Artificial Neural Networks for Automated Quality Control of Textile Seams

Bahlmann C, Heidemann G, Ritter H. Artificial Neural Networks for Automated Quality Control of Textile Seams. Pattern Recognition. 1999;32(6):1049-1060.We present a method for an automated quality control of textile seams, which is aimed to establish a standardized quality measure and to lower coals in manufacturing. The system consists of a suitable image acquisition setup, an algorithm for locating the seam, a feature extraction stage and a neural network of the self-organizing map type for feature classification. A procedure to select an optimized feature set carrying the information relevant for classification is described. (C) 1999 Pattern Recognition Society. Published by Elsevier Science Ltd, All rights reserved

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

Application of Neural Network in Shop Floor Quality Control in a Make to Order Business

A make to order business has to produce the products that are customized to the customer\u27s current need. The customization can be realized by assembling different standard parts with various \u27configurations\u27. The oil field service industry is a typical example where most products produced are cylindrical assemblies made up of standard parts customized in their size, material specifications, coating specifications, and threading suited for the particular load rating and environment. As business cycles go up and down, hiring and firing of personnel is the routine of the day. Thus, it is very hard to keep experienced inspectors due to high turnover of the staff on shop floor and thus intensive endeavor to train the inspectors for the same recurrent problems of the same standard parts is required. This paper proposes a neural network model to help the industrial practitioners address such a concern. The neural network is trained with ample \u27judgment calls\u27 from the manufacturing experts so that it can properly generate the decision to \u27scrap\u27, \u27rework\u27 or \u27use as is\u27 for the inspected parts. The real quality data from an oil field service industry is used to validate the effectiveness of the proposed tool

Development of a conceptual framework with a smart database for fabric sewability

Fabric sewability is an important element in garment manufacturing and has a critical impact on the aesthetic qualities and value of a garment. Garment manufacturers who fail to recognise and apply appropriate sewing practices incur huge inefficiencies in resources which can have both social and economic impact. The focus of this research was to bridge the gap between the human and machine interaction by understanding the fabric handle and creating an automated system to minimise sewing defects and maximise production. In doing this, a smart database was developed to predict lower and upper limits for sewing machine settings based on the mechanical and physical properties of the fabrics. The research further establishes the relationship between the fabric and the performance of the material during the sewing process. A feasibility study was undertaken to generate data on machine settings using woven shirt materials. These lightweight fabrics, with plain weave construction, were chosen as they generally exhibit higher levels of seaming problems during sewing. The relationship between the fabric parameters were examined, by using objective and subjective methods of assessment, to determine the physical and mechanical properties of the material. A technical expert, with extensive years of experience on stitching materials in the apparel industry, was invited to assess the materials and to offer their opinion on the potential sewability and recommend sewing machine parameters to produce a high quality seam. Based on the outcomes from the feasibility study, the research widened to a representative cohort of fabrics and examined the relationship between the mechanical properties and physical characteristics of the fabric and how they influence seam appearance and seam quality. A team of experts with specialist knowledge referred to as the ‘Sewing Parameter Evaluation Committee’ (SPEC) were invited to handle the materials and offer their advice on the machine settings to reduce seam deformation. Kendall’s coefficient of concordance was used to determine the level of alignment between the experts’ ranking of twenty fabrics and their suitability for a defect free seam. It highlighted that there was little agreement with the ranking of fabrics between experts. The fabrics were stitched using a standard lockstitch ISU (Integrated Stitching Unit) sewing machine and all the machine settings were adjusted manually. The expert opinions were collated based on their advice to establish the best possible settings to produce a garment with minimal seam deformation. The fabric intelligent technology system (FIT) was created to store the data and generate reports on machine settings for the sewability of the material by combining the validated SPEC recommendations and the fabric mechanical and physical properties. During the final phase of the project, a second set of experts (different from SPEC), were identified to rank the quality of the seams using the American Association of Textile Colourists and Chemists (AATCC) chart for seam deformation. The crux of this work was to develop a conceptual framework for a sewing machine settings database that would benefit the apparel industry by providing a knowledge based system for the optimisation of seam performance, quality and aesthetic appeal. The outcomes from this study add new knowledge to the body of literature that highlights the significance of fabric sewability in garment manufacturing and the limitations of predictive. The study also contributes to a greater understanding of the behaviour of textile materials during the sewing of garments and the application of machine settings which improve the manufacturing process of sewn seams. The framework underscores the significance of the robust system that reduces seam deformation, increases productivity, and facilitates the overall efficiency of the garment manufacturing process. The implementation of an efficient quality management system (QMS) is vital to the global economy and to the overall well-being of the workforce and this novel framework and system should contribute to the successful implementation of any QM

