Performance Of Inspection System Research Articles

Deep Learning-Based Automated Optical Inspection System for the Additive Manufacturing of Diamond Tools.

Cutting tools with orderly arranged diamond grits using additive manufacturing show better sharpness and longer service life than traditional diamond tools. A retractable needle jig with vacuum negative pressure was used to absorb and place grits in an orderly arranged manner. However, needle hole wear after a long service time could not promise complete grit adsorption forever. This article proposed an improved YOLOv5s to detect the adsorption status of diamond grits on pinholes to maintain the planting rate of diamond grits in each matrix during the additive manufacturing process. First, the added detection head extracts higher level semantic information. Second, depthwise separable convolution + batch normalization + sigmoid linear unit modules containing depthwise separable convolutions (DSC) are used instead of convolution + batch normalization + sigmoid linear unit to reduce the number of parameters. Introducing DSC into the Bottleneck1 module results in faster computational speed than introducing bottleneck. Finally, coordinate attention is added at appropriate locations to improve detection accuracy. The improved YOLOv5s achieves an average 19.6% reduction in both parameters and floating point operations per second. The inspection system performance was validated by collecting data on a large number of vacancies and worn vacancy pinholes. Compared with the original YOLOv5s, the detection time for a layer of diamond grits with the system based on the improved YOLOv5s model decreased from 6.35 to 5.06 ms, and the detection accuracy was higher than 98%. When the absorption rate was detected below 95%, a redo command was given. The equipment has been in continuous operation for 1 year, and the vacancy rate of diamond grits in the orderly arranged diamond green segment produced by this additive manufacturing equipment is less than 5%.

Standard wafer with programed defects to evaluate the pattern inspection tools for 300-mm wafer fabrication for 7-nm node and beyond

Background: Standard patterned sample with programed defects (PDs) is effective to evaluate the tool performance of pattern inspection system, but the fabrication of such standard sample, having large area dense patterns with PDs suitable for the evaluation of sub-7-nm node, is difficult. Aim: The goal of this study is to fabricate a standard sample to evaluate the performance of inspection tool for below 7-nm nodes. Approach: We use electron beam lithography with an acceleration voltage of 130 keV to fabricate standard sample. Results: We form large area dense sub-16-nm half pitch (hp) line and space (LS) patterns with PDs on 300-mm-Si-wafers, and 10- to 7-nm hp LS patterns on a 100-mm-Si wafer. Approximately 5-nm PDs with shapes including protrusions, intrusions, bridges, and openings are formed without additional defects. Moreover, pattern-etched Si wafers with 16- to 12-nm hp LS are successfully fabricated. A 100-mm-wafer with patterns is mounted into a 300-mm-Si wafer. Conclusions: The acceleration voltage of 130 keV is sufficient for the fabrication of large area dense pattern with PDs suitable for the evaluation of sub-7-nm node. Moreover, the fabricated standard wafers are useful to evaluate the tool performance of the inspection system for 300-mm wafer fabrication.

MEC-Driven UAV-Enabled Routine Inspection Scheme in Wind Farm Under Wind Influence

As a promising choice of alternative energy, wind power will account for a major part of energy generation in future Energy Internet. With the exploitation of wind power, multiple wind turbines (WTs) are deployed at remote and harsh areas, in which the adverse working environment may lead to enormous WT operating and maintenance costs. Deploying unmanned aerial vehicles (UAVs) for WT detection and sensory data processing in wind farms has been considered as a promising technology to reduce the costs and improve inspection efficiency. In this paper, a mobile edge computing (MEC) driven UAV routine inspection scheme is proposed, in which the UAV not only detects WTs in multiple sorties, but also provides computing and offloading services. To provide seamless communication service, UAV can offload the sensory data to the ground station or satellite optimally. In order to minimize the total completion time, we jointly optimize the UAV trajectory and computation operations, while guaranteeing the data processing accuracy. In the proposed scheme, in order to overcome the influence of wind on UAV trajectory planning, a low complexity WT routine inspection trajectory and UAV scheduling approach is designed firstly. Then, we present an iterative optimization solution to minimize the energy consumption of computation processing, via finding the optimal offloading trajectory and computation offloading parameters. Finally, simulation results show that the proposed scheme can effectively improve the efficiency of UAV routine inspection system performance.

