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This study aims to identify leather type and authenticity through optical coherence tomography.

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.

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.

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.

Organisations are investing in systems such as product lifecycle management (PLM) to support product development, collaboration across complex supply chains and to provide a framework for digital transformation. Graduates of apparel programmes would benefit from a knowledge of PLM to help realise the opportunities that PLM offers. The purpose of this paper is to report on an educational research project that used PLM as a context for practice-based learning and as a mechanism to update the learning experience and stimulate the development of future practice.

This paper reports on the experiences, critical reflections and data from an action research study to establish a learning community through an educational partnership for PLM software within an undergraduate fashion business course. The cohort of the first year of the intervention (n = 28) is the main study population.

The findings indicate that PLM provided a stimulating learning context supportive of a detailed understanding of current industry practice, critical and innovative thinking and the development of a professional identity.

The opportunity for the development of both industry and educational practice is outlined.

A general introduction to PLM provides important information to support and advance Fashion Industry 4.0. Educational partnerships can reduce barriers to the integration of advanced technologies into the higher education curriculum.

Applications of PLM are under researched in textiles and apparel. The paper contributes to the broadening of the knowledge base of PLM and its potential to achieve strategic transformation of the sector.

This study’s main objective is to explore the ISO 9001 implementation model and identify a future research agenda. This is important because not all organizations find it easy to implement ISO 9001, and not all organizations get positive benefits after implementing it.

The paper presents a comprehensive review of the literature on ISO 9001 implementation models using the preferred reporting items for systematic reviews (PRISMA) methodology to systematically review the existing literature on ISO 9001 implementation models. Relevant studies published from 2003 to early 2023 are explored to reveal the research landscape, gaps and trends.

Many ISO 9001 implementation methods have been developed for actual implementation in organizations, including models, frameworks, special variable considerations, application uses and integration. These methods were developed and applied to cover gaps regarding constraints, unbeneficial, special conditions, implementation objectives and organization types in ISO 9001 implementation. Current issues and future research on ISO 9001 implementation models were found, namely ISO 9001 implementation models specific to SMEs, ISO 9001 implementation levels, ISO 9001 implementation models that are agile to change, and affordable certification models.

Only a few researchers have systematically reviewed the literature or taken a bibliometric approach in their analyses to provide an overview of the current trends and links to ISO 9001 implementation models. The ISO 9001 standard is a general standard and can be applied by all organizations with the implementation method left to the implementer. Many implementation methods have been developed, but several implementation obstacles and disadvantages are still found. It is important to know the extent of current research and discover future research gaps regarding methods of implementing the ISO 9001 standard.

The compliance checking of Building Information Modeling (BIM) models is crucial throughout the lifecycle of construction. The increasing amount and complexity of information carried by BIM models have made compliance checking more challenging, and manual methods are prone to errors. Therefore, this study aims to propose an integrative conceptual framework for automated compliance checking of BIM models, allowing for the identification of errors within BIM models.

This study first analyzed the typical building standards in the field of architecture and fire protection, and then the ontology of these elements is developed. Based on this, a building standard corpus is built, and deep learning models are trained to automatically label the building standard texts. The Neo4j is utilized for knowledge graph construction and storage, and a data extraction method based on the Dynamo is designed to obtain checking data files. After that, a matching algorithm is devised to express the logical rules of knowledge graph triples, resulting in automated compliance checking for BIM models.

Case validation results showed that this theoretical framework can achieve the automatic construction of domain knowledge graphs and automatic checking of BIM model compliance. Compared with traditional methods, this method has a higher degree of automation and portability.

This study introduces knowledge graphs and natural language processing technology into the field of BIM model checking and completes the automated process of constructing domain knowledge graphs and checking BIM model data. The validation of its functionality and usability through two case studies on a self-developed BIM checking platform.

The purpose of this study is to identify the characterizations of business process management (BPM) methodology in hotel industry through an aggregate processing of the core cyclesteps (CCCs) of the highly-cited BPM life-cycle models in the literature aiming to highlight the major issues of the current methodological approach of BPM in hotels when to put the notion of service process into practice.

The paper identifies and examines the most popular BPM life-cycles models in the literature and locates 15 life-cycles that are highly cited. The paper then focuses on applying the theory on nine listed hotel companies in Egypt using a questionnaire in the form of a semi-structured interview technique.

The CCSs of BPM life-cycle model applied in hotels revealed a gap between BPM theory and practice in this sector. Utilizing this model of BPM life-cycle, the paper focuses on describing several of the main problems or pitfalls found in the methodological approach of BPM in hotels, which brings the essence of the whole operation management problems.

In light of these findings, the paper discusses the practical implications and focuses on recommendations on how to properly improve the methodological approach of BPM in hotels in order to get better business results.

The paper bridges the gap between BPM theory and practice and suggests recommendations that will assist hotel companies to eliminate the problems of poor process management (PM). There are also future research recommendations to enhance the knowledge of BPM theory in the service sector.

