Image Of Workpiece Research Articles

A MIL-based approach for welding defect classification

Welding quality plays a prominent role in many fabrication processes, such as those in the automotive, aerospace, shipbuilding, and electronics industries. Deep learning facilitates the automatic classification of welding quality using non-invasive techniques such as X-rays, thermal imaging, and ultrasonic techniques. In this study, we investigate the ability of deep learning to detect welding quality directly from the welded workpieces. Typically, good and bad weld samples can be seen together along with surrounding material as a background, which makes traditional classification based using computer vision difficult. Therefore, we propose a new framework based on Multiple Instance Learning (MIL) that reveals the distribution of each small piece of the image and serves as an effective guide for welding quality classification. Furthermore, a new collection of welding image datasets is developed to verify the baseline model and our proposed method. The results show that our proposed method is effective in classifying welding quality in welding workpiece images and identifying welding features in each small section of the welding area in workpiece images. This study demonstrates the effectiveness of incorporating MIL in weakly supervised welding quality classification and reveals the potential for performing quality control on welding in industrial pipelines automatically.

Performance assessment of graphene oxide based nano-cutting fluids during sustainable hard turning through synthesis, characterization and machinability investigation

Achieving sustainability in manufacturing necessitates the implementation of eco-friendly solutions by adopting environmentally friendly coolants with cost-effective, energy-efficient and cleaner production processes without posing any risk to the operator's health. In this current work, mineral oil-based graphene oxide (GO) nano-cutting fluids of different concentration (0.05, 0.1, 0.2, 0.3, 0.4, 0.5 % wt.) are synthesized and their characteristics such as rheological, thermo-physical and tribological properties along with their stability and toxicology impact are thoroughly investigated. Further, the influence of nano-cutting fluid concentration on machinability and sustainability aspects during hard turning such as flank wear, surface integrity, power consumption, cutting temperature, carbon and noise emissions are investigated. According to the result an enhancement of 28.83 % and 29.54 % in viscosity and thermal conductivity have been recorded when concentration of nanofluid increases from 0.05 % to 0.5 %. GO nano-cutting fluids shows anti wear and friction reduction properties by providing excellent lubricious properties, as a result coefficient of friction has been reduced up to 28.36 % at 0.5 % wt. GO compared to plane mineral oil. A notable reduction in flank wear, cutting temperature, surface roughness, cutting temperature, power consumption, carbon emission and noise emission were noticed to be 25.96 %, 27.82 %, 23.86 %, 25.22 %, 11.66 % and 2.72 % respectively when the concentration of GO nanoparticles increases from 0.05 % to 0.5 % wt. Smoother surface with minimal defect has been noticed from the SEM and AFM image of machined workpiece at highest concentration (0.5 % wt.) of GO due to the synergetic effect of GO nano-cutting fluid and dual nozzle MQL system.

High-Frequency Workpiece Image Recognition Model Integrating Multi-Level Network Structure.

High-frequency workpieces have the characteristics of complex intra-class textures and small differences between classes, leading to the problem of low recognition rates when existing models are applied to the recognition of high-frequency workpiece images. We propose in this paper a novel high-frequency workpiece image recognition model that uses EfficientNet-B1 as the basic network and integrates multi-level network structures, designated as ML-EfficientNet-B1. Specifically, a lightweight mixed attention module is first introduced to extract global workpiece image features with strong illumination robustness, and the global recognition results are obtained through the backbone network. Then, the weakly supervised area detection module is used to locate the locally important areas of the workpiece and is introduced into the branch network to obtain local recognition results. Finally, the global and local recognition results are combined in the branch fusion module to achieve the final recognition of high-frequency workpiece images. Experimental results show that compared with various image recognition models, the proposed ML-EfficientNet-B1 model has stronger adaptability to illumination changes, significantly improves the performance of high-frequency workpiece recognition, and the recognition accuracy reaches 98.3%.

