Generalized Completed Local Binary Patterns for Time-Efficient Steel Surface Defect Classification

© 2018 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted ncomponent of this work in other works.Efficient defect classification is one of the most important preconditions to achieve online quality inspection for hot-rolled strip steels. It is extremely challenging owing to various defect appearances, large intraclass variation, ambiguous interclass distance, and unstable gray values. In this paper, a generalized completed local binary patterns (GCLBP) framework is proposed. Two variants of improved completed local binary patterns (ICLBP) and improved completed noise-invariant local-structure patterns (ICNLP) under the GCLBP framework are developed for steel surface defect classification. Different from conventional local binary patterns variants, descriptive information hidden in nonuniform patterns is innovatively excavated for the better defect representation. This paper focuses on the following aspects. First, a lightweight searching algorithm is established for exploiting the dominant nonuniform patterns (DNUPs). Second, a hybrid pattern code mapping mechanism is proposed to encode all the uniform patterns and DNUPs. Third, feature extraction is carried out under the GCLBP framework. Finally, histogram matching is efficiently accomplished by simple nearest-neighbor classifier. The classification accuracy and time efficiency are verified on a widely recognized texture database (Outex) and a real-world steel surface defect database [Northeastern University (NEU)]. The experimental results promise that the proposed method can be widely applied in online automatic optical inspection instruments for hot-rolled strip steel.Peer reviewe

SISTEM PENGENDALIAN MUTU PADA INDUSTI PAKAIAN DENGAN METODE STATISTICAL PROCESS CONTROL

Di era globalisasi saat ini, usaha kecil dan menengah harus dapat berkompetisi untuk menghasilkan sebuah produk yang dapat diterima dalam upaya meningkatkan mutu bisnis. Oleh karena itu, jumlah kerusakan pada proses produksi harus dikurangi, terlebih dalam industri pakaian. Pada proses desain pakaian terdapat beberapa fase yang terjadi seperti pemilihan kain, pemotongan, penjahitan, hingga pengepakan dan inspeksi. Penelitian ini bertujuan untuk membangun sebuah sistem pengendalian mutu pada usaha kecil menengah industri pakaian di daerah Kudus, Indonesia. Cara pertama yang digunakan untuk melakukan pengendalian mutu ialah dengan menggunakan aplikasi mobile yang digunakan oleh auditor untuk melakukan pengecekan proses produksi dan mengirim data defect ke database. Sedangkan cara kedua dengan menggunakan website untuk mengelola data produksi yang dilakukan oleh administrator. Perhitungan p-chart bulan Januari sampai Maret menunjukkan proses produksi cukup terkendali, karena proporsi yang ditemukan masih berada dalam rentang UCL dan LCL. Namun, untuk kemampuan proses dikatakan belum mampu untuk memenuhi kebutuhan produksi, karena salah satu dari nilai Cp 1,84 dan Cpk 0,85 tidak melebihi standar yaitu kedua nilai tersebut harus lebih atau sama dengan satu. Sedangkan level six sigma dengan nilai 4.987 berada pada yield (persentase barang yang dapat diterima) yang berkisar antara 99,38% sampai dengan 99,977%. Pengujian sistem menggunakan dua pendekatan, yaitu black box dan white box. Dari kedua pengujian tersebut menunjukkan bahwa sistem yang dibangun dapat diterima dan beroperasi dengan benar. Kata kunci: Aplikasi Mobile, Pengendalian Proses Statistika, Sistem Pengendalian Mutu, Usaha Kecil dan Menengah, Website In the era of globalization, Small and Medium Enterprises should process the competence to produce an acceptable product to stimulate enhance of business. Therefore, defective rates product should reducing, more over in the process of apparel production. The process design of apparel consist of various phase with the base fabric component, cutting, tailoring until finishing and inspecting. The research aims to develop a quality control system on Small and Medium Enterprises in apparel production as the case study in Kudus, Indonesia. The first way that used to perform quality control is by using a mobile application that is used by auditors to do the checking of the production process and transmit data defects to the database. While the second way by using websites to manage production data is done by an administrator. Calculation of the p-chart in January through March showed the production process was quite restrained, because the proportions were found to still be in range of UCL and LCL. However, the process ability not able to meet production needs, because one of the values of Cp (1,84) and Cpk (0,85) does not exceed the standard value that both of Cp and Cpk should be more or equal to one. While, the value level of six sigma at 4,987, so yield (the percentage of goods that can be accepted) that ranged between 99.38% up to 99.977%. Testing system using two approaches, namely the black box and white box. From both of these tests showed that the system was built to be accepted and operating properly. Keywords: Mobile Application, Quality Control Systems, Small and Medium Enterprises, Statistical Process Control, Websit

