Modeling of textile manufacturing processes using intelligent techniques: a review

Global strategy for optimizing textile dyeing manufacturing process via GA-based grey nonlinear integer programming

In this paper, the growing significance of data analysis in manufacturing environments is exemplified through a review of relevant literature and a generic framework to aid the ease of adoption of regression-based supervised learning in manufacturing environments. To validate the practicality of the framework, several regression learning techniques are applied to an open-source multi-stage continuous-flow manufacturing process data set to typify inference-driven decision-making that informs the selection of regression learning methods for adoption in real-world manufacturing environments. The investigated regression learning techniques are evaluated in terms of their training time, prediction speed, predictive accuracy (R-squared value), and mean squared error. In terms of training time (TT), k-NN20 (k-Nearest Neighbour with 20 neighbors) ranks first with average and median values of 4.8 ms and 4.9 ms, and 4.2 ms and 4.3 ms, respectively, for the first stage and second stage of the predictive modeling of the multi-stage continuous-flow manufacturing process, respectively, over 50 independent runs. In terms of prediction speed (PS), DTR (decision tree regressor) ranks first with average and median values of 5.6784×106 observations per second (ob/s) and 4.8691×106 observations per second (ob/s), and 4.9929×106 observations per second (ob/s) and 5.8806×106 observations per second (ob/s), respectively, for the first stage and second stage of the predictive modeling of the multi-stage continuous-flow manufacturing process, respectively, over 50 independent runs. In terms of R-squared value (R2), BR (bagging regressor) ranks first with average and median values of 0.728 and 0.728, respectively, over 50 independent runs, for the first stage of the predictive modeling of the multi-stage continuous-flow manufacturing process, and RFR (random forest regressor) ranks first with average and median values of 0.746 and 0.746, respectively, over 50 independent runs, for the second stage of the predictive modeling of the multi-stage continuous-flow manufacturing process. In terms of mean squared error (MSE), BR (bagging regressor) ranks first with average and median values of 2.7 and 2.7, respectively, over 50 independent runs, for the first stage of the predictive modeling of the multi-stage continuous-flow manufacturing process, and RFR (random forest regressor) ranks first with average and median values of 3.5 and 3.5, respectively, over 50 independent runs, for the second stage of the predictive modeling of the multi-stage continuous-flow manufacturing process. All methods are further ranked inferentially using the statistics of their performance metrics to identify the best method(s) for the first and second stages of the predictive modeling of the multi-stage continuous-flow manufacturing process. A Wilcoxon rank sum test is then used to statistically verify the inference-based rankings. DTR and k-NN20 have been identified as the most suitable regression learning techniques given the multi-stage continuous-flow manufacturing process data used for experimentation.

Dyeing is the most important and prime section of the textile manufacturing process. During dyeing, different types of faults occur which may cause huge loss of production cost, energy, and time. Batch-to-Batch shade variation, Patchy dyeing effect, Dye spots, Uneven dyeing, and Lighter shades are the most common dyeing fault in our textile dyeing manufacturing process. From a thorough investigation and a significant number of research works, we identified some common causes of those dyeing faults. Among them, improper chemical mixing in the mixing tank is one of them which changes the pH condition of the Dye-Bath and causes huge dyeing faults. In most cases, chemicals or dyes are applied to the dyeing machine manually and create these problems. During every production after a while pH of the Dye-Bath is checked manually. To avoid this manual pH checking system we propose a device that detects pH, monitor, and control it automatically. The device is designed with an Analog pH meter as pH sensor and 5v DC micro submersible motor pump, with an Arduino UNO R3 connected with a 4-channel Relay module, HC-05 Bluetooth module, and other parts. The sensor senses the pH of the Dye-Bath and the pumps will help to maintain the required pH in the vessel by pumping the proper amount of chemicals or dyes in the dye bath. Dyeing faults can be reduced significantly by controlling the pH of Dye bath and our developed system offers to do it automatically.KeywordsDye-BathDyeing faultpH monitoringpH controllingIoT-based automatic controlling system

The word “textile” means to weave and was taken from the Latin word “texere.” Nowadays, textiles not only fulfill humankind's basic necessity for clothing, they also allow individuals to make fashion statements. As one of the oldest industries, the textile industry occupies a unique place in India. It is responsible for 14% of the total industrial manufacture in India. However, the textile industry is also considered to be one of the biggest threats to the environment. Pretreatment, dyeing, printing, and finishing operations are among the various stages of the industrial textile manufacturing process. These fabrication operations not only utilize huge quantities of power and water, they also generate considerable amounts of waste. The textile industry utilizes a number of dyes, chemicals, and other materials to impart the required qualities to the fabrics. These operations produce a significant amount of effluents. The quality of effluents is such that they cannot be put to other uses, and they can create environmental problems if they are disposed of without appropriate treatment. This review discusses different textile processing stages, pollution problems associated with these stages, and their eco‐friendly alternatives. Textile wet processing is described in detail, as it is the key process in the industry and it also generates the greatest amount of pollutants in textile processing. The environmental impact of textile effluents is discussed, as textile effluents not only impose negative effects on the quality of water and soil, they also imperil plant and animal health. In this paper, various methods for treating textile effluents are described. Discussion of physical, chemical, biological, and advanced treatment technologies of effluent treatment are included in this paper.

