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With the help of basic physics, the application of computer algorithms in the form of recent advances such as machine learning and neural networking in textile Industry has been discussed in this review. Scientists have linked the underlying structural or chemical science of textile materials and discovered several strategies for completing some of the most time-consuming tasks with ease and precision. Since the 1980s, computer algorithms and machine learning have been used to aid the majority of the textile testing process. With the rise in demand for automation, deep learning, and neural networks, these two now handle the majority of testing and quality control operations in the form of image processing.

The state-of-the-art of artificial intelligence (AI) applications in the textile sector is reviewed in this paper. Based on several research problems and AI-based methods, the current literature is evaluated. The research issues are categorized into three categories based on the operation processes of the textile industry, including yarn manufacturing, fabric manufacture and coloration.

AI-assisted automation has improved not only machine efficiency but also overall industry operations. AI's fundamental concepts have been examined for real-world challenges. Several scientists conducted the majority of the case studies, and they confirmed that image analysis, backpropagation and neural networking may be specifically used as testing techniques in textile material testing. AI can be used to automate processes in various circumstances.

This research conducts a thorough analysis of artificial neural network applications in the textile sector.

This chapter offers an overview of the applications of artificial intelligence (AI) in the textile industry and in particular, the textile colouration and finishing industry. The advent of new technologies such as AI and the Internet of Things (IoT) has changed many businesses and one area AI is seeing growth in is the textile industry. It is estimated that the AI software market shall reach a new high of over US$60 billion by 2022, and the largest increase is projected to be in the area of machine learning (ML). This is the area of AI where machines process and analyse vast amount of data they collect to perform tasks and processes. In the textile manufacturing industry, AI is applied to various areas such as colour matching, colour recipe formulation, pattern recognition, garment manufacture, process optimisation, quality control and supply chain management for enhanced productivity, product quality and competitiveness, reduced environmental impact and overall improved customer experience. The importance and success of AI is set to grow as ML algorithms become more sophisticated and smarter, and computing power increases.

This paper describes the design and implementation of an activity‐based costing (ABC) system for a textile company in Taiwan.

An in‐depth field investigation by collecting and analyzing 39 months of field data, gathering information from files and archives, direct observation, interviews, and statistical analyses was conducted.

First, the company's existing cost system adopted a volume‐based cost driver to allocate overhead costs to products. While the company devised an “equivalent factor” to take production‐complexity into account, the weakness of the metric led to product cost distortions. Second, the existing volume‐based cost system ignores the impact of rework processes on product costs. Third, adding complexity‐related cost drivers to the volume‐based cost driver increases the ability to explain variations in overhead costs. Fourth, the newly designed ABC system incorporates both volume‐based and non‐volume based drivers, which considers the effect of rework on product costs. Fifth, the existing volume‐based cost system overestimates the costs of high‐volume products and underestimates the costs of products with high production‐complexity. Finally, the company still stays at the analysis phase of the ABC system implementation, possibly due to revision of strategy, no linkage to incentives, lack of MIS support, and inadequate inventory control.

The above findings have implications for companies attempting to implement ABC.

This paper extends prior research in the following. First, it reports on the entire process of ABC implementation for a given company, as well as facilitators/impediments in the process. Second, while most prior research tends to focus on success cases, our study presents a failure case, which has implications for practitioners trying to avoid the same mistakes.

This paper aims to study microwave pad dyeing process for wool fabric. Influences of various dyeing process conditions including galactomannan dosage, urea dosage, sodium bisulphite dosage, pH value, microwave irradiation power, treating time and cold batching time before microwave fixation on K/S values were analysed. The colour yield, fixation and levelness were compared between microwave fixation and cold batching fixation.

Colour yield (K/S values) was calculated using a Datacolor SF650 colour measuring and matching instrument (10° standard observer, CIE D65 light source Measuring; Datacolor, USA) and was used to determine the depth of the shade of dyed wool fabrics. Levelness of dyeing was evaluated also using the Datacolor SF650 colour measuring and matching instrument by measuring average deviation (S), range (P) of the maximum and the minimum for lightness (L), chroma (C) and hue (h), and balanced colour difference (ΔE) at 20 specified uniform locations on the wool fabrics. The colour difference was calculated as per the equation ΔE=(ΔL2+Δa2+Δb2)1/2 as appearing in the Experimental section. Fixation was determined using a Datacolor SF650 colour measuring and matching instrument by measuring ratio the of K/S for wool fabrics that were rinsed, washed, neutralised and then dried, and wool fabrics that were dried after fixation without washing. The pH of the padding solution was evaluated using a PHSJ-4A PH meter (Datacolor, USA). SEM analysis was done on JEOL JSM-5600LV machine (JEOL Ltd, Japan).

This study is based on application of microwave technology in the processing of silk.

It was found in laboratory experiments that uniform dyeing and deeper colour can be achieved throughout the microwave pad dyeing process for wool by using galactomannan. The novel process could reduce the dyeing time and the energy consumption of the traditional cold pad-batch dyeing process for wool fabric.

Environmental legislation and its enforcement has undoubtedly forced the textile industry to be rather cautious in selecting the appropriate processes and equipment. The most efficient, economic and minimal environmental pollution processing methods will be increasingly demanded throughout the 1990s. However, the majority of the textile industry consists of small and medium enterprises, where the lack of expertise on the use of best available techniques leads to levels of operation far away from the optimal. An expert system for a priori economic evaluation of potential interventions in the textile manufacturing processes and equipment is necessary to support decision making in the management level. This paper presents such an expert system, designed and implemented in four stages: formal description of the key factors that affect the dyeing process in the textile industry, development of models for the representation of relevant information, development of models for the representation of knowledge and integration of the above‐mentioned models in a unified information system that supports the decision‐making process in the management of textile enterprises.