Image-based feature extraction for inline quality assurance and wear classification in high-speed blanking processes

Wear is one of the key factors that determine the efficiency of multi-stage processes that include blanking operations. Since wear in these processes not only causes unplanned downtime but also directly affects product quality, inline detection of wear and its effect on product quality is of major importance. However, current quality assurance (QA) methods are limited to manual offline inspection by operators at predefined intervals, so that 100% inspection of the product and description of the state of wear is not found in industrial practice. The aim of this work is therefore to develop an optical system that enables in-line acquisition of product images and the associated control of blanking-specific quality features up to stroke rates of 300 strokes per minute (spm). In order to make the system attractive to small- and medium-sized enterprises (SME), the system is designed to minimize integration and investment costs using commercially available components. By combining the system with a methodology for extracting blanking-specific features, so-called key performance parameters (KPPs), the condition of the blanked surface as a relevant quality parameter is derived directly from the workpiece image. To demonstrate the transferability of the methodology to industrial applications, two use cases are investigated. In the first case, the KPPs are used directly to determine the quality of the blanked workpiece and are compared with reference measurements. Here, the KPPs are quantified with a mean absolute error of 18 μm compared to a ground truth. In the second case, the KPPs are used to build a machine learning (ML) model to estimate the wear of the blanking tool. Here, an accuracy of 92% is achieved in classifying the actual wear state.

Robot Target Location Based on the Difference in Monocular Vision Projection

Visual guidance is widely used in industrial robots. At present, the traditional method of template matching and positioning is often used in industrial robots under monocular vision guidance. However, for complex workpieces with height differences, if the angle and position of the workpiece are not consistent with the template, the projection will be different, and the positioning accuracy will be obviously reduced. A new method is proposed to solve this problem by dividing the whole contour of the workpiece and using the weighted method of the contour module to correct and match the workpiece image. First, the contour region of the template image is divided according to the position of the grasping points. Then the weight is assigned according to the distance from each contour region to the grasping points. Then, a fast feature point matching method is used to match the initial image of the workpiece to be measured. Finally, accurate contour matching is carried out for each weighted contour region so that the robot can grasp the object accurately. A large number of experiments show that the method has the design requirements of high stability and high accuracy.

Multi-object surface roughness grade detection based on Faster R-CNN

In a realistic scenario where a large number of workpieces need to be measured, any measurement method that can detect roughness only for a single workpiece is very limited in terms of measurement efficiency. To address this problem, a multi-object surface roughness detection model based on Faster R-CNN is proposed in this paper. The model features milled workpiece images with a convolutional neural network. And the obtained features will feed into a Region Proposal Network for inferring those regions where workpieces may be present. The regions and features go through a ROI pooling layer and a predictor to get more accurate target regions and measure the roughness of the workpieces in the regions. The experimental results show that the model proposed in this paper can accurately detect those regions where workpieces are present in the image and detect the corresponding roughness grade of the workpieces. A mean average precision of 97.80% and a detection speed of 5.82 fps for the test set of milled workpieces were achieved by the model under different placement angles and variable light conditions.

Taguchi’s design optimization for finishing of plane surface with diamond-based sintered magnetic abrasives

The magnetic field-assisted abrasive finishing parameters used in this study were the distance of the workpiece from the magnetic pole (D), amount of sintered magnetic abrasives (A), speed of rotation of the pole (R), grain size of the abrasive particles (S) and feed to the workpiece (F). The surface roughness percentage change (SRPC) and material removal rate (MRR) of the plane surface workpiece of aluminum are the response quantities. An orthogonal L16 array of the Taguchi method was constructed and is used in this study to analyze the influence of finishing parameters on the surface roughness percentage change of the workpiece. The analysis of variance (ANOVA) showed the extent to which finishing parameters are important. The experimental results showed that for SRPC, the best combination of levels for the given control factors is the distance of the workpiece from the magnetic pole (D) = 4 mm, the amount of sintered magnetic abrasive (A) = 16 g, the speed of rotation of the pole (R) = 600 rpm, the grain size of the abrasive particles (S) = 175 μm and the feed rate of the workpiece (F) = 1.5 m min−1. For MRR, the best combination of levels for the given control factors is the distance of the workpiece from the magnetic pole (D) = 4 mm, the amount of sintered magnetic abrasive (A) = 16 g, the speed of rotation of the pole (R) = 400 rpm, the grain size of the abrasive particles (S) = 175 μm and the feed rate of the workpiece (F) = 6.0 m min−1. The surface profiles and microscopic images of polished and unpolished workpieces show that the magnetic field-assisted abrasive finishing process is very effective in finishing aluminum surfaces.

