The article presents the development of an automated cyber-physical chocolate customization system based on laser engraving with an integrated machine vision module for quality control. An experimental stand is described, which includes a Co₂ laser with a power of up to 30 W, a precision XYZ positioning system, a conveyor feed and an optical unit with an RGB camera and ring illumination. A laser control software has been developed that implements trajectory generation, focus auto-calibration, and power/speed control, as well as a hybrid visual control algorithm based on convolutional neural networks. Integration is carried out via Modbus/TCP and REST API: real-time inspection results are returned to the PLC for adaptive adjustment of engraving parameters or automatic rejection of defective products. During the DoE experiments, the model showed a decrease in rejection from 8.3% to 1.7% and an increase in throughput from 25 to 36 units/min. Economic and technical analysis confirm.

Keywords: automation, laser engraving, chocolate products, machine vision, quality control, customization

The article proposes an approach to creating an intelligent industrial emissions monitoring system based on the YOLO architecture and digital simulation. The work is relevant for improving the effectiveness of environmental control at industrial facilities, for example, an oil refinery. The system automatically detects and classifies smoke against a complex background (glare, fog, sky), combining real video data with synthetic images of a digital model of the site. Simulation settings and augmentation have been performed for different weather and light conditions. Experiments have shown that adding 30% synthetics to the training set increases classification accuracy, especially for subtle outliers. Recommendations on simulation parameters have been developed and the precision metric for pollution classes has been evaluated. The results confirm the effectiveness of the approach and its readiness to be implemented in automation.

Keywords: machine vision, digital simulation, emission monitoring, neural network models, pollution classification

The article presents a technique for automated control of the gloss of chocolate bars based on machine vision, integrated into the functional scheme of automation of cooling and molding processes. The key factors affecting gloss are considered, existing control methods are analyzed and the need for continuous objective quality assessment is substantiated. To optimize the process, a digital simulation has been created in the R-PRO environment, which allows simulating various technological modes. The developed image processing algorithms calculate quantitative gloss values and form feedback with the control system, adjusting key production parameters. The proposed approach improves the accuracy of control, reduces the volume of defects and reduces the time for debugging equipment, creating conditions for the further development of full automation in the chocolate factory.

Keywords: chocolate, surface gloss, automation, machine vision, quality control, cooling and molding, digital simulation