The amount of data collected by the Laser cutting-bending Monitor box is huge and complex. To achieve fast and effective processing, the following key steps are required.
The first is data preprocessing. Due to various interference factors in the collection environment, the collected data may have noise or outliers. Therefore, it is necessary to filter and denoise the data to remove these interference information to improve the quality and reliability of the data. For example, the use of methods such as mean filtering and median filtering can smooth the data curve and reduce the impact of noise on subsequent analysis. At the same time, for abnormal values that obviously deviate from the normal range, they are identified and eliminated by setting thresholds to ensure the accuracy of the data.
The second is data feature extraction. Extracting key features that can reflect the state and quality of laser cutting from a large amount of raw data is an important part of data processing. These features may include the changing trends, peak values, and mean values of parameters such as cutting speed, laser power, cutting depth, and incision width. By extracting and analyzing these features, the state of the cutting process can be more intuitively understood. For example, calculating the mean and standard deviation of the cutting depth can evaluate the stability of the cutting; analyzing the change curve of the laser power can determine whether the output of the laser energy is uniform.
The next step is data analysis and modeling. Using professional data analysis algorithms and models, the extracted feature data is deeply analyzed. Common methods include statistical analysis, machine learning algorithms, etc. Statistical analysis can calculate the correlation between various parameters and find out the factors that have a significant impact on cutting quality. Machine learning algorithms, such as neural networks and decision trees, can establish a prediction model between cutting quality and various parameters, and achieve real-time prediction and evaluation of cutting quality through learning and training of historical data. For example, according to parameters such as cutting speed and laser power, the surface roughness or dimensional accuracy after cutting can be predicted so as to adjust the cutting parameters in time and optimize the cutting process.
Finally, data visualization and feedback. The processed data is visualized in the form of intuitive and easy-to-understand charts and graphs, so that operators can quickly understand the key information and quality status of the cutting process. At the same time, the analysis results are fed back to the control system of the laser cutting equipment in a timely manner to achieve closed-loop control. For example, when the cutting quality is detected to be abnormal, the Monitor box can automatically issue an alarm and feed back the adjustment suggestions to the control system to automatically adjust the cutting parameters to ensure the stability and consistency of the cutting quality, thereby improving the overall efficiency of laser cutting and product quality.
