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1 – 10 of 23Metin Sabuncu and Hakan Özdemir
This study aims to identify leather type and authenticity through optical coherence tomography.
Abstract
Purpose
This study aims to identify leather type and authenticity through optical coherence tomography.
Design/methodology/approach
Optical coherence tomography images taken from genuine and faux leather samples were used to create an image dataset, and automated machine learning algorithms were also used to distinguish leather types.
Findings
The optical coherence tomography scan results in a different image based on leather type. This information was used to determine the leather type correctly by optical coherence tomography and automatic machine learning algorithms. Please note that this system also recognized whether the leather was genuine or synthetic. Hence, this demonstrates that optical coherence tomography and automatic machine learning can be used to distinguish leather type and determine whether it is genuine.
Originality/value
For the first time to the best of the authors' knowledge, spectral-domain optical coherence tomography and automated machine learning algorithms were applied to identify leather authenticity in a noncontact and non-invasive manner. Since this model runs online, it can readily be employed in automated quality monitoring systems in the leather industry. With recent technological progress, optical coherence tomography combined with automated machine learning algorithms will be used more frequently in automatic authentication and identification systems.
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Hao Wang, Hamzeh Al Shraida and Yu Jin
Limited geometric accuracy is one of the major challenges that hinder the wider application of additive manufacturing (AM). This paper aims to predict in-plane shape deviation for…
Abstract
Purpose
Limited geometric accuracy is one of the major challenges that hinder the wider application of additive manufacturing (AM). This paper aims to predict in-plane shape deviation for online inspection and compensation to prevent error accumulation and improve shape fidelity in AM.
Design/methodology/approach
A sequence-to-sequence model with an attention mechanism (Seq2Seq+Attention) is proposed and implemented to predict subsequent layers or the occluded toolpath deviations after the multiresolution alignment. A shape compensation plan can be performed for the large deviation predicted.
Findings
The proposed Seq2Seq+Attention model is able to provide consistent prediction accuracy. The compensation plan proposed based on the predicted deviation can significantly improve the printing fidelity for those layers detected with large deviations.
Practical implications
Based on the experiments conducted on the knee joint samples, the proposed method outperforms the other three machine learning methods for both subsequent layer and occluded toolpath deviation prediction.
Originality/value
This work fills a research gap for predicting in-plane deviation not only for subsequent layers but also for occluded paths due to the missing scanning measurements. It is also combined with the multiresolution alignment and change point detection to determine the necessity of a compensation plan with updated G-code.
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H.P.M.N.L.B. Moragane, B.A.K.S. Perera, Asha Dulanjalie Palihakkara and Biyanka Ekanayake
Construction progress monitoring (CPM) is considered a difficult and tedious task in construction projects, which focuses on identifying discrepancies between the as-built product…
Abstract
Purpose
Construction progress monitoring (CPM) is considered a difficult and tedious task in construction projects, which focuses on identifying discrepancies between the as-built product and the as-planned design. Computer vision (CV) technology is applied to automate the CPM process. However, the synergy between the CV and CPM in literature and industry practice is lacking. This study aims to fulfil this research gap.
Design/methodology/approach
A Delphi qualitative approach was used in this study by conducting two interview rounds. The collected data was analysed using manual content analysis.
Findings
This study identified seven stages of CPM; data acquisition, information retrieval, verification, progress estimation and comparison, visualisation of the results and schedule updating. Factors such as higher accuracy in data, less labourious process, efficiency and near real-time access are some of the significant enablers in instigating CV for CPM. Major challenges identified were occlusions and lighting issues in the site images and lack of support from the management. The challenges can be easily overcome by implementing suitable strategies such as familiarisation of the workforce with CV technology and application of CV research for the construction industry to grow with the technology in line with other industries.
Originality/value
This study addresses the gap pertaining to the synergy between the CV in CPM literature and the industry practice. This research contributes by enabling the construction personnel to identify the shortcomings and the opportunities to apply automated technologies concerning each stage in the progress monitoring process.
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Hazard warning schemes provide efficient hazard recognition and promote project safety. Nevertheless, these schemes perform poorly because the warning information is calibrated…
Abstract
Purpose
Hazard warning schemes provide efficient hazard recognition and promote project safety. Nevertheless, these schemes perform poorly because the warning information is calibrated for individual characters and is not prioritized for the entire system. This study proposes a hazard warning scheme that prioritizes hazard characters from the inspection process based on the inspectors' experience.
Design/methodology/approach
First, hazard descriptions were decomposed into their characters, forming a double-layer network. Second, warning schemes based on cascading effects were proposed. Third, character-based warning schemes were simulated for various experiences.
Findings
The results show that when a specific hazard is detected, the degree centrality is the most effective parameter for prioritization, and hazard characters should be prioritized based on betweenness centrality for experienced inspectors, whereas degree centrality is preferred for novice inspectors.
