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.
(5) Perform predictive maintenance, analyze machine operating conditions, determine the main causes of failures, and predict component failures to avoid unplanned downtime.Traditional quality improvement programs include Six Sigma, Deming Cycle, Total Quality Management (TQM), and Dorian Scheinin’s Statistical Engineering (SE) [6]. Methods developed in the 1980s and 1990s are typically applied to small amounts of data and find univariate relationships between participating factors. The use of the MapReduce paradigm to simplify data processing in large data sets and its further development have led to the mainstream proliferation of big data analytics [7]. Along with the development of machine learning technology, the development of big data analytics has provided a series of new tools that can be applied to manufacturing analysis. These capabilities include the ability to analyze gigabytes of data in batch and streaming modes, the ability to find complex multivariate nonlinear relationships among many variables, and machine learning algorithms that separate causation from correlation.Millions of parts are produced on production lines, and data on thousands of process and quality measurements are collected for them, which is important for improving quality and reducing costs. Design of experiments (DoE), which repeatedly explores thousands of causes through controlled experiments, is often too time-consuming and costly. Manufacturing experts rely on their domain knowledge to detect key factors that may affect quality and then run DoEs based on these factors. Advances in big data analytics and machine learning enable the detection of critical factors that effectively impact quality and yield. This, combined with domain knowledge, enables rapid detection of root causes of failures. However, there are some unique data science challenges in manufacturing.(1) Unequal costs of false alarms and false negatives. When calculating accuracy, it must be recognized that false alarms and false negatives may have unequal costs. Suppose a false negative is a bad part/instance that was wrongly predicted to be good. Additionally, assume that a false alarm is a good part that was incorrectly predicted as bad. Assuming further that the parts produced are safety critical, incorrectly predicting that bad parts are good (false negatives) can put human lives at risk. Therefore, false negatives can be much more costly than false alarms. This trade-off needs to be considered when translating business goals into technical goals and candidate evaluation methods.
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