Fusion of multi-light source illuminated images for effective defect inspection on highly reflective surfaces

It is observed that a human inspector can obtain better visual observations of surface defects via changing the lighting/viewing directions from time to time. Accordingly, we first build a multi-light source illumination/acquisition system to capture images of workpieces under individual lighting directions and then propose a multi-stream CNN model to process multi-light source illuminated images for high-accuracy surface defect classification on highly reflective metal. Moreover, we present two effective techniques including individual stream deep supervision and channel attention (CA) based feature re-calibration to generate and select the most discriminative features on multi-light source illuminated images for the subsequent defect classification task. Comparative evaluation results demonstrate that our proposed method is capable of generating more accurate recognition results via the fusion of complementary features extracted on images illuminated by multi-light sources. Furthermore, our proposed light-weight CNN model can process more than 20 input frames per second on a single NVIDIA Quadro P6000 GPU (24G RAM) and is faster than a human inspector. Source codes and the newly constructed multi-light source illuminated dataset will be accessible to the public.

Towards high-performance deep learning models in tool wear classification with generative adversarial networks

Image-based deep learning (DL) applications have been increasingly applied in manufacturing processes in recent years and enable real-time decisions to be derived on the basis of raw image data. However, there are major obstacles that prevent a widespread application. The generation of extensively labeled datasets is associated with costly experiments, long downtimes and demanding measurement setups. Thus, users are usually faced with a trade-off problem, where they have to weigh between the profitability and the performance of their DL algorithms. For this reason, this paper demonstrates how this obstacle can be tackled through the application of data augmentation (DA) techniques. The investigations are based on an image dataset from a tool wear classification task in a blanking process, in which a pre-trained convolutional neural network (CNN) identifies correlations between cutting punch radii and workpiece images. It is shown how synthesized images in combination with transfer learning (TL) models affect classification accuracies using basic image manipulation, different types of generative adversarial networks (GAN) and their hybrid application, varying the number of available images in the training dataset between 160 and 4800. These augmentation techniques lead to accuracy improvements of up to 18%, although their effectiveness depends heavily on the amount of image data available.

Review of Industry Workpiece Classification and Defect Detection using Deep Learning

Object detection and classification denotes one of the most extensively-utilized machine vision applications given the high requirements put forward for object classification and defect detection with the rise of object recognition scenes. Notwithstanding, conventional image recognition processing technology encounters specific drawbacks. Its benefits and limitations were duly compared upon selecting several typical conventional image recognition techniques. Resultantly, such recognition approaches required multiple manual participation elements and extensive manpower with restricted object identification. As a branch of machine learning, deep learning has attained more optimal results in the image recognition discipline. In the classification and defect detection of industrial workpieces, over 70 literature reviews of deep learning algorithms across multiple application scenarios for classical algorithm model and network structure assessment based on the deep learning theory. Relevant network model performance was compared and analyzed based on network intricacies parallel to natural image classification. Six research gaps were found based on the reviewed algorithm pros and cons. The corresponding six research proposal in workpiece image classification was highlighted with prospects on the workpiece image classification and defect detection direction development. It provides an empirical solution for the selection of workpiece classification and defect detection deep learning model in the future.