Originality/value
The warning scheme theoretically supplements the information-processing theory in construction hazard warnings and provides a practical warning scheme with priority for the development of automated hazard navigation systems.
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Monica Puri Sikka, Alok Sarkar and Samridhi Garg
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…
Abstract
Purpose
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.
Design/methodology/approach
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.
Findings
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.
Originality/value
This research conducts a thorough analysis of artificial neural network applications in the textile sector.
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Walaa AlKhader, Raja Jayaraman, Khaled Salah, Andrei Sleptchenko, Jiju Antony and Mohammed Omar
Quality 4.0 (Q4.0) leverages new emerging technologies to achieve operational excellence and enhance performance. Implementing Q4.0 in digital manufacturing can bring about…
Abstract
Purpose
Quality 4.0 (Q4.0) leverages new emerging technologies to achieve operational excellence and enhance performance. Implementing Q4.0 in digital manufacturing can bring about reliable, flexible and decentralized manufacturing. Emerging technologies such as Non-Fungible Tokens (NFTs), Blockchain and Interplanetary File Storage (IPFS) can all be utilized to realize Q4.0 in digital manufacturing. NFTs, for instance, can provide traceability and property ownership management and protection. Blockchain provides secure and verifiable transactions in a manner that is trusted, immutable and tamper-proof. This research paper aims to explore the concept of Q4.0 within digital manufacturing systems and provide a novel solution based on Blockchain and NFTs for implementing Q4.0 in digital manufacturing.
Design/methodology/approach
This study reviews the relevant literature and presents a detailed system architecture, along with a sequence diagram that demonstrates the interactions between the various participants. To implement a prototype of the authors' system, the authors next develop multiple Ethereum smart contracts and test the algorithms designed. Then, the efficacy of the proposed system is validated through an evaluation of its cost-effectiveness and security parameters. Finally, this research provides other potential applications and scenarios across diverse industries.
Findings
The proposed solution's smart contracts governing the transactions among the participants were implemented successfully. Furthermore, the authors' analysis indicates that the authors' solution is cost-effective and resilient against commonly known security attacks.
Research limitations/implications
This study represents a pioneering endeavor in the exploration of the potential applications of NFTs and blockchain in the attainment of a comprehensive quality framework (Q4.0) in digital manufacturing. Presently, the body of research on quality control or assurance in digital manufacturing is limited in scope, primarily focusing on the products and production processes themselves. However, this study examines the other vital elements, including management, leadership and intra- and inter-organizational relationships, which are essential for manufacturers to achieve superior performance and optimal manufacturing outcomes.
Practical implications
To facilitate the achievement of Q4.0 and empower manufacturers to attain outstanding quality and gain significant competitive advantages, the authors propose the integration of Blockchain and NFTs into the digital manufacturing framework, with all related processes aligned with an organization's strategic and leadership objectives.
Originality/value
This study represents a pioneering endeavor in the exploration of the potential applications of NFTs and blockchain in the attainment of a comprehensive quality framework (Quality 4.0) in digital manufacturing. Presently, the body of research on quality control or assurance in digital manufacturing is limited in scope, primarily focusing on the products and production processes themselves. However, this study examines the other vital elements, including management, leadership and intra- and inter-organizational relationships, which are essential for manufacturers to achieve superior performance and optimal manufacturing outcomes.
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Tahmineh Raoofi and Sahin Yasar
This study aims to elaborate on the existing link between maintenance practices and the digital world while also highlighting any unaddressed potential for digital transformation…
Abstract
Purpose
This study aims to elaborate on the existing link between maintenance practices and the digital world while also highlighting any unaddressed potential for digital transformation in aircraft maintenance. Additionally, explore how digital technologies contribute to optimizing efficiency within the continuing airworthiness management (CAM) processes.
Design/methodology/approach
A literature review was performed to provide a precise review of the authority regulations on CAM processes and existing literature on digital transformation, including artificial intelligence, machine learning, neural network and big data in civil aircraft maintenance and continuing airworthiness processes. This method is used to organize, analyze and structure the body of literature to identify research gaps in the selected scope of the study.
Findings
The high position of digital technologies in preventive and predictive maintenance and the need for legislative development for using them in CAM are emphasized. Moreover, it is shown in which area of CAM scientific research has been performed regarding the application of frontier digital technologies. In addition, the gaps between maintenance practices and the digital world, along with the potential scopes of digital transformation which has not been well addressed, are identified. And finally, how digital technologies can effectively increase efficiency in CAM processes is discussed.
Originality/value
To the best of our knowledge, no study comprehensively determined the body of existing knowledge on the aspects of digitalization related to the field of continuing airworthiness management and aircraft maintenance. The results of this study provide a positive contribution to airlines, policymakers, manufacturers and maintenance organizations achieving additional benefits from the implementation of digital technologies in the CAM processes.
