An Automatic Detection and Online Quality Inspection Method for Workpiece Surface Cracks based on Machine Vision

The wide application of intelligent manufacturing technologies imposes higher requirements for the quality inspection of industrial products; however, the existing industrial product quality inspection methods generally have a few shortcomings such as requiring many inspectors, too complicated methods, difficulty in realizing standardized monitoring, and the low inspection efficiency, etc. Targeting at these problems, this paper proposed an automatic detection and online quality inspection method for workpiece surface cracks based on the machine vision technology. At first, it proposed a vision-field environment calibration method, gave the specific method for workpiece shape feature recognition and size measurement based on machine vision, and achieved the on-line monitoring of workpiece quality problems such as feature defects and size deviations. Then, this study integrated the multi-scale attention module and the up-sampling module that can restore the locations of image pixels based on the high-level and low-level hybrid feature maps, built a workpiece crack extraction network, and realized workpiece crack feature extraction, crack type classification, and damage degree division. At last, experimental results verified the effectiveness of the proposed method, and this paper provided a reference for the application of machine vision technology in other fields.

Preparation and Characterization of Super-Absorbing Gel Formulated from κ-Carrageenan–Potato Peel Starch Blended Polymers

κ-carrageenan is useful for its superior gelling, hydrogel, and thickening properties. The purpose of the study was to maximize the hydrogel properties and water-absorbing capacity of κ-carrageenan by blending it with starch from potato peels to be used as safe and biodegradable water-absorbent children’s toys. The prepared materials were analyzed using FTIR and Raman spectroscopy to analyze the functional groups. Results showed that there was a shift in the characteristic peaks of starch and κ-carrageenan, which indicated their proper reaction during blend formation. In addition, samples show a peak at 1220 cm−1 corresponding to the ester sulfate groups, and at 1670 cm−1 due to the carbonyl group contained in D-galactose. SEM micrographs showed the presence of rough surface topology after blending the two polymers, with the appearance of small pores. In addition, the presence of surface cracks indicates the biodegradability of the prepared membranes that would result after enzymatic treatment. These results are supported by surface roughness results that show the surface of the κ-carrageenan/starch membranes became rougher after enzymatic treatment. The hydrophilicity of the prepared membranes was evaluated from contact angle (CA) measurements and the swelling ratio. The swelling ratio of the prepared membranes increased gradually as the starch ratio increased, reaching 150%, while the water-uptake capacity increased from 48 ± 4% for plain κ-carrageenan to 150 ± 5% for 1:2 κ-carrageenan/starch blends. The amylase enzyme showed an effective ability to degrade both the plain κ-carrageenan and κ-carrageenan/starch membranes, and release glucose units for up to 236 and 563, respectively. According to these results, these blends could be effectively used in making safe and biodegradable molded toys with superior water-absorbing capabilities.

Lightweight Neural Network for Real-Time Crack Detection on Concrete Surface in Fog

Cracks are one of the most common factors that affect the quality of concrete surfaces, so it is necessary to detect concrete surface cracks. However, the current method of manual crack detection is labor-intensive and time-consuming. This study implements a novel lightweight neural network based on the YOLOv4 algorithm to detect cracks on a concrete surface in fog. Using the computer vision algorithm and the GhostNet Module concept for reference, the backbone network architecture of YOLOv4 is improved. The feature redundancy between networks is reduced and the entire network is compressed. The multi-scale fusion method is adopted to effectively detect cracks on concrete surfaces. In addition, the detection of concrete surface cracks is seriously affected by the frequent occurrence of fog. In view of a series of degradation phenomena in image acquisition in fog and the low accuracy of crack detection, the network model is integrated with the dark channel prior concept and the Inception module. The image crack features are extracted at multiple scales, and BReLU bilateral constraints are adopted to maintain local linearity. The improved model for crack detection in fog achieved an mAP of 96.50% with 132 M and 2.24 GMacs. The experimental results show that the detection performance of the proposed model has been improved in both subjective vision and objective evaluation metrics. This performs better in terms of detecting concrete surface cracks in fog.

The Influence of Moss Colonization and Biochar Application on Evaporation Losses and Surface Crack in Shallow Carbonate–Derived Laterite During Dry–Wet Cycles

AimsSoil water deficit in karst mountain lands is becoming an issue of concern owing to porous, fissured, and soluble nature of underlying karst bedrock. It is important to identify feasible methods to facilitate soil water preservation in karst mountainous lands. This study aims to seek the possibility of combined utilization of moss colonization and biochar application to reduce evaporation losses in carbonate-derived laterite.MethodsThe treatments of the experiments at micro-lysimeter included four moss spore amounts (0, 30, 60, and 90 g·m−2) and four biochar application levels (0, 100, 400, and 700 g·m−3). The dynamics of moss coverage, characteristics of soil surface cracks and surface temperature field were identified. An empirical evaporation model considering the interactive effects of moss colonization and biochar application was proposed and assessed.ResultsMoss colonization reduced significantly the ratio of soil desiccation cracks. Relative cumulative evaporation decreased linearly with increasing moss coverage under four biochar application levels. Biochar application reduced critical moss coverage associated with inhibition of evaporation by 33.26%-44.34%. The empirical evaporation model enabled the calculation of soil evaporation losses under moss colonization and biochar application, with the R2 values ranging from 0.94 to 0.99.Conclusions Our result showed that the artificially cultivated moss, which was induced by moss spores and biochar, decreased soil evaporation by reducing soil surface cracks, increasing soil moisture and soil surface temperature.Moss colonization and biochar application has the potential to facilitate soil moisture conservation in karst mountain lands.

Non-Destructive Measurement and Evaluation of Surface Cracks Using Ultrasonic Rayleigh Waves – A Review

Quality control and inspection methods have become a critical challenge in everyday situations of the engineering profession. This is due to the evolution of the materials used today in industry and also increasingly complex and critical nature of many of the products and structures produced with them. Ultrasonic measurement is widely used especially in oil and gas and aerospace industries. This method is used because it is effective and not involving damaging the original parts. In ultrasonic measurement there are few types of waves emitted and where one of it is Rayleigh wave or mostly known as surface wave. Surface waves are generated when longitudinal waves intersects a surface near to the second critical angle. This review paper will describe about the types of waves emitted and produce and also some of the research that has been done related to the surface wave. This research can contribute to green environment because it reduces waste by suggesting the uses of Perspex.