In bio-inspired design, identifying keywords is an important step of biological information searching. Based on existing information retrieval approaches, the amount of relevant biological information in a search result largely depends on the identified keywords. Due to the limitation of biological knowledge, design engineers are difficult to identify the appropriate keywords that can find the biological information related to engineering requirements. To address this issue, we present an algorithm that can calculate the Composite Correlation Intension of functionally combined words, which integrates semantic similarity computation, data normalization, and corpus technology. Based on the algorithm, a method that automatically pushes keywords for biological information searching in bio-inspired design is also proposed. The method decomposes engineering requirements and structures functionally combined words, calculates the Composite Correlation Intension values of all functionally combined words, ranks the functionally combined words by the Composite Correlation Intension values, takes the functionally combined words with larger Composite Correlation Intension values as keywords, and pushes them. Through these keywords, design engineers retrieve the relevant biological information and discover the required design knowledge. In order to facilitate the use of proposed method, an auxiliary tool was developed in Python environment. Finally, the possibility of proposed method was demonstrated by a preliminary validation and an application case. The results show that the proposed method would be a promising alternative to identify keywords for biological information searching in bio-inspired design.

In the aerospace manufacturing industry, it is impossible to achieve precise and efficient automatic drilling and riveting for largescale composite board parts. The bottleneck is that the depth detection of rivet holes still relies on manual operation, which seriously affects the assembly efficiency and stability of composite board parts. In order to realize accurate and efficient on-machine automatic measurement for through holes in the automatic drilling and riveting process of largescale composite board parts, this paper presents a novel hole depth measuring device. Its mechanical structure is developed based on our newly designed measurement scheme and optical path, the purpose of which is to convert the hole depth data into displacement data of the probe motion. Its electrical hardware consists of three units: a laser transceiver unit to pick up laser spots; a displacement measuring unit to capture the probe movement in real time; and a driving unit to achieve motion control of the probe. Finally, the experimental results indicated that the proposed method and device are capable of performing automatic measurements for through-hole depth. In addition, factors affecting the measuring accuracy and stability of the device are initially analyzed and discussed, which lay a foundation for subsequent research on error compensation and probe calibration.

In order to obtain a highly accurate profile of a measured three-dimensional (3D) free-form surface, a scanning measuring device has to produce extremely dense point cloud data with a great sampling rate. Bottlenecks are created owing to inefficiencies in manipulating, storing and transferring these data, and parametric modelling from them is quite time-consuming work. In order to effectively compress the dense point cloud data obtained from a 3D free-form surface during the real-time scanning measuring process, this paper presents an innovative methodology of an on-line point cloud data compression algorithm for 3D free-form surface scanning measurement. It has the ability to identify and eliminate data redundancy caused by geometric feature similarity between adjacent scanning layers. At first, the new algorithm adopts the bi-Akima method to compress the initial point cloud data; next, the data redundancy existing in the compressed point cloud is further identified and eliminated; then, we can get the final compressed point cloud data. Finally, the experiment is conducted, and the results demonstrate that the proposed algorithm is capable of obtaining high-quality data compression results with higher data compression ratios than other existing on-line point cloud data compression/reduction methods.

In order to further explore the technical difficulties involved in orienting the sparse-aperture imaging system towards practical applications, this paper proposes an optical–mechanical structure design scheme and performance simulation, as well as actual alignment on the beam steering and adjustment structure, which is the core component of the Golay3 sparse-aperture imaging system. The beam steering and adjustment structure corrects the cophasing error by using a combination of mechanical rough adjustment and piezoelectric ceramic precision adjustment. The beam adjustment capability of the beam steering and adjustment structure is analyzed by simulation when the system contains the piston error and the tilt error. The piston error is controlled within 12 μm and the tilt error is controlled within 1200 μrad through the mechanical rough adjustment light path using a large-diameter collimation as the point light source, which realizes the confocality of three beams and mutual interference between two beams. With the USAF1951 resolution test panel display in the distance as the surface target, the adjustable piezoelectric ceramics control the piston error within 55 nm and the tilt error within 0.25 μrad, obtaining the surface target imaging result of the sparse aperture.