Design of On-line Detecting Device for Groove Wear of Elevator Traction Wheel

After the wear of elevator traction wheel, there is a traditional detection method which can measure the geometric dimensions of wheel groove. But this method has low measurement accuracy and cannot accurately reflect the actual profile of wheel groove. By means of the machine vision inspection technology, the profile of the traction wheel groove can be measured, a new detection method is proposed in this paper, and the corresponding measuring device is designed in detail. At the same time, with help of the computer simulation, the processing of the work piece image is used. In this method, the size and profile of the wheel groove of the traction wheel can be measured more accurately and quickly to reflect the actual wear value of the wheel groove.

The Acquisition of Position and Orientation of the Conveyor Belt Workpiece Based on the Inter Frame Difference in ROI in Camera Video

Conveyor belt transfer is a widely used transportation means in industry and agriculture, with the help of the robot arms the workpiece on the belt can be picked and placed, replacing human sorters for production lines work. The position and orientation of the workpiece are important for grabbing by the robot arms. The goal of the paper was to investigate the acquisition of the position and orientation of the conveyor belt workpiece by means of the camera video overhead looking down the belt. The proposed method is the inter frame difference in nature, using the conveyor belt background as the first frame, but the other frames were not used wholly as usually, only an ROI all around the conveyor belt in the camera video was chosen, and the inter frame difference was carried out in the ROI. The ROI was of the same width as that of the belt in the video which was known in advance, while the length of the ROI was arbitrary, so one pixel in the frame was scaled to the actual length conveniently. Every read frame behind the background was computed the difference with the background in such ROI, and the four vertexes coordinates of the rectangle workpiece image on the belt were obtained when it passed the ROI, and then the distance apart from the right belt boundary was calculated due to the proportional relation between the width of workpiece and that of the ROI. Two kind workpiece orientation on the belt toward the left and right were judged using the same obtained four vertexes coordinates by means of Euclidian length, and the tilt angle was calculated by arc tangent function in favour of two narrow sides of rectangle workpiece grab. The actual test showed that the method of obtaining the position and orientation of workpiece on the belt proposed in the paper could be realized correctly.

Application of Computer Vision Technology in Industrial Automation

With the continuous development of artificial intelligence and Internet of Things technology, the manufacturing industry is transforming and upgrading from automation to intelligence. Countries continue to increase support and investment in intelligent manufacturing from the policy and financial aspects. This paper mainly studies the application of computer vision technology in the field of industrial automation. In this paper, the sparse representation algorithm is applied to the category detection of artifacts with arbitrary orientation. By establishing the Gabor feature dictionary of the workpiece and solving the sparse reconstruction solution according to the feature vector of the detected workpiece image, the category label of the workpiece is obtained. Aiming at the reconstruction of 3D point cloud of workpiece, this paper uses the method of feature point matching to realize the reconstruction of 3D point cloud. Finally, the workpiece detection method studied in this paper is implemented on the system and the performance of the algorithm is verified.

An online visual measurement method for workpiece dimension based on deep learning

Image preprocessing and edge detection are the two most critical steps when applying machine vision to measure workpiece dimension in industrial field. The surface of the workpiece is usually covered with interference regions, which makes edge detection difficult. Aiming at the problem, the Full Convolutional Network (FCN) model is used to detect interference regions of workpiece images, and then interference regions are processed by the directional texture repair method. To avoid the influence of workpiece surface texture after rough machining, a method based on Holistically-nested Edge Detection (HED) model for rough edge detection of workpiece images is proposed. The edge is obtained by HED, and then it is post-processed based on the non-maximum value suppression and double threshold segmentation methods in the Canny operator to obtain a refined edge image. In order to further improve the accuracy of the dimension measurement, sub-pixel level edge detection accuracy is achieved by cubic spline interpolation. Finally, the method is validated by a shaft workpiece. The measuring accuracy of the outer diameter of the workpiece can reach 0.02 mm, which can effectively meet the requirements of fast semi-finishing inspection. This research provides a common workpiece dimension measurement method in industrial field and methodological guidance for the application of deep learning in industrial inspection.