A visual inspection system of glass ampoule packaging defects: effect of lighting configurations

ABSTRACTIn any pharmaceutical packaging process, error rates must be kept very low to prevent defective or incorrect medicine from reaching the consumer. Published articles include a focus on the inspection of ampoule content for the presence of contaminants. Methods of inspection include optical cameras that record sequential images to detect the movement of floating objects via feature extraction and classification. Other methods employ spectral imaging and dying of ampoules to detect cracks. Thermal imaging is also used to inspect structural integrity and mechanical stress; however, this approach is marked by relatively high cost. This paper describes the enhancement of an optical vision-based inspection system for a glass ampoule packaging process. The developed solution involves a reconfiguration of the illumination system of the ampoule trays as well as reprogramming of a real-time optical camera. The camera is linked to the process controller and triggers the ejection of trays containing faulty ampoules. Eight defect types are of interest, namely: missing ampoules; ampoules without a label; crushed ampoules; ampoules with a broken tip – present or missing; ampoules with a broken base; cracked ampoules; empty ampoules. The original inspection system performance was inadequate and only detected 2 of the 8 defect types reliably. The improved system which is described in this paper has adequately detected all aforementioned defect types. Experiments show that the overall detection rates have improved from 55% to 99.6% on average. Average camera detection accuracy has improved from 38% to 99.5%. In addition, the false positives, i.e. rejection rates, were reduced from 45% to 7%.

3D monitoring of woody crops using an unmanned ground vehicle

This paper presents an inspection system integrated into an on-ground autonomous platform with range of approximately 80 km. The vehicle is prepared to autonomously cover a field following a predefined route plan. Two types of cameras were integrated in the platform. An RGB-D sensor and a reflex camera were placed in a fixture and connected to a high-performance computer. The heterogeneous information acquired from the RGB-D was later integrated to automatically generate 3D maps of the crops by using custom software developed in the authors’ previous work. The inspection system performance was tested in actual vineyards by conducting several samplings in 2016. Results show that the proposed technology is viable and can provide complementary information to other inspection alternatives.

Quality Control of Slot-Die Coated Aluminum Oxide Layers for Battery Applications Using Hyperspectral Imaging

Hyperspectral inspection using imaging systems is becoming more and more important for quality control tasks in several industries, replacing well trained operators or established machine vision systems based on RGB-systems. Hyperspectral imaging (HSI) on thin coated substrates is challenging due to the high reflectivity of the substrates. Nevertheless, the thin films contribute to the spectral data and can be evaluated. Therefore, the performance of inspection systems can be increased significantly. However, the large amount of data generated by HSI has to be processed and evaluated for quality information about the product. In this paper, thin aluminum oxide (Al2O3) layers on stainless steel foil are investigated by HSI. These substrates can be used for the growth of vertically aligned carbon nanotubes (VA-SWCNT) for battery electrodes. HSI and spectral ellipsometry in combination with Partial Least Squares regression (PLS) was used to estimate the thickness of the Al2O3 layers and to calculate quality parameters for a possible monitoring process. The PLS model shows a R2CV of 0.979 and a RMSECV of 3.6.

Design and Optimization of Eddy Current Testing Probe Using Bees Algorithm and Finite Element Analysis

Eddy current testing (one of the non- destructive testing-NDT) is used to investigate heat exchanger tubes and detecting any possible faults in these tubes. Main aims of this paper determine the optimum design of a probe in order to improve sensitivity and inspection system performance. Firstly, this paper presents equations related to designing and characteristics of the probe to investigate sample tubes. Then, optimum design is presented in order to reach the highest signal to noise ratio (SNR) and sensitivity (S) using Bees Algorithm-BA. Finally, eddy current testing is performed for optimum probe using finite element analysis (FEA).