Medical diagnosis and treatment processes exhibit a high degree of variability, as during the process execution, healthcare professionals can decide on additional steps, change the execution order or skip a task. Process models can help to document and to discuss such processes. However, depicting variability in graphical process models using standardized languages, such as Business Process Model and Notation (BPMN), can lead to large and complicated diagrams that medical staff who do not have formal training in modeling languages have difficulty understanding. This study proposes a pattern-based process visualization that medical doctors can understand without extensive training. The process descriptions using this pattern-based visualization can later be transformed into formal business process models in languages such as BPMN.

The authors derived patterns for expressing variability in healthcare processes from the literature and medical guidelines. Then, the authors evaluated and revised these patterns based on interviews with physicians in a Danish hospital.

A set of business process variability patterns was proposed to express situations with variability in hospital treatment and diagnosis processes. The interviewed medical doctors could translate the patterns into their daily work practice, and the patterns were used to model a hospital process.

When communicating with medical personnel, the patterns can be used as building blocks for documenting and discussing variable processes.

The patterns can reduce complexity in process visualization. This study provides the first validation of these patterns in a hospital.

This paper presents an experimental investigation in establishing the relationship between FDM process parameters and tensile strength of polycarbonate (PC) samples using the I-Optimal design.

I-optimal design methodology is used to plan the experiments by means of Minitab-17.1 software. Samples are manufactured using Stratsys FDM 400mc and tested as per ISO standards. Additionally, an artificial neural network model was developed and compared to the regression model in order to select an appropriate model for optimisation. Finally, the genetic algorithm (GA) solver is executed for improvement of tensile strength of FDM built PC components.

This study demonstrates that the selected process parameters (raster angle, raster to raster air gap, build orientation about Y axis and the number of contours) had significant effect on tensile strength with raster angle being the most influential factor. Increasing the build orientation about Y axis produced specimens with compact structures that resulted in improved fracture resistance.

The fitted regression model has a p-value less than 0.05 which suggests that the model terms significantly represent the tensile strength of PC samples. Further, from the normal probability plot it was found that the residuals follow a straight line, thus the developed model provides adequate predictions. Furthermore, from the validation runs, a close agreement between the predicted and actual values was seen along the reference line which further supports satisfactory model predictions.

This study successfully investigated the effects of the selected process parameters - raster angle, raster to raster air gap, build orientation about Y axis and the number of contours - on tensile strength of PC samples utilising the I-optimal design and ANOVA. In addition, for prediction of the part strength, regression and ANN models were developed. The selected ANN model was optimised using the GA-solver for determination of optimal parameter settings.

The proposed ANN-GA approach is more appropriate to establish the non-linear relationship between the selected process parameters and tensile strength. Further, the proposed ANN-GA methodology can assist in manufacture of various industrial products with Nylon, polyethylene terephthalate glycol (PETG) and PET as new 3DP materials.

This research paper aims to analyze the scientific and grey literature on Quality 4.0 and zero-defect manufacturing (ZDM) frameworks to develop an integrated quality 4.0 framework (IQ4.0F) for quality improvement (QI) based on Six Sigma and machine learning (ML) techniques towards ZDM. The IQ4.0F aims to contribute to the advancement of defect prediction approaches in diverse manufacturing processes. Furthermore, the work enables a comprehensive analysis of process variables influencing product quality with emphasis on the use of supervised and unsupervised ML techniques in Six Sigma’s DMAIC (Define, Measure, Analyze, Improve and Control) cycle stage of “Analyze.”

The research methodology employed a systematic literature review (SLR) based on PRISMA guidelines to develop the integrated framework, followed by a real industrial case study set in the automotive industry to fulfill the objectives of verifying and validating the proposed IQ4.0F with primary data.

This research work demonstrates the value of a “stepwise framework” to facilitate a shift from conventional quality management systems (QMSs) to QMSs 4.0. It uses the IDEF0 modeling methodology and Six Sigma’s DMAIC cycle to structure the steps to be followed to adopt the Quality 4.0 paradigm for QI. It also proves the worth of integrating Six Sigma and ML techniques into the “Analyze” stage of the DMAIC cycle for improving defect prediction in manufacturing processes and supporting problem-solving activities for quality managers.

This research paper introduces a first-of-its-kind Quality 4.0 framework – the IQ4.0F. Each step of the IQ4.0F was verified and validated in an original industrial case study set in the automotive industry. It is the first Quality 4.0 framework, according to the SLR conducted, to utilize the principal component analysis technique as a substitute for “Screening Design” in the Design of Experiments phase and K-means clustering technique for multivariable analysis, identifying process parameters that significantly impact product quality. The proposed IQ4.0F not only empowers decision-makers with the knowledge to launch a Quality 4.0 initiative but also provides quality managers with a systematic problem-solving methodology for quality improvement.