Exploring combined dark and bright field illumination to improve the detection of defects on specular surfaces

Forte, Paulo M.F.; Felgueiras, Paulo Eduardo Reis; Ferreira, Flávio P.; Sousa, Marta Noronha e; Nunes-Pereira, Eduardo J.; Bret, Boris P. J.; Belsley, M.

doi:10.1016/j.optlaseng.2016.08.002

Cited by 27 publications

(4 citation statements)

References 8 publications

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“…However, the ring light that is significantly larger than the cutting tool, whose image is to be captured, is chosen for a homogenous light distribution. Shadows and specular reflections are avoided by using diffused bright-field front lighting [50]. All the experiments are conducted under dry machining conditions.…”

Section: Resultsmentioning

confidence: 99%

Tool life prognostics in CNC turning of AISI 4140 steel using neural network based on computer vision

Bagga

Makhesana

Darji

et al. 2022

Int J Adv Manuf Technol

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One of the essential requirements for intelligent manufacturing is the low cost and reliable predictions of the tool life during machining. It is crucial to monitor the condition of the cutting tool to achieve cost-effective and high-quality machining. Tool conditioning monitoring (TCM) is essential to determining the remaining useful tool life to assure uninterrupted machining to achieve intelligent manufacturing. The same can be done by direct and indirect tool wear measurement and prediction techniques. In indirect methods, the data is acquired from the sensors resulting in some ambiguity, such as noise, reliability, and complexity. However, in direct methods, the data is available in images resulting in significantly less chances of ambiguity with the proper data acquisition system. The direct methods, which provide higher accuracy than indirect methods, involve collecting images of worn tools at different stages of the machining process to predict the tool life. In this context, a novel tool wear prediction system is proposed to examine the progressive tool wear utilizing the artificial neural network (ANN). Experiments were performed on AISI 4140 steel material under dry cutting conditions with carbide inserts. The cutting speed, feed, depth of cut, and white pixel counts are considered as input parameters for the proposed model, and the flank wear along with remaining tool life is predicted as the output. The worn tool images were captured using an industrial camera during the turning operation at regular intervals. The ANN training set predicts the remaining useful tool life, especially the sigmoid function and rectified linear unit (ReLU) activation function of ANN. The sigmoid function showed an accuracy of 86.5%, and the ReLU function resulted in 93.3% accuracy in predicting tool life. The proposed model’s maximum and minimum root mean square error (RMSE) is 1.437 and 0.871 min. The outcomes showcased the ability of image processing and ANN modeling as the potential approach for developing a low-cost industrial tool condition monitoring system that can measure tool wear and predict tool life in turning operations.

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Section: Resultsmentioning

confidence: 99%

Tool life prognostics in CNC turning of AISI 4140 steel using neural network based on computer vision

Bagga

Makhesana

Darji

et al. 2022

Int J Adv Manuf Technol

View full text Add to dashboard Cite

show abstract

“…The low-level activity typically observed among TS0 particles is consistent with submicron changes, whereas medium-and high-level activity, observed with more delayed supply of Ce(III), are clearly related to micron-scale deposits. This correspondence is attributed to the scattering of incident light by the deposits [37]. As the deposits grow larger, they also scatter more light, resulting in a greater reduction in the intensity of light reflected back to the microscope.…”

Section: Activity Levels and Extent Of Inhibitor Depositionmentioning

confidence: 96%

Effect of Delayed Inhibitor Supply on Aa2024-T3 Intermetallic Activity: A Local in Situ Analysis with Reflected Microscopy

Mopon,

Garcia,

Mol

2023

Preprint

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“…Therefore, timely inspection for surface defects of injection moulded products is crucial for controlling product quality, optimising the manufacturing process, and enhancing the benefits of the enterprise. Currently, machine vision is considered to be an effective method for automated surface defect inspection of industrial products [9][10][11]. However, there are various types of surface defects in injection moulded products, such as discontinuous defects, overlapping defects, and highly similar defects, etc., and these defects have low contrast to the background.…”

Section: Introductionmentioning

confidence: 99%

A novel deep learning algorithm applied to machine vision inspection for surface defects of injection moulded products

Fan,

Qiu

2024

Meas. Sci. Technol.

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In modern industry, the surface defect inspection of injection moulded products is crucial for controlling product quality and optimizing the manufacturing process. With the development of optical measurement and computer technology, machine vision inspection methods have been widely adopted instead of manual inspection. However, current machine vision inspection methods are difficult to simultaneously ensure the accuracy and efficiency of surface defect inspection of injection moulded products. Considering this problem, a novel deep learning algorithm applied to machine vision inspection for surface defects of injection moulded products is proposed. To train and evaluate the proposed deep learning algorithm, an image acquisition platform is established and the dataset of surface defects in moulded products is obtained. In the proposed deep learning algorithm, reparameterization-based convolution modules are employed for feature extraction and feature fusion. A median iterative clustering algorithm based on hierarchical clustering initialization is proposed to obtain prior anchors that are highly matched with the actual distribution of defect sizes. A novel Focus-Entire Union over Covering (Focus-EUoC) loss function is utilized for bounding box regression. On these bases, the proposed deep learning algorithm applied to machine vision inspection is evaluated on the dataset of surface defects in moulded products. The experimental results indicate that compared to the traditional inspection algorithms and other deep learning algorithms currently used in machine vision inspection, the proposed deep learning algorithm exhibits superior inspection accuracy and inspection efficiency on the acquired dataset. The inspection precision reaches 0.964, the inspection recall reaches 0.955, and the inference time for each subgraph is only 6.1ms, confirming its effectiveness.

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Exploring combined dark and bright field illumination to improve the detection of defects on specular surfaces

Cited by 27 publications

References 8 publications

Tool life prognostics in CNC turning of AISI 4140 steel using neural network based on computer vision

Tool life prognostics in CNC turning of AISI 4140 steel using neural network based on computer vision

Effect of Delayed Inhibitor Supply on Aa2024-T3 Intermetallic Activity: A Local in Situ Analysis with Reflected Microscopy

A novel deep learning algorithm applied to machine vision inspection for surface defects of injection moulded products

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