On flexibly integrating machine vision inspection systems in PCB manufacture

The objective of this research is to advance computer vision techniques and their applications in the electronics manufacturing industry. The research has been carried out with specific reference to the design of automatic optical inspection (AOI) systems and their role in the manufacture of printed circuit boards (PCBs). To achieve this objective, application areas of AOI systems in PCB manufacture have been examined. As a result, a requirement for enhanced performance characteristics has been identified and novel approaches and image processing algorithms have been evolved which can be used within next generation of AOI systems. The approaches are based on gaining an understanding of ways in which manufacturing information can be used to support AOI operations. Through providing information support, an AOI system has access to product models and associated information which can be used to enhance the execution of visual inspection tasks. Manufacturing systems integration, or more accurately controlled access to electronic information, is the key to the approaches. Also in the thesis methods are proposed to achieve the flexible integration of AOI systems (and computer vision systems in general) within their host PCB manufacturing environment. Furthermore, potential applications of information supported AOI systems at various stages of PCB manufacturing have been studied. It is envisaged that more efficient and cost-effective applications of AOI can be attained through adopting the flexible integration methods proposed, since AOI-generated information can now be accessed and utilized by other processes

Computer Vision Inspection And Classification On Printed Circuit Boards For Flux Defects

The manual inspection of Printed Circuit Boards (PCB) is labor intensive and slow down the production line. During the assembly process, the defective PCBs with flux defects if not detected and remove, it can create corrosion and cause harmful effects on the board itself. As such, an automated inspection system is very much needed to overcome the aforementioned problems in PCB production line. The main objective of this work is to develop a real-time machine vision system for quality assessment of PCBs by detecting defectives PCBs. The proposed system should be able to detect flux defect on PCB board during the re-flow process and achieve good accuracy of the PCB quality checking. The proposed system is named as An Automatic Inspection System for Printed Circuit Boards (AIS-PCB), involves design and fabrication of a total automation control system involving the use of mechanical PCB loader/un-loader, robotic pneumatic system handler with vacuum cap and a vision inspection station that makes a decision either to accept or reject. The decision making part involves classifier training of PCB images. Prior to ANN training, the images need to be processed by the image processing and feature extraction. The image processing system is based on pattern matching and color image analysis techniques. The shape of the PCB pins is analyzed by using pattern matching technique to detect the PCB flux defect area. After that, the color analysis of the flux defect on a PCB boards are processed based on their red color pixel percentage in Red, Green and Blue (RGB) model. The red color filter band mean value of histogram is measured and compared to the value threshold to determine the occurrence of flux defect on the PCBs. The texture of the PCB flux defect can also be extracted based on line detection of the gradient field PCB images and feature indexing by using Radon transform-based approach. The feed-forward back-propagation (FFBP) model is used as classifier to classify the product quality of the PCBs via a learning concept. A number of trainings using the FFBP are performed for the classifier to learn and match the targets. The learned classifier, when tested on the PCBs from a factory’s production line, achieves a grading accuracy of coefficient of efficiency (COE) greater than 95%. As such, it can be concluded that the developed AIS-PCB system has shown promising results by successfully classifying flux defects in PCBs through visual information and facilitates automatic inspection, thereby aiding humans in conducting rapid inspections

A Hierarchical, Fuzzy Inference Approach to Data Filtration and Feature Prioritization in the Connected Manufacturing Enterprise

The current big data landscape is one such that the technology and capability to capture and storage of data has preceded and outpaced the corresponding capability to analyze and interpret it. This has led naturally to the development of elegant and powerful algorithms for data mining, machine learning, and artificial intelligence to harness the potential of the big data environment. A competing reality, however, is that limitations exist in how and to what extent human beings can process complex information. The convergence of these realities is a tension between the technical sophistication or elegance of a solution and its transparency or interpretability by the human data scientist or decision maker. This dissertation, contextualized in the connected manufacturing enterprise, presents an original Fuzzy Approach to Feature Reduction and Prioritization (FAFRAP) approach that is designed to assist the data scientist in filtering and prioritizing data for inclusion in supervised machine learning models. A set of sequential filters reduces the initial set of independent variables, and a fuzzy inference system outputs a crisp numeric value associated with each feature to rank order and prioritize for inclusion in model training. Additionally, the fuzzy inference system outputs a descriptive label to assist in the interpretation of the feature’s usefulness with respect to the problem of interest. Model testing is performed using three publicly available datasets from an online machine learning data repository and later applied to a case study in electronic assembly manufacture. Consistency of model results is experimentally verified using Fisher’s Exact Test, and results of filtered models are compared to results obtained by the unfiltered sets of features using a proposed novel metric of performance-size ratio (PSR)