time monitoring of motor operation can be achieved through the bus
- 2-channel optoelectronic isolation programmable input interface
medical equipment, various types of robots, robotic arms, automated production lines, and various CNC machine tools.
0 PrefaceGermany”s “Industry 4.0″ and the United States” “Industrial Internet” will restructure the world”s industrial layout and economic structure, bringing different challenges and opportunities to countries around the world. The State Council of China issued “Made in China 2025” as an action plan for the first ten years of implementing the strategy of manufacturing a strong country, which will accelerate the integrated development of IoT technology and manufacturing technology [1]. IoT collects data on machine operations, material usage, facility logistics, etc., bringing transparency to operators. This transparency is brought about by the application of data analytics, which refers to the use of statistical and machine learning methods to discover different data characteristics and patterns. Machine learning technology is increasingly used in various manufacturing applications, such as predictive maintenance, test time reduction, supply chain optimization, and process optimization, etc. [2-4]. The manufacturing process of enterprises has gradually developed from the traditional “black box” model to the “multi-dimensional, transparent and ubiquitous perception” model [5].1 Challenges facing manufacturing analysisThe goal of manufacturing analytics is to increase productivity by reducing costs without compromising quality:(1) Reduce test time and calibration, including predicting test results and calibration parameters;(2) Improve quality and reduce the cost of producing scrap (bad parts) by identifying the root causes of scrap and optimizing the production line on its own;(3) Reduce warranty costs, use quality testing and process data to predict field failures, and cross-value stream analysis;(4) Increase throughput, benchmark across production lines and plants, improve first-pass rates, improve first-pass throughput, and identify the cause of performance bottlenecks such as overall equipment effectiveness (OEE) or cycle time;
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