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The application of thermal barrier coatings (TBCs) allows aero-engine blades to operate at higher temperatures with higher efficiency. The preparation of the TBCs increases the surface roughness of the blade, which impacts the thermal cycle life and thermal insulation performance of the coating. To reduce the surface roughness of blades, particularly the blades with small size and complex curvature, this paper aims to propose a method for industrial robot polishing trajectory planning based on on-site measuring point cloud.

The authors propose an integrated robotic polishing trajectory planning method using point cloud processing technical. At first, the acquired point cloud is preprocessed, which includes filtering and plane segmentation algorithm, to extract the blade body point cloud. Then, the point cloud slicing algorithm and the intersection method are used to create a preliminary contact point set. Finally, the Douglas–Peucker algorithm and pose frame estimation are applied to extract the tool-tip positions and optimize the tool contact posture, respectively. The resultant trajectory is evaluated by simulation and experiment implementation.

The target points of trajectory are not evenly distributed on the blade surface but rather fluctuate with surface curvature. The simulated linear and orientation speeds of the robot end could be relatively steady over 98% of the total time within 20% reduction of the rest time. After polishing experiments, the coating roughness on the blade surface is reduced dramatically from Ra 7–8 µm to below Ra 1.0 µm. The removal of the TBCs is less than 100 mg, which is significantly less than the weight of the prepared coatings. The blade surface becomes smoothed to a mirror-like state.

The research on robotic polishing of aero-engine turbine blade TBCs is worthwhile. The real-time trajectory planning based on measuring point cloud can address the problem that there is no standard computer-aided drawing model and the geometry and size of the workpiece to be processed differ. The extraction and optimization of tool contact points based on point cloud features can enhance the smoothness of the robot movement, stability of the polishing speed and performance of the blade surface after polishing.

During the process of the robotic grinding and polishing operations on aero-engine blades, the key problem of calibration error lies in fixture error and uneven margin. To solve this problem, this paper aims to propose a novel method to achieve rapid online calibration of the workpiece coordinate system through laser-based measurement techniques.

The authors propose a calibration strategy based on point cloud registration algorithm. The main principle is presented as follows: aero blade mounted on clamping end-effector is hold by industry robot, the whole device is then scanned by a 3D laser scanner to obtain its surface point cloud, and a fast segmentation method is used to acquire the point cloud of the workpiece. Combining Super4PCS algorithm with trimmed iterative closest point, we can align the key points of the scanned point cloud and the sampled points of the blade model, thus obtaining the translation and rotation matrix for calculating the workpiece coordinate and machining allowance. The proposed calibration strategy is experimentally validated, and the positioning error, as well as the margin distribution, is finally analyzed.

The experimental results show that the algorithm can well accomplish the task of cross-source, partial data and similar local features of blade point cloud registration with high precision. The total time spent on point cloud alignment of 100,000 order of magnitude blade is about 4.2 s, and meanwhile, the average point cloud alignment error is reduced to below 0.05 mm.

An improved point cloud registration method is proposed and introduced into the calibration process of a robotic system. The online calibration technique improves the accuracy and efficiency of the calibration process and enhances the automation of the robotic grinding and polishing system.

This paper aims to check the presence of such relationship in the field. Certain values are at stake for the success of economic behavior. Since the genesis of modern capitalism, a set of beliefs proper of Calvinism (mainly Predestination but also Beruf, inner-worldly asceticism, role of Sects […] ) was said by Max Weber to cause an anxiety about salvation and generate a propensity to economic success as a sign of election. The author argues on the contrary that the Calvinist belief in the Perpetual Assurance of Salvation might cause a sense of self-efficacy able to favor economic success. To observe this in action today, it is crucial to consider the evolution that the Protestant ethic went through migrating first in North America and, finally, through the Protestant revival of China. Wenzhou is called “Jerusalem of China” for its large Protestant community that is also strongly involved in business. Some scholar already pointed out the presence among those entrepreneurs of this Protestant ethic (Yi Xiang, Boss-Christian […]).

The data presented in this comparative qualitative study pertain to ethnographic observations, job-shadowing and interviews done among Chinese Christian and non-Christian entrepreneurs from Wenzhou living in Milan, Italy.

The results show, with some adjustments, the presence of a Chinese version of the Protestant ethic overlapping with several values proper to the Chinese context (Confucianism, lineage, social network). The extension of the study to other cases must be done with caution considering the non-causal justificatory role of the belief.

Successful entrepreneurship involves specific social, cultural and even religious aspects that move beyond mere business strategies.

The purpose is to identify and evaluate the safety risk factors in the coal mine construction process.

The text mining technique was applied in the stage of safety risk factor identification. The association rules method was used to obtain associations with safety risk factors. Decision-Making Trial and Evaluation Laboratory (DEMATEL) and Interpretative Structural Modeling (ISM) were utilized to evaluate safety risk factors.

The results show that 18 safety risk factors are divided into 6 levels. There are 12 risk transmission paths in total. Meanwhile, unsafe behavior and equipment malfunction failure are the direct causes of accidents, and inadequate management system is the basic factor that determines the safety risk status.

Due to the limitation of the computational matrix workload, this article only categorizes numerous lexical items into 18 factors. Then, the workshop relied on a limited number of experts; thus, the findings may be potentially biased. Next, the accident report lacks a universal standard for compilation, and the use of text mining technique may be further optimized. Finally, since the data are all from China, subsequent cross-country studies should be considered.

The results can help China coal mine project managers to have a clear understanding of safety risks, efficiently carry out risk hazard identification work and take timely measures to cut off the path of transmission with risks identified in this study. This helps reduce the economic losses of coal mining enterprises, thus improving the safety standards of the entire coal mining industry and the national standards for coal mine safety policy formulation.

Coal mine construction projects are characterized by complexity and difficulties in construction. Current research on the identification and assessment of safety risk factors in coal mine construction is insufficient. This study combines objective and systematic research approaches. The findings contribute to the safety risk management of China coal mine construction projects by providing a basis for the development of safety measures.