A PRISMA-driven systematic review of data mining methods used for defects detection and classification in the manufacturing industry

Bártová, Blanka; Bína, Vladislav; Váchová, Lucie

doi:10.1590/0103-6513.20210097

Cited by 8 publications

(6 citation statements)

References 63 publications

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“…It was subsequently enhanced as PRISMA 2020, which includes reporting guidance that advance sophisticated methodologies for identifying, selecting, assessing, and synthesising research [33]. A few examples of the PRISMA method in the manufacturing area are sustainable development [28], sustainable manufacturing [35], lean manual assembly [30], manufacturing data mining [7], factory planning [10], additive manufacturing [27], manufacturing methodology [19], augmented reality manufacturing [22] and Industry 4.0 [39]. As a result, this technique is suited for systematically describing the actions and methods for improving stamping die production via searching and identification, screening, eligibility, data abstraction, and analysis.…”

Section: Methodsmentioning

confidence: 99%

Improvement Activities in Stamping Die Manufacturing: A Systematic Literature Review

Rozeman¹,

Adesta²,

Sophian³

et al. 2023

IJEMM

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Die making is regarded as the mother of all industries. In the manufacturing world, this industry is thought to have existed for a long time. There is a need to address the improvement activities in this area in order to assess their relevance, results, and impacts on its latest industrial development. However, there were insufficient studies that systematically reviewed the existing literature related to stamping die manufacturing's latest improvement activities. As a result, the current article conducted a systematic literature review on stamping die manufacturing improvement activities. The present study applies the integrated multiple research design, and the review was based on the publication standard, namely the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA). This research is using two leading databases, namely Scopus and Science Direct, and five supporting databases, namely Emerald Insight, IEE Explore, Wiley Online, Taylor & Francis, and Google Scholar. Using thematic analysis, this review has six main themes: design, machining, finishing, trials, and overall improvements. These six major themes were subdivided into 20 sub-themes. The findings show that the researchers are covering most of the improvements in the main elements of die manufacturing processes. Based on this study, the contribution for practical purposes of stamping die manufacturing improvements was identified. The study contributes significant findings, such as detailed improvement activities that are specific to the targeted issues but have the potential to be adapted or imitated by other practitioners and future researchers.

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Section: Methodsmentioning

confidence: 99%

Improvement Activities in Stamping Die Manufacturing: A Systematic Literature Review

Rozeman¹,

Adesta²,

Sophian³

et al. 2023

IJEMM

View full text Add to dashboard Cite

show abstract

“…Based on a previously developed PRISMA-based systematic review (Bartova, Bina & Vachova, 2022), the method Support Vector Machine (SVM) was chosen for further research on effective defect detection. The PCA method for feature extraction was used as a preprocessing step.…”

Section: Methodsmentioning

confidence: 99%

“…The research authors aimed to find a solution or propose a model for the higher accuracy of defect detection. Based on the previous literature review (Bartova, Bina & Vachova, 2022), the chosen method for this classification task was the support vector machine (SVM). SVMs are currently a hot topic in the machine learning community, creating a similar enthusiasm now as previously encountered by Artificial Neural Networks.…”

Section: Engineering Management In Production and Servicesmentioning

confidence: 99%

A Novel Data Mining Approach for Defect Detection in the Printed Circuit Board Manufacturing Process

Bártová

Bína

2022

Engineering Management in Production and Services

Self Cite

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This research aims to propose an effective model for the detection of defective Printed Circuit Boards (PCBs) in the output stage of the Surface-Mount Technology (SMT) line. The emphasis is placed on increasing the classification accuracy, reducing the algorithm training time, and a further improvement of the final product quality. This approach combines a feature extraction technique, the Principal Component Analysis (PCA), and a classification algorithm, the Support Vector Machine (SVM), with previously applied Automated Optical Inspection (AOI). Different types of SVM algorithms (linear, kernels and weighted) were tuned to get the best accuracy of the resulting algorithm for separating good-quality and defective products. A novel automated defect detection approach for the PCB manufacturing process is proposed. The data from the real PCB manufacturing process were used for this experimental study. The resulting PCALWSVM model achieved 100 % accuracy in the PCB defect detection task. This article proposes a potentially unique model for accurate defect detection in the PCB industry. A combination of PCA and LWSVM methods with AOI technology is an original and effective solution. The proposed model can be used in various manufacturing companies as a postprocessing step for an SMT line with AOI, either for accurate defect detection or for preventing false calls.

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“…Support systems for artificial intelligence (AI) present bright opportunities for improving students' psychological well-being in higher education. Artificial Intelligence has a number of clear advantages over traditional support approaches [11]. Obviously, Improved Accessibility is one of AI based system advances.…”

Section: Rq 1 To What Extent Can Ai-based Support Systems Be Used To ...mentioning

confidence: 99%

“…On the other hand, AI systems are always available and can offer prompt assistance when required. Students who are reluctant to ask for aid because of time restrictions or social shame would especially benefit from this [11,12]. Secondly, Personalized Interventions has been known as AI's strength.…”

Section: Rq 1 To What Extent Can Ai-based Support Systems Be Used To ...mentioning

confidence: 99%

Enhancing Psychological Well-being in Higher Education Post-Covid-19 Pandemic. The Role of AI-Based Support Systems—Bibliometric Reviews

Nguyen Thuy Van,

Mohd Amran Mohd Daril,

Masroor Ali

et al. 2024

Int. J. Onl. Eng.

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Psychological well-being is a cornerstone of student success in higher education. However, many students struggle with mental health challenges like stress, anxiety, and depression during and even after the Covid-19 pandemic. These challenges, often stemming from academic, personal, social, or career concerns, negatively impact student learning and development. This underscores the need for robust support systems within higher education (HE). Artificial intelligence (AI) emerges as a promising field in educational technology, offering students readily available guidance on their path to well-being. This research, guided by the PRISMA Statement 2015, provides an overview of AI applications in higher education through a systematic review. From an initial pool of 270 publications identified between year 2021 and 2023, finally, 24 articles met our inclusion criteria and were analyzed for the final synthesis. This paper revealed three key areas where AI-based systems can support student well-being: i) AI’s Advancement and Potential: Exploring the evolving capabilities and promise of AI in this context. ii) Building Effective AI Systems: Identifying crucial components for successful AI-based well-being interventions. iii) Barriers to Implementing AI in Higher Education: Addressing ethical considerations and challenges unique to academic settings. The conclusions and the road ahead from this research is the critical need for ethical, well-designed AI-based systems to overcome existing barriers and deliver exceptional student well-being support services. By prioritizing student mental health and providing them with the necessary tools and resources, we can empower them to achieve their full potential and thrive in their academic endeavors.

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A PRISMA-driven systematic review of data mining methods used for defects detection and classification in the manufacturing industry

Cited by 8 publications

References 63 publications

Improvement Activities in Stamping Die Manufacturing: A Systematic Literature Review

Improvement Activities in Stamping Die Manufacturing: A Systematic Literature Review

A Novel Data Mining Approach for Defect Detection in the Printed Circuit Board Manufacturing Process

Enhancing Psychological Well-being in Higher Education Post-Covid-19 Pandemic. The Role of AI-Based Support Systems—Bibliometric Reviews

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