The Textile Industry is one of the largest industrial sectors and the fifth largest exporter of the textiles employing 45 million workers in India. The Indian textile industry has changed its ways in the production of finished textiles, Energy is involved in each of stage processing. Thus, this study aims to evaluate the energy efficiency of the processes in the textile industry and identify opportunities for improvement in the process involving raw fabric to the finished textile product. The energy efficiency determination in an industry can be evaluated by the energy consumption of the respective process equipment in an industry which includes the performance evaluation of the textile manufacturing processes. This paper describes the operations in textile manufacturing such as weaving, yarn production, spinning, drying, and also the significance of PAT schemes in energy improvement opportunities for various industries, including the technical improvement studies and also provides the brief description on validating various unit operations and respective parameters that affect the performance of various process equipment such as stenter, heaters, compressors, motors, and other non-production equipment. This review paper also described the impact of PAT cycle 1 in validating the energy intensity of technologies used in textile industries and some important measures required to improve the energy efficiency of a process as this could improve the functioning of the system. The best available techniques in the process has also been discussed in the sections which can be implemented in practice for improving the energy efficiency of the processes.

Defects in the textile manufacturing process lead to a great waste of resources and further affect the quality of textile products. Automated quality guarantee of textile fabric materials is one of the most important and demanding computer vision tasks in textile smart manufacturing. This survey presents a thorough overview of algorithms for fabric defect detection. First, this review briefly introduces the importance and inevitability of fabric defect detection towards the era of manufacturing of artificial intelligence. Second, defect detection methods are categorized into traditional algorithms and learning-based algorithms, and traditional algorithms are further categorized into statistical, structural, spectral, and model-based algorithms. The learning-based algorithms are further divided into conventional machine learning algorithms and deep learning algorithms which are very popular recently. A systematic literature review on these methods is present. Thirdly, the deployments of fabric defect detection algorithms are discussed in this study. This paper provides a reference for researchers and engineers on fabric defect detection in textile manufacturing.

: Manufacturing process models are developed for a data-driven, reconfiguarable manufacturing facility. A human assembly process model is developed to characterize assembly processes. Machine and tool resources are characterized for manufacturing processes to provide a broad characterization of manufacturing process coverage in a hypothetical final assembly factory for infantry fighting vehicles. A sample population of manufacturing process parameters and resources is collected and incorporated into a manufacturing capability and process model library. Requirements for a virtual manufacturing environment are developed, and an initial, prototype version is implemented to facilitate process model validation, configuration, and experimentation.

This paper describes an on going research by the authors who are seeking methods to integrate an earlier developed manufacturing process modeling method in the virtual manufacturing (VM) technology. The primary aim is to attach special manufacturability evaluation to modeling of mechanical products. A more or less generic process model is generated for a cluster of manufacturing tasks then manufacturing process entities are created by evaluation of the manufacturing process model using part, form feature, production environment, production planning and cost information. Petri net manufacturing process model representation is used. It has proved to be a versatile virtual manufacturing tool in manufacturing process planning, evaluation of manufacturability and in manufacturing environment design. A process model evaluation procedure is emphasized in the course of which feasibility and resource requirements of the manufacturing are revealed. Firstly, techniques of evaluation of manufacturability, the applied approach to integration of shape and manufacturing process modeling and the related virtual manufacturing concepts are introduced. The process model is outlined and earlier relevant publications of the authors are cited. Then problems and solutions at evaluating of process model in close connection with design and environmental changes are detailed. Following this, integration of the method into a comprehensive virtual manufacturing concept is explained. Finally, main issues and future research plans are concluded.

Manufacturing Advantage: War, the State, and the Origins of American Industry, 1776–1848