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Hongwei Zhang, Shihao Wang, Hongmin Mi, Shuai Lu, Le Yao and Zhiqiang Ge
The defect detection problem of color-patterned fabric is still a huge challenge due to the lack of manual defect labeling samples. Recently, many fabric defect detection…
Abstract
Purpose
The defect detection problem of color-patterned fabric is still a huge challenge due to the lack of manual defect labeling samples. Recently, many fabric defect detection algorithms based on feature engineering and deep learning have been proposed, but these methods have overdetection or miss-detection problems because they cannot adapt to the complex patterns of color-patterned fabrics. The purpose of this paper is to propose a defect detection framework based on unsupervised adversarial learning for image reconstruction to solve the above problems.
Design/methodology/approach
The proposed framework consists of three parts: a generator, a discriminator and an image postprocessing module. The generator is able to extract the features of the image and then reconstruct the image. The discriminator can supervise the generator to repair defects in the samples to improve the quality of image reconstruction. The multidifference image postprocessing module is used to obtain the final detection results of color-patterned fabric defects.
Findings
The proposed framework is compared with state-of-the-art methods on the public dataset YDFID-1(Yarn-Dyed Fabric Image Dataset-version1). The proposed framework is also validated on several classes in the MvTec AD dataset. The experimental results of various patterns/classes on YDFID-1 and MvTecAD demonstrate the effectiveness and superiority of this method in fabric defect detection.
Originality/value
It provides an automatic defect detection solution that is convenient for engineering applications for the inspection process of the color-patterned fabric manufacturing industry. A public dataset is provided for academia.
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Matthew Ikuabe, Clinton Aigbavboa and Ernest Kissi
In most developing countries, the delivery of construction project is still characterised by inefficiencies resulting from the use of outdated methods and techniques, which…
Abstract
Purpose
In most developing countries, the delivery of construction project is still characterised by inefficiencies resulting from the use of outdated methods and techniques, which retards project performance. Hence, the call for the implementation of innovative technologies such as humanoids in the execution of construction projects as it has been proven to be very effective in other sectors while improving productivity and quality of work. Consequently, this study looks at how humanoids can be used in the construction industry and what benefits they can bring.
Design/methodology/approach
The study employed a quantitative approach underpinned in post-positivist philosophical view using questionnaire as the instrument for data collection. The target respondents were construction professionals, and purposive sampling was used, while a response rate of 62.5% was gotten. The methods of data analysis were mean item score, standard deviation and one-sample t-test.
Findings
The findings revealed that humanoids can be used in progress tracking, auto-documentation and inspection and surveillance of tasks in construction activities. Also, the most important benefits of using humanoids in construction work were found to be shorter delivery times, fewer injuries and more accurate work.
Practical implications
The outcome of the study gives professionals and relevant stakeholders in construction and other interested parties' information about the areas where humanoids can be used and their benefits in construction.
Originality/value
The novelty of this study is that it is a pioneering study in South Africa on humanoids' usage in the construction industry. Also, it expands the existing borderline of the conservation of construction digitalisation for enhanced project execution.
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Lúcia Sortica de Bittencourt, Istefani Carísio de Paula, André Teixeira Pontes and Aline Cafruni Gularte
This study aims to enhance storage and distribution operations at a pharmaceutical supply center (PSC) in primary health care (PH) using lean health care (LH) tools. Supply…
Abstract
Purpose
This study aims to enhance storage and distribution operations at a pharmaceutical supply center (PSC) in primary health care (PH) using lean health care (LH) tools. Supply centers for health products, medications and supplies have unique characteristics compared to centers for other goods due to complex processes, specific services, diverse stakeholders and multiple interactions. The authors adapt LH tools to address these complexities and meet industry-specific needs.
Design/methodology/approach
The investigation unit is a PSC in a large southern Brazilian city, and the processes analyzed are the storage and distribution of medications. The authors performed action research from June 2019 to February 2020. Data collection and problem diagnosis involved the development of a value stream mapping.
Findings
The authors adapted the overall equipment effectiveness calculation, efficiency analysis, and loss classification for PSC operations. Eighteen core issues were found: waiting, movement, transport, stock, inadequate processing, defects and human potential losses. The authors proposed waste reduction tools and practices. Inadequate storage conditions may compromise medicine quality, efficacy and safety. This can result from lacking physical structures or noncompliance with procedures. Next, the authors recommend simulating scenarios for validation before implementation.
Practical implications
The study explored ways to enhance layout and medicine distribution at the PSC, focusing on reducing loss and cost impact.
Originality/value
Originality lies in LH application in a PSC of PH, often applied in secondary or tertiary health levels like hospitals. The novelty necessitated adaptations of tools for future PSC applications.
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