During the SMT stencil laser cutting process, real-time monitoring of cutting quality relies on optical sensing, dynamic feedback, intelligent algorithms, and multi-parameter coordinated control technology. By capturing the cutting status in real time, analyzing quality characteristics, and rapidly adjusting process parameters, we ensure that the cut edge accuracy, surface quality, and heat-affected zone control meet process requirements. This process involves monitoring the entire laser-material interaction cycle, from beam emission to cutting completion. Each step requires precise control to ensure final quality.
In the SMT stencil laser cutting machine, a coaxial vision sensing system is a core tool for real-time monitoring. This system uses a beam splitter to synchronously transmit the laser reflected light and the material melt state in the cutting area to an industrial camera, generating a high-resolution real-time image. The camera captures dynamic information about the cutting front, including the beam center position, melt pool shape, and cut edge contour. This data directly reflects the stability of the cutting process. For example, if the cutting speed is too fast, the melt pool will extend due to insufficient cooling, resulting in dross on the cut edge. If the speed is too slow, the heat-affected zone will expand, causing material deformation. By analyzing these characteristics in real time, the coaxial vision system can immediately trigger parameter correction instructions to avoid quality defects. Real-time assessment of cut quality relies on image processing algorithms to quantitatively analyze dynamic features. The algorithm extracts parameters such as cut edge grain frequency, edge straightness, and melt pool width to determine whether the cutting state deviates from the standard. For example, cut edge grain frequency is closely related to surface roughness: high-frequency grains correspond to smoother cut surfaces, while low-frequency grains may be caused by insufficient laser energy or uneven gas purge. By comparing real-time data with preset thresholds, the algorithm can quickly identify defects such as overburning, burrs, or kerf deviation, and guide the system to adjust laser power, focus position, or assist gas pressure.
Dynamic feedback and closed-loop control of laser parameters are critical to ensuring cutting quality. Laser power, pulse frequency, and duty cycle must be optimized in real time based on material thickness, reflectivity, and cutting speed. For example, when cutting highly reflective stainless steel, the system uses power modulation to reduce laser energy density to avoid cutting instability caused by excessively strong reflected light. For thick plate materials, the system uses a stepped power output, increasing energy during the penetration phase and decreasing energy during the stable cutting phase to reduce heat input. Real-time calibration of the focal position is equally important. Changes in lens focal length due to thermal effects can directly affect cutting quality. The system automatically adjusts the relative position of the focal point to the workpiece surface using inductive or capacitive sensors to ensure the beam is always focused at the optimal cutting point.
Precise control of the assist gas system has a direct impact on cut quality. The choice of nitrogen, oxygen, or compressed air should be determined based on material properties and cutting requirements. The gas pressure, flow rate, and nozzle angle must be adjusted in conjunction with the laser parameters. For example, when cutting thin plates, high-pressure nitrogen effectively removes slag and prevents oxidation of the cut surface. When cutting thick plates, low-pressure oxygen enhances oxidation and improves cutting efficiency. The system monitors gas consumption in real time using a flow sensor and adjusts nozzle pressure based on images of the cutting edge to ensure timely removal of slag and avoid edge roughness caused by secondary melting.
Real-time monitoring and control of the heat-affected zone (HAZ) relies on the coordinated adjustment of multiple parameters. During laser cutting, the metal surrounding the cut undergoes a phase change due to heat input, forming a HAZ. Changes in the HAZ's width and hardness directly affect the performance of the stencil. The system uses infrared thermometers to monitor the temperature field around the cut in real time and, based on the material's thermal conductivity characteristics, dynamically adjusts the laser pulse frequency and duty cycle. For example, at the end of the cut, the system reduces pulse energy and extends the cooling time to minimize the risk of deformation caused by heat accumulation.
Full-process data traceability and analysis provide a basis for quality optimization. The system records parameter combinations, image data, and quality inspection results for each batch of cutting. Using machine learning algorithms, it builds a correlation model between process parameters and cutting quality. When batch-specific quality fluctuations are detected, the model quickly identifies the root cause and generates optimization recommendations. For example, for stainless steel of a specific thickness, the system can recommend the optimal beam mode, cutting speed, and gas pressure combination, achieving process standardization and intelligence. This data-driven quality control approach has significantly improved the stability and consistency of the SMT stencil laser cutting machine.