The use of nanomaterials- and nanotechnology-based processes is growing at a tremendous rate in all fields of science and technology. Textile industry is also experiencing the benefits of nanotechnology in its diverse field of applications. Textile-based nanoproducts starting from nanocomposite fibers, nanofibers to intelligent high-performance polymeric nanocoatings are getting their way not only in high performance advanced applications but nanoparticles are also successfully being used in conventional textiles to impart new functionality and improved performance. Greater repeatability, reliability and robustness are the main advantages of nanotechnological advancements in textiles. Nanoparticle application during conventional textile processing techniques, such as finishing, coating and dyeing, enhances the product performance manifold and imparts hitherto unachieved functionality. New coating techniques like sol-gel, layer-by-layer, plasma polymerization etc. can develop multi-functionality, intelligence, excellent durability and weather resistance to fabrics. The present paper focuses on the development and potential applications of nanotechnology in developing multifunctional and smart nanocomposite fibers, nanofibers and other new finished and nanocoated textiles. The four main areas of textile chemical processing, namely nanofinishing, nanocoating, nanocomposite coating and nanodyeing, are covered in the first section of this paper and the second section deals with developments in nanocomposite fibers and nanofibers. The influence of nanomaterials in textile finishing and processing to enhance product performance is discussed. Nanocoating is a relatively new technique in the textile field and is currently under research and development. Polymeric nanocomposite coatings, where nanoparticles are dispersed in polymeric media and used for coating applications, are the most promising route to develop multifunctional and intelligent high-performance textiles. Not much research has been done on applying the concept of nanotechnology in dyeing of textiles except a few reports on dye particle size reduction, structural change in fibers or the surface etching of textiles to create nanostructured surfaces. The reduction in water consumption during nanotechnology applications in textile processing has the potential to control the effluent problems of a textile process house. The most researched area to produce multifunctional, smart fibers is the preparation of nanocomposite fibers where the exceptional properties of nanoparticles have been utilized to enhance and impart several functionalities on conventional textile grade fibers. Nanofibers are gaining popularity in some specialized technical applications such as filter fabric, antibacterial patches and chemical protective suits. Nanotechnological advances in these two areas of nanocomposite fibers and nanofibrous forms have also been reviewed.

We describe statistical methods for sensitivity and performance analysis of complex computer simulation experiments. Graphical methods, such as trellis plots, are suggested for exploratory analysis of individual or aggregate performance metrics, conditional on different experiment inputs. More formal statistical methods, such as analysis of variance based methods and regression tree analysis, are used to determine variables having substantive influence on the experimental results and to investigate the structure of the underlying relationship between inputs and outputs. The methods are discussed in relation to a supply chain model of the textile manufacturing process having many possible input and output variables of interest and for a computer model used to describe the flow of material in an ecosystem.

1 - Introduction: textile manufacturing process

The production, use, and disposal of synthetic textiles potentially release a significant amount of microfibers into the environment. Studies performed on municipal wastewater treatment plants (WWTPs) effluent reported a higher presence of microfibers due to the mix of domestic laundry effluent through sewage. As municipal WWTPs receive influents from households and industries, it serves as a sink for the microfibers. However, research on textile industry WWTPs that primarily treat the textile fabric processing wastewater was not explored with the concern of microfibers. Hence, the review aims to analyze the existing literature and enlighten the impact of WWTPs on microplastic emission into the environment by specifically addressing textile industry WWTPs. The results of the review confirmed that even after 95-99% removal, municipal WWTPs can emit around 160 million microplastics per day into the environment. Microfiber was the dominant shape identified by the review. The average microfiber contamination in the WWTP sludge was estimated as 200 microfibers per gram of sludge. As far as the industry-specific effluents are analyzed, textile wet processing industries effluents contained > 1000 times higher microfibers than municipal WWTP. Despite few existing studies on textile industry effluent, the review demonstrates that, so far, no studies were performed on the sludge obtained from WWTPs that handle textile industry effluents alone. Review results pointed out that more attention should be needed to the textile wastewater research which is addressing the textile wet processing industry WWTPs. Moreover, the sludge released from these WWTPs should be consideredas an important source of microfiber as they contain more quantity of microfibers than the effluent, and also, their routes to the environment are huge and easy.

It is well known that textiles and textile products may contain hazardous compounds. Formally, all imported textiles and textile products must be registered through the Indonesian Custom. Ideally, the Indonesian Custom has the capability to detect chemicals of concern in textiles or textile products entering Indonesian territory. However, this is not the case, particularly for chemicals listed in the Stockholm Convention. The difficulties arise from the lack of identification regarding substances listed in the Stockholm Convention that might be present in textiles, textile products, and finished products. The Indonesian Government has initiated programs to assess the presence of persistent organic pollutants (POPs) in Indonesian territory. Results of the assessment were elaborated in the National Implementation Plan Document on POPs, which was updated recently. Not all substances listed in the Stockholm Convention can be described in depth. Some POPs such as short-chain chlorinated paraffins (SCCPs) and polychlorinated naphthalenes have not been included in Indonesian regulation, particularly in Indonesian Customs Tariff Book, making it extremely difficult to assess them. Nevertheless, a preliminary assessment of polybrominated diphenyl ethers and SCCPs has been carried out. Using the Tier 1 approach, it was revealed that 2,194 tonnes of SCCPs was imported from India to Indonesia, which was listed under HS code 38249090 that covered CP52 (containing 50–54% chlorinated paraffins). Furthermore, another prominent issue for chemicals of concern (CoC) in textiles was the use of lead-containing dyes, nonylphenols, and nonylphenol ethoxylates. The latter two compounds are known to be used in detergents and surfactants during textile manufacturing processes. At present, nonylphenols and nonylphenol ethoxylates are not listed in Indonesian laws for regulated chemicals in textiles and textile products, as well as in the wastewater quality standard for the textile industry. Therefore, to avoid circular economy obstacles of used textiles and textile products and support sustainable Indonesian textiles, a systematic inventory of CoC in textiles is very important.

Time delay systems applications in textile industry - Modelling of sliver drafting process