Technical Library | 2021-11-22 20:39:44.0
Quality control is a key activity performed by manufacturing companies to verify product conformance to the requirements and specifications. Standardized quality control ensures that all the products are evaluated under the same criteria. The decreased cost of sensors and connectivity enabled an increasing digitalization of manufacturing and provided greater data availability. Such data availability has spurred the development of artificial intelligence models, which allow higher degrees of automation and reduced bias when inspecting the products. Furthermore, the increased speed of inspection reduces overall costs and time required for defect inspection. In this research, we compare five streaming machine learning algorithms applied to visual defect inspection with real world data provided by Philips Consumer Lifestyle BV. Furthermore, we compare them in a streaming active learning context, which reduces the data labeling effort in a real-world context. Our results show that active learning reduces the data labeling effort by almost 15% on average for the worst case, while keeping an acceptable classification performance. The use of machine learning models for automated visual inspection are expected to speed up the quality inspection up to 40%.
Technical Library | 2021-03-18 20:03:27.0
Much has been said and written about the accuracy of visual attribute inspections of potentially counterfeit components. The techniques and procedures being used to inspect counterfeit and reworked electronic components in the open marketplace can be quite effective in most cases.
Technical Library | 2013-07-02 16:44:31.0
AOI and AXI systems can address multiple tasks in various locations of the manufacturing process and have become the leading technologies in the quest to identify defects and improve process yields.
Technical Library | 2023-11-20 18:10:20.0
The electronics production is prone to a multitude of possible failures along the production process. Therefore, the manufacturing process of surface-mounted electronics devices (SMD) includes visual quality inspection processes for defect detection. The detection of certain error patterns like solder voids and head in pillow defects require radioscopic inspection. These high-end inspection machines, like the X-ray inspection, rely on static checking routines, programmed manually by the expert user of the machine, to verify the quality. The utilization of the implicit knowledge of domain expert(s), based on soldering guidelines, allows the evaluation of the quality. The distinctive dependence on the individual qualification significantly influences false call rates of the inbuilt computer vision routines. In this contribution, we present a novel framework for the automatic solder joint classification based on Convolutional Neural Networks (CNN), flexibly reclassifying insufficient X-ray inspection results. We utilize existing deep learning network architectures for a region of interest detection on 2D grayscale images. The comparison with product-related meta-data ensures the presence of relevant areas and results in a subsequent classification based on a CNN. Subsequent data augmentation ensures sufficient input features. The results indicate a significant reduction of the false call rate compared to commercial X-ray machines, combined with reduced product-related optimization iterations.
Technical Library | 2021-05-06 13:45:49.0
The high-sensitive micro eddy-current testing (ECT) probe composed of planar meander coil as an exciter and spin-valve giant magneto-resistance (SV-GMR) sensor as a magnetic sensor for bare printed circuit board (PCB) inspection is proposed in this paper. The high-sensitive micro ECT probe detects the magnetic field distribution on the bare PCB and the image processing technique analyzes output signal achieved from the ECT probe to exhibit and to identify the defects occurred on the PCB conductor. The inspection results of the bare PCB model show that the proposed ECT probe with the image processing technique can be applied to bare PCB inspection. Furthermore, the signal variations are investigated to prove the possibility of applying the proposed ECT probe to inspect the high-density PCB that PCB conductor width and gap are less than 100 μm.
Technical Library | 2015-06-30 22:02:41.0
This paper describes the losses from defects at the placement process in the SMT line. Two case studies of European and Taiwanese SMT manufacturers illustrate the actual losses from their defects. An evaluation method to select a pre-reflow AOI system maximizing the return on investment (ROI) is introduced. In the end, ROIs of three commercial pre-reflow AOI systems are compared to demonstrate the importance of selecting an appropriate AOI system. This paper will increase the probability that anyone installing an AOI system during the pre-reflow process will obtain a successful gain with short payback period.
Technical Library | 2017-08-28 17:14:41.0
PCB suppliers in the automotive space are vastly accelerating their time to market by using automated optical inspection (AOI) systems during PCB assembly. However, this next-generation technique is not limited in scope to the automotive industry – it has powerful implications for the entire PCB industry.
Technical Library | 2010-03-25 06:26:37.0
The complexity of Printed Circuit Assembly process is increasing day by day and causing productivity issues in the industry, introducing ultra fine pitch components (pitch less than 15mil) in PCA is a challenge to minimize risk of defects as solder short, dry solder. This paper is focusing on minimizing these defects.
Technical Library | 2023-11-20 17:36:58.0
With PCB complexity and density increasing and also wider use of 3D devices, tougher requirements are now imposed on device inspection both during original manufacture and at their subsequent processing onto printed circuit boards. More complicated and dense packages have more opportunities to exhibit defects both internal to the package as well as to the PCB. As components increase in complexity their cost increases, making counterfeiting them a potentially lucrative business for unscrupulous individuals and organizations.
Technical Library | 2014-02-20 15:59:17.0
In this chapter, we discuss the type of parts used to create counterfeits and the defects/degradations inherent in these parts due to the nature of the sources they come from, proposed inspection standards, and limitations of these standards. The processes used to modify the packaging of these parts to create counterfeits are then discussed along with the traces left behind from each of the processes. We then present a systematic methodology for detecting signs of possible part modifications to determine the risk of a part or part lot being counterfeit.
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