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Presently, 6 Degree of Freedom (6DOF) visual pose measurement methods enjoy popularity in the industrial sector. However, challenges persist in accurately measuring the visual pose of blank and rough metal casts. Therefore, this paper introduces a 6DOF pose measurement method utilizing stereo vision, and aims to the 6DOF pose measurement of blank and rough metal casts.

This paper studies the 6DOF pose measurement of metal casts from three aspects: sample enhancement of industrial objects, optimization of detector and attention mechanism. Virtual reality technology is used for sample enhancement of metal casts, which solves the problem of large-scale sample sampling in industrial application. The method also includes a novel deep learning detector that uses multiple key points on the object surface as regression objects to detect industrial objects with rotation characteristics. By introducing a mixed paths attention module, the detection accuracy of the detector and the convergence speed of the training are improved.

The experimental results show that the proposed method has a better detection effect for metal casts with smaller size scaling and rotation characteristics.

A method for 6DOF pose measurement of industrial objects is proposed, which realizes the pose measurement and grasping of metal blanks and rough machined casts by industrial robots.

This paper aims to study six degrees-of-freedom (6DOF) pose measurement of reflective metal casts by machine vision, analyze the problems existing in the positioning of metal casts by stereo vision sensor in unstructured environment and put forward the visual positioning and grasping strategy that can be used in industrial robot cell.

A multikeypoints detection network Binocular Attention Hourglass Net is constructed, which can complete the two-dimensional positioning of the left and right cameras of the stereo vision system at the same time and provide reconstruction information for three-dimensional pose measurement. Generate adversarial networks is introduced to enhance the image of local feature area of object surface, and the three-dimensional pose measurement of object is completed by combining RANSAC ellipse fitting algorithm and triangulation method.

The proposed method realizes the high-precision 6DOF positioning and grasping of reflective metal casts by industrial robots; it has been applied in many fields and solves the problem of difficult visual measurement of reflective casts. The experimental results show that the system exhibits superior recognition performance, which meets the requirements of the grasping task.

Because of the chosen research approach, the research results may lack generalizability. The proposed method is more suitable for objects with plane positioning features.

This paper realizes the 6DOF pose measurement of reflective casts by vision system, and solves the problem of positioning and grasping such objects by industrial robot.

The purpose of this paper is to understand a flameproof distance necessary to avoid the flame harms to underground personnel which may have great significance to the safety of underground personnel and the disaster relief of gas explosions in coal mines.

Through a roadway with a length of 100 m and a cross-section area of 80 mm×80 mm, the flame propagation of premixed methane-air deflagrations were simulated by using AutoReaGas software for various fuel concentrations (7, 8, 9.5, 11, and 14 percent), fuel volumes (0.0128, 0.0384, 0.064, and 0.0896 m3), initial temperatures (248, 268, 288, 308, and 328 K), and initial pressures (20, 60, 101.3, 150, and 200 kPa).

The maximum combustion rate for each point follows a changing trend of increasing and decreasing with the distance increasing from the ignition source, and it increases with the fuel volume increasing or the initial pressure increasing, and decreases with the initial temperature increasing. However, increasing the initial temperature increases the flame arrival time for each point. The flameproof distance follows a changing trend of increasing and decreasing with the fuel concentration increasing, and it linearly increases with the fuel volume increasing or the initial temperature increasing. However, the flameproof distances are all 17 m for various initial pressures.

Increasing initial temperature increases flame arrival time for each test point. Flameproof distance increases and then decreases with fuel concentration increasing. Increasing fuel volume or initial temperature linearly increases flameproof distance. Initial pressure has little impact on the flameproof distance.