In recent times, wool- and silk-blended fabrics are popular for creating glamourous products. Silk is blended to wool for creating more lustrous effect and to impart strength; on the other hand, wool is responsible for resilience, softness and warmth properties. Chemically both the fibres are protein-based, but the amount of amino acids is different. Due to this, the dye absorption behaviours of the two fibres from the same dye-bath are different. Wool is become darker than the silk fibre, if both the fibres are dyed together in a single bath dyeing process.

Here the wool fibres are first pre-treated with a commercial synthetic tanning agent (syntan) Mesitol HWS at three different pH values of 2.2, 3.2 and 4.2 and at three different concentrations: 5, 10 and 15 per cent. Then the syntan pre-treated wool fibres are dyed together with silk fibres maintaining the blend ratio as 80:20 by Telon Red MR, Telon Yellow M4GL and Telon Blue MRLW with sodium sulphate at three different concentrations of 10, 20 and 30 per cent.

The dye absorbency of the syntan-treated wool fibres decreased with increase in syntan concentration, whereas the colour strength of silk fibres increased. The resist effectiveness of wool fibres is increased from 6 to 59 per cent with increase of syntan concentration. So after the dyeing process, the colour strength of syntan-treated wool fibres are almost same with the colour strength of silk fibres. The washing fastness of the samples is improved, and wash fastness behaviour of both wool and silk fibres is almost same.

This paper gives an idea about the one bath dyeing process of wool- and silk-blended fabrics to achieve solid dyeing effect.

Texas instruments has a cheap starter kit for newcomers to AI computing. For a mere £135, five video tapes of the highlights of the company's satellite symposia on AI are provided. Also included are “hands‐on” PC‐compatible demonstration software, a 35 mm colour slide presentation on the theory and benefits of AI, a set of literature which contains two copies of “Understanding Artificial Intelligence” — Texas Instrument's book on AI — and a copy of an issue of Intelligence, its AI newsletter. The company will provide full details; write to: TI/AI Starter Kit, PO Box 50, Market Harborough, Leicestershire, UK.

Focuses on Korean garment manufacturing and the feasibility and benefits of introducing an expert system. Reviews previous literature on knowledge‐based system and describes in detail the stages followed in developing, constructing, testing, evaluating, integrating, implementing and maintaining the expert system. Concludes that an expert system could be used to solve various garment manufacturing problems and would contribute to garment quality improvement through a standardized apparel production process.

The purpose of this paper is to purify the wastewater in the garment industry.

The preparation of the calcium alginate (CA)/activated carbon (AC) composite membrane was achieved by vacuum freeze-drying and the cross-linking reaction between sodium alginate and CaCl₂. Effective parameters in the methylene blue (MB) adsorption such as temperature, dose, contact time and pH were discussed. The adsorption properties of the composite membrane were investigated by isotherm, kinetics and thermodynamic analysis. The adsorption equilibrium data were described by the adsorption isotherm Langmuir model and the Freundlich model. The pseudo-first-order, pseudo-second-order and intra-particle diffusion equations were selected to evaluate the kinetics. The thermodynamic study described that the adsorption reaction was spontaneous and exothermic.

The AC/CA membrane is an efficient and powerful adsorbent to remove MB in printing and dyeing wastewater, and provides a new idea for the selection of adsorption materials for industrial printing and dyeing wastewater.

The composite membrane research on CA and AC can provide new ideas for the research of these kinds of materials.

The paper contributes to its wider and convenientapplication in wastewater treatment.

Studies on the combination of CA and AC into adsorption membranes and for the removal of dyes from printing and dyeing wastewater have not been reported. A novel composite material is provided for treatment dyeing wastewater in garment production. The composite membrane research on CA and AC can provide new ideas for the research of these kinds of materials and contribute to its wider and convenient application in wastewater treatment.

This study aims to observe the coloring efficacy of graphite (G) and nano bentonite clay (BCNPs) on the adsorption of Basic Blue 5 dye from residual dye bath solution.

Some factors that affected the adsorption processes were examined and found to have significant impacts on the adsorption capacity such as the initial concentration of G and/or BCNPs (Co: 40–2,320 mg/L), adsorbent bath pH (4–9), shaking time (30–150 min.) and initial dye concentration (40–200 mg/L). The adsorption mechanism of dye by using G and/or BCNPs was studied using two different models (first-pseudo order and second-pseudo order diffusion models). The equilibrium adsorption data for the dye understudy was analyzed by using four different models (Langmuir, Freundlich, Temkin modle and Dubinin–Radushkevich) models.

It has been found that the adsorption kinetics follow rather a pseudo-first-order kinetic model with a determination coefficient (R²) of 0.99117 for G and 0.98665 for BCNPs. The results indicate that the Freundlich model provides the best correlation for G with capacities q_max = 2.33116535 mg/g and R² = 0.99588, while the Langmuir model provides the best correlation for BCNPs with R² = 0.99074. The adsorbent elaborated from BCNPs was found to be efficient and suitable for removing basic dyes rather than G from aqueous solutions due to its availability, good adsorption capability, as well as low-cost preparation.

There is no research limitation for this work. Basic Blue 5 dye graphite (G) and nano bentonite clay (BCNPs) were used.

This work has practical applications for the textile industry. It is concluded that using graphite and nano bentonite clay can be a possible alternative to adsorb residual dye from dye bath solution and can make the process greener.

Socially, it has a good impact on the ecosystem and global community because the residual dye does not contain any carcinogenic materials.

The work is original and contains value-added products for the textile industry and other confederate fields.