An Improved Tool Wear Monitoring Method Using Local Image and Fractal Dimension of Workpiece

Tool wear is a key factor that dominates the surface quality and distinctly influences the generated workpiece surface texture. In order to realize accurate evaluation of the tool wear from the generated workpiece surface after machining process, a new tool wear monitoring method is developed by fractal dimension of the acquired workpiece surface digital image. A self-made simple apparatus is employed to capture the local digital images around the region of interest. In addition, a skew correction method based on local fast Fourier transformation energy is also proposed for the surface texture direction adjustment. Furthermore, the tool wear quantitative evaluation was derived based on fractal dimension utilizing its high reliability for inherent irregularity description. The proposed tool wear monitoring method has verified its feasibility as well as its effectiveness in actual milling experiments using the material of AISI 1045 in a vertical machining center. Testing results demonstrate that the proposed method was capable of tool wear condition evaluation.

Research on Workpiece Image Mosaic Technology of Groove Cutting Robot

Research on Workpiece Image Mosaic Technology of Groove Cutting Robot

Automatic 3D Seam Extraction Method for Welding Robot Based on Monocular Structured Light

The teaching programming mode and offline programming mode of welding robot are essential parts in today's manufacturing industry. However, these modes can't meet the automation requirements and adaptive ability of welding robot. To achieve 3D path acquisition of weld seam for robot autonomous programming, a fast and accurate offline 3D seam extraction method is proposed based on monocular structured light sensor. In this method, gray code and phase-shift code raster images are projected to the workpiece, and the 3D coordinates of the weld are calculated by collecting the workpiece images with raster patterns. The path fitting of robot is completed by polynomial fitting method. The novel monocular structured light sensor used by this paper has compact structure and small volume, and can adapt to a variety of welding working scenes. The combination of gray code and phase shift code makes the measurement accurate and fast, and achieves the effect of full resolution decoding. The experimental results show that the proposed method can complete the task of 3D weld extraction and path fitting, which has the characteristics of high efficiency and strong robustness.

An enhancement framework based on gradient domain tone mapping and fuzzy logical for X-ray image of complex workpiece

X-ray images of complex workpiece often suffer low brightness and contrast in industrial defect inspection. In this paper, we propose an enhancement framework to improve the visual quality. In detail, a logarithm transformation is first to increase global brightness, and then, to increase local contrast, we propose a novel gradient domain tone mapping operator, in which we design a new gradient attenuation function based on fuzzy entropy and construct a new cost function constrained by a hybrid regularization. Finally a fuzzy enhancement operator is performed on the result obtained by minimizing the new cost function, to further increase contrast and prevent halo artifacts created by manipulating the gradient domain. Experiments show that the proposed method produces good visual effect for defect inspection for X-ray image of complex workpiece.

High-precision visual localization based on an improved shape-based matching algorithm.

To deal with the requirement of high-precision localization of large-size workpieces in an industrial environment, an improved shape-based matching algorithm is proposed based on the phase stretching transformation and the iterative closest point algorithms. Basler industrial cameras are used to collect images of large-size workpieces, such as glass. The experimental results show that the average localization error is 0.05±0.013mm, which can meet the requirements of practical applications. This algorithm can effectively and accurately achieve high-precision localization of different positions of multi-directionally transformed objects in industrial environments.

Surface roughness accuracy prediction in turning of Al7075 by adaptive neuro-fuzzy inference system

This research proposes the surface roughness inspection by an adaptive Neuro-fuzzy inference system. The adaptive Neuro-fuzzy inference system model developed by input parameters (Speed, Depth of cut, feed rate, and Grayscale value) and an output parameter (surface roughness). The training and testing module, which is used to generate the surface roughness value. The grayscale value derived from the machinability of the Al7075 workpiece. The machined workpiece image as converted as grayscale value, which is feed into one of the inputs of the adaptive Neuro-fuzzy inference system. The vision measurement value was compared with the stylus probe value for predicting the accuracy level of the adaptive neuro-fuzzy inference system. The accuracy was above 98%, which is helpful for inspecting all machined components in the mass production system.