Facing some special operating environments or conditions, existing control methods for the peg-in-hole assembly guided by robots always have their own disadvantages, for example, low efficiency or poor adaptability. For the above problem, in this article, a new circular peg-in-hole assembly control strategy is proposed for the 6-Degree of Freedom (DOF) robot based on hybrid visual measurements, avoiding peg-in-hole contacts during the robotic operation. In the strategy, the pose of the monocular camera mounted at the end-effector is adaptively adjusted to improve the image quality through an algorithm based on the rough pose measurement of the target hole by the binocular camera; the accurate 3-D pose of the hole is determined by an algorithm based on processing of high-quality images and the compensation of the orientation error. Combined with the robotic collision-free path planning, the automatic peg-in-hole assembly can be implemented in real setting. The assembly precision of the robotic system based on the proposed method is validated and discussed based on experimental results. Then, the minimal peg-in-hole interval relative to the alignment error is modeled through the spatial relation analysis to analyze the applicable condition of the robotic system with the control strategy. Also, the reliability of the proposed strategy is verified through experimental tests under some applicable conditions. Finally, suggestions and plans of future works are discussed for further extension of the application area of the proposed strategy, such as fields of precision and ultraprecision manufacturing. This contribution has the major significance on the automatic peg-in-hole assembly under 3-D operating environment.

The machine tools are consisted of many parts and most of them are connected by the bolts. Accurate modeling of contact stiffness and damping for bolted joint is crucial in predicting the dynamic performance of machine tools. This paper presents a modified three-dimensional fractal contact model to obtain the stiffness and damping of bolted joint. Topography of the contact surface of bolted joint is fractal featured and determined by fractal parameters. Asperities in microscale are considered as elastic, elastic–plastic, and full plastic deformation. The expand coefficient ψ is introduced to the size-distribution function of asperities. The real contact area, contact stiffness, and damping of the contact surface can be calculated by integrating the microasperities. The relationship of contact stiffness, damping, fractal dimension D, and fractal roughness parameter G can be obtained. Experiments are conducted to verify the efficiency of the proposed model. The results show that the theoretical mode shapes are in good agreement with the experimental mode shapes. The relative errors between the theoretical and experimental natural frequencies are less than 3.33%, which is less than those of the W-K model and L-L model. The presented model can be used to accurately predict the dynamic characteristic of bolted assembly in the machine tools.

A flexible manufacturing system (FMS) improves productivity and makes it more efficient. Maintaining reliability levels and reducing costs through proper maintenance strategies are key problems for the development and application of a FMS. This paper proposes a grouping preventive maintenance strategy of a FMS with optimized parameters by considering both reliability and cost. In this work, a three-layer evaluation index system is first presented to accurately estimate the reliability of the FMS; index weights of each layer were obtained by reliability importance modeling and analysis, considering maintenance strategies. An element-grouping preventive strategy is proposed based on an influencing analysis, and a parameter optimization problem (considering reliability and maintenance costs) was established. In this strategy, three maintenance methods are presented for the elements, including low-level maintenance with a large period, low-level maintenance with a small period, as well as the combination of low-level maintenance with a small period and high-level maintenance with a large period; the effects of reliability improvement of the elements on the subsystem’s reliability were analyzed to provide evidence for element grouping. Finally, the proposed method was applied to a box-part finishing FMS; the results indicate that this method can effectively reduce maintenance costs on the premise of satisfying the reliability requirements.

Disassembly plays an important role in the production process. Screw automatic unfastening guided by a robot has been widely used in the fields of industrial manufacturing and maintenance. Different from the previous studies that have used a flexible effector and expensive sensors, this paper presents a novel unfastening strategy based on robot vision for a hexagonal screw with an arbitrary loose state. In a robotic system, an industrial camera and a servo unfastening tool are installed at a robotic end-effector. The main contributions of this work are as follows. A camera pose adjustment method is proposed to obtain high-quality images of a target screw. The hexagonal screw pose calculation method based on the geometric analysis is developed to complete the screw–tool engagement. The cooperated motion of a robot and an unfastening tool is planned for the screw unfastening action. The effectiveness of the proposed control strategy is verified by experiments, and the influence of the motion speed on the unfastening quality is analyzed using the torque data collected by the unfastening tool. The analysis results can provide a significant foundation for the motion parameter selection in the proposed strategy.

For complex equipment, it is easy to over-evaluate the impact of failure on production by estimating the reliability level only through failure probability. To remedy this problem, this paper proposes a statistical evaluation method based on fuzzy failure data considering the multi-state characteristics of equipment failures. In this method, the new reliability-evaluation scheme is firstly presented based on the traditional statistical analysis method using the Weibull distribution function. For this scheme, the failure-grade index is defined, and a fuzzy-evaluation method is also proposed by comprehensively considering failure severity, failure maintenance, time, and cost; this is then combined with the time between failures to characterize the failure state. Based on the fuzzy failure data, an improved adaptive-failure small-sample-expansion method is proposed based on the classical bootstrap method and the deviation judgment between distributions of the original and newborn samples. Finally, a novel reliability-evaluation model, related to the failure grade and its membership degree, is established to quantify the reliability level of equipment more realistically. Example cases for three methods of the scheme (the failure-grade fuzzy-evaluation method, the sample-expansion method, and the reliability-evaluation modeling method) are presented, respectively, to validate the effectiveness and significance of the proposed reliability-evaluation technology.