Practical Industrial Environment Research Articles

Optimization of target detection model based on Yolov5 network with small samples

As an important part of automotive shock absorber, the columnar parts in automotive shock absorber will inevitably have machining defects during the process, which will not only degrade the performance of the parts, but also degrade or even fail the performance of the final shock absorber after assembly. Yolov5, as a target detection algorithm, has received much attention due to its high accuracy and fast operation speed. However, the algorithm faces some challenges when applied in a practical industrial environment. In this paper, improvement measures are proposed to address the limitations of sample collection and the high speed of pipeline recognition in industrial environments. The network model is optimized and designed. Firstly, the ASPP module is replaced by the SPP module thus improving the viewability throughout the process providing recognition accuracy. Secondly, the Conv and C3 layers of Yolov5s are replaced by Transformer to obtain higher recognition accuracy. By improving and optimizing the above methods, we can better cope with the improvement of detection accuracy under small sample conditions. Experiments show that the method can significantly improve the detection accuracy and operation speed of Yolov5s under the hardware condition of lower computing power, which is more suitable for industrial scenario application scenarios.

An efficient automobile assembly state monitoring system based on channel-pruned YOLOv4 algorithm

ABSTRACT An efficient automobile assembly state monitoring system in industrial environment is presented in this paper. The system only needs to input a video that contains the whole detected parts and manually label it in the first frame. By finding the best point for tracking and tracking the point, the dataset can be automatically generated which saves time spent on manufacturing the dataset and makes the assembly state monitoring system easy to deploy into a practical industrial environment. The target detection algorithm uses the channel-pruned YOLOv4 neural network. The experimental result shows the algorithm balances speed and accuracy. Compared to original YOLOv4, the proposed method is two times faster and the performance is nearly equal to it. Comparative experiments show that the proposed algorithm performs better and is faster than other lightweight models which demonstrates that the channel pruning process dynamically improves the speed of the forward propagation without sacrificing accuracy. Additionally, the algorithms are deployed on two common embedded systems. The results show that in the industrial environment, the speed can fully meet real-time requirements.

An industrial intelligent grasping system based on convolutional neural network

PurposeThis paper aims to save time spent on manufacturing the data set and make the intelligent grasping system easy to deploy into a practical industrial environment. Due to the accuracy and robustness of the convolutional neural network, the success rate of the gripping operation reached a high level.Design/Methodology/ApproachThe proposed system comprises two diverse kinds of convolutional neuron network (CNN) algorithms used in different stages and a binocular eye-in-hand system on the end effector, which detects the position and orientation of workpiece. Both algorithms are trained by the data sets containing images and annotations, which are generated automatically by the proposed method.FindingsThe approach can be successfully applied to standard position-controlled robots common in the industry. The algorithm performs excellently in terms of elapsed time. Procession of a 256 × 256 image spends less than 0.1 s without relying on high-performance GPUs. The approach is validated in a series of grasping experiments. This method frees workers from monotonous work and improves factory productivity.Originality/ValueThe authors propose a novel neural network whose performance is tested to be excellent. Moreover, experimental results demonstrate that the proposed second level is extraordinary robust subject to environmental variations. The data sets are generated automatically which saves time spent on manufacturing the data set and makes the intelligent grasping system easy to deploy into a practical industrial environment. Due to the accuracy and robustness of the convolutional neural network, the success rate of the gripping operation reached a high level.

A novel antibody population optimization based artificial immune system for rotating equipment anomaly detection

Rotating machines are one of the most common equipments in modern industry, effective fault detection and diagnosis methods are vital to equipment health monitoring. In industrial production, the known information of fault types is insufficient generally, especially for constructing complex equipment and components. In previous studies of equipment fault detection, accurate fault classification and diagnosis methods have been presented, while seldom takes the condition of paucity of fault data into account. Therefore, this paper presents a novel antibody population optimization based artificial immune system (APO-AIS) for rotating equipment anomaly detection. The proposed approach can detect abnormal events while monitoring the operating condition. Meanwhile, an antigen-based antibody selecting method, a density-based antibody screening method and an optimized judgment rule based on individual difference are presented for improving the iteration evolution. The presented methods and optimized judgment rule enhance the robustness and reduces training burden for the proposed approach, which leads to accurate anomaly detection in strong background noise and in practical industrial environment. The effectiveness and robustness of the proposed method has been proven experimentally by bearing fault diagnosing and centrifugal pump condition monitoring in this paper.

An adaptive mesh refinement of quadrilateral finite element meshes based upon a posteriori error estimation of quantities of interest: linear static response

A posteriori error estimation in finite element analysis serves as an important guide to the meshing tool in an adaptive refinement process. However, the traditional posteriori error estimates, which are often defined in the energy or energy-type norms over the entire domain, provide users insufficient information regarding the accuracy of specific quantities in the solution. This paper describes an adaptive quadrilateral refinement process with a goal-oriented error estimation, in which a posteriori error is estimated with respect to the specified quantity of interest. A highlight of this paper is the demonstration of tools described in the paper used in a practical industrial environment. The performance of this process is demonstrated on several practical problems where the comparison is with the adaptive process based on the traditional error estimation.

Monitoring for N,N-dimethylformamide and N,N-dimethylacetamide with a diffusive sampler using distilled water as an absorbent.

To obtain an appropriate sampling method for evaluating individual workers' exposure concentrations to chemicals highly soluble in water, the suitability of LiPS (liquid passive sampler) as diffusive sampler was examined. Although adsorbed chemical substances with activated carbon felt being used as the adsorbent must be desorbed in such samplers, this process can be omitted by using liquid as the absorbent. This kind of simplification can be utilized for water-soluble solvents such as N,N-dimethylformamide (DMF) and N,N-dimethylacetamide (DMAC). In laboratory experiments DMF and DMAC levels determined by this method were sufficiently correlated with exposure concentrations, exposure duration, and cumulative exposure levels. The sampling rates for DMF and DMAC obtained from these results were 0.50 and 0.43 mL/min, respectively, and the theoretical sampling rate for DMF obtained using Fick's diffusion formula was consistent with the experimental value. The effects of relative humidity, wind velocity, and temperature on the measured values were negligible within the range expected in a practical industrial environment. In a field survey the exposure concentrations for 38 workers handling DMF in the process of manufacturing spandex fiber and polyurethane gloves were determined both by this method and by the National Institute of Occupational Safety and Health (NIOSH) method, in which silica gel tubes are used. The DMF concentrations detected by the two methods were comparable (Y = 0.912X, correlation coefficient: 0.932). This LiPS method proved to be useful in determining the concentrations of DMF and DMAC to which workers were exposed.

Knowledge-based selection and sequencing of hole-making operations for prismatic parts

This paper describes the rule structure used to select the appropriate sequence of operations to produce various types of holes in 2 1/2D prismatic components manufactured on machining centres. Operation selection is based on forward chained rules coded in Prolog. A different rule priority sequence is applicable depending on the rule fired last. This solves the conflict resolution problem and ensures reasonably fast execution. Rule conditions perform feature extraction, feature size checks, operation suitability checks, tool suitability checks and processing status checks. Actions perform the processing operations and update the model through feature processing tags. The knowledge base implemented for hole-making was not obtained from a practical industrial environment. Nevertheless it does produce sensible plans and it is relatively easy to modify.