Laser energetics and propagation modelling for the NIF

Design, activation, and operation of modern high-energy, fusion-class lasers rely heavily on accurate simulation of laser performance. Setup, equipment protection, and data interpretation of the National Ignition Facility[1] (NIF) at Lawrence Livermore National Laboratory (LLNL) are being controlled by a Laser Performance Operations Model (LPOM) [2], which, at its core, utilizes a Virtual Beam Line (VBL) simulation code to predict laser energetics, wavefront, near- and far-field beam profiles, and damage risk prior to each shot. This same simulation tool is being used widely to understand such diverse phenomena as regenerative-amplifier saturation, damage inspection system performance, fratricide risk from small-scale flaws in large optics, converter performance, and conjugate image formation.

Evaluation of a job‐aiding tool in inspection systems

AbstractVisual inspection plays a very important role in ensuring quality in the manufacturing and service industries. Two determinants of inspection performance are visual search and decision‐making. Improvement in any one of the components will have an impact on system performance. Job‐aids, accompanied by training, have proven to be effective in enhancing accuracy and reducing search time in visual inspection systems. This article aims to investigate the effects of search strategy along with task complexity and pacing on inspection performance using a job‐aiding tool. To facilitate the experiments, an enhanced job‐aiding tool in a simulated visual inspection environment was developed. This tool enables an inspector to track his or her search path in visual inspection systems. A pilot study and two experiments were conducted using this tool. The pilot study examined the effectiveness of the job‐aiding tool. The first experiment studied the effect of search strategy and task complexity on inspection system performance and the second experiment studied the impact of search strategy, task complexity, and pacing on system performance. Results from this research can be used to better design an inspection system. © 2007 Wiley Periodicals, Inc.

Impact of Task Complexity on Hybrid Inspection System Performance

Recently, 100% inspection using automated systems has seen more frequent application than traditional sampling inspection using human inspectors. Nevertheless, humans still outperform machines for most attribute inspection tasks. Since neither humans nor automation can achieve superior inspection system performance, hybrid inspection systems where humans work cooperatively with machines merit study. In response to this situation, this research was conducted to evaluate the impact of task complexity on inspection performance in a hybrid environment. Results from the study showed that task complexity has a significant effect on inspection performance. Furthermore, the study looked at the impact of human intervention in such a hybrid inspection system and indicated that human inspector will continue to play a vital role in future hybrid systems.

Evaluation of best system performance: Human, automated, and hybrid inspection systems

AbstractRecently, 100% inspection with automated systems has seen more frequent application than traditional sampling inspection with human inspectors. Nevertheless, humans still outperform machines in most attribute inspection tasks. Because neither humans nor automation can achieve superior inspection system performance, hybrid inspection systems where humans work cooperatively with machines merit study. In response to this situation, this research was conducted to evaluate three of the following different inspection systems: (1) a human inspection system, (2) a computer search/human decision‐making inspection system, and (3) a human/computer share search/decision‐making inspection system. Results from this study showed that the human/computer share search/decision‐making system achieve the best system performance, suggesting that both should be used in the inspection tasks rather than either alone. Furthermore, this study looked at the interaction between human inspectors and computers, specifically the effect of system response bias on inspection quality performance. These results revealed that the risky system was the best in terms of accuracy measures. Although this study demonstrated how recent advances in computer technology have modified previously prescribed notions about function allocation alternatives in a hybrid inspection environment, the adaptability of humans was again demonstrated, indicating that they will continue to play a vital role in future hybrid systems. © 2003 Wiley Periodicals, Inc. Hum Factors Man 13: 137–152, 2003.