The Platform for non-metallic pipes defects recognition. Design and Implementation

This paper describes a prototype software and hardware platform to provide support to field operators during the inspection of surface defects of non-metallic pipes. Inspection is carried out by video filming defects created on the same surface in real-time using a "smart" helmet device and other mobile devices. The work focuses on the detection and recognition of the defects which appears as colored iridescence of reflected light caused by the diffraction effect arising from the presence of internal stresses in the inspected material. The platform allows you to carry out preliminary analysis directly on the device in offline mode, and, if a connection to the network is established, the received data is transmitted to the server for post-processing to extract information about possible defects that were not detected at the previous stage. The paper presents a description of the stages of design, formal description, and implementation details of the platform. It also provides descriptions of the models used to recognize defects and examples of the result of the work

The FAST fabric objective measurement properties of commercial worsted apparel fabrics available in South Africa

In the last few decades, there has been a shift globally towards the objective measurement of these textile fibre, yarn and fabric properties which determine processing performance and product quality. This shift is also very apparent in the objective measurement of fabric properties, particularly those relating to handle and making-up into a garment. This study was motivated by the fact that the adoption of fabric objective measurement (FOM), specifically the FAST system, will benefit the South African worsted apparel sector, as it has done in various other countries which produce high quality worsted apparel fabrics and garments. FAST is robust and portable, yet inexpensive. The main objective of the study was to develop a FAST referencing system which can be used for benchmarking by the local apparel industry and, as a basis for encouraging and persuading the industry to adopt this system of fabric quality measurement and assurance and thereby improve their product quality and international competitiveness. To achieve the main objective, involved sourcing and FAST testing a representative cross-section of commercial worsted apparel fabrics with the emphasis on wool and wool blends from the local fabric and clothing manufacturing industry, and determining how the various FAST properties were affected by factors such as fabric weave, fibre blend and weight, since this could impact on the specific nature and validity of the referencing system. A total of some 394 worsted type commercial fabrics, mainly in wool and wool blends, were sourced from, and with the inputs of, local apparel fabric and clothing manufacturers so as to ensure the local fabric and garment representative of the sample population and after which the fabrics were tested on the FAST system. ANOVA (regression analysis) was carried out on each of the FAST parameters in order to determine whether fabric weight, weave, thickness and fibre composition (pure wool and wool blends) had a statistically significant effect on them, since this is an important aspect which needs to be clarified prior to the development of a envisaged meaningful FAST system

A Hybrid Neural Network Architecture for Texture Analysis in Digital Image Processing Applications

A new hybrid neural network model capable of texture analysis in a digital image processing environment is presented in this thesis. This model is constructed from two different types of neural network, self-organisation and back-propagation. Along with a brief resume of digital image processing concepts, an introduction to neural networks is provided. This contains appropriate documentation of the neural networks and test evidence is also presented to highlight the relative strengths and weaknesses of both neural networks. The hybrid neural network is proposed from this evidence along with methods of training and operation. This is supported by practical examples of the system's operation with digital images. Through this process two modes of operation are explored, classification and segmentation of texture content within images. Some common methods of texture analysis are also documented, with spatial grey level dependence matrices being chosen to act as a feature generator for classification by a back-propagation neural network, this provides a benchmark to assess the performance of the hybrid neural network. This takes the form of descriptive comparison, pictorial results, and mathematical analysis when using aerial survey images. Other novel approaches using the hybrid neural network are presented with concluding comments outlining the findings presented within this thesis