Improving quality control by early prediction of manufacturing outcomes

Weiss, Sholom M.; Dhurandhar, Amit; Baseman, Robert J.

doi:10.1145/2487575.2488192

Cited by 17 publications

(8 citation statements)

References 19 publications

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“…Therefore, most primary studies on manufacturing industries propose manufacturing analytics frameworks and support data quality as a preprocessing step for data analytics input. For instance, Weiss et al [46] provide methods for continually predicting manufactured product quality in semiconductor manufacturing. The proposed methods have a data preprocessing step to predict missing input data.…”

Section: Rq1 -What Is Data Quality For Cps and Iot In Industry 40?mentioning

confidence: 99%

A Systematic Review of Data Quality in CPS and IoT for Industry 4.0

et al. 2023

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The Internet of Things (IoT) and Cyber-Physical Systems (CPS) are the backbones of Industry 4.0, where data quality is crucial for decision support. Data quality in these systems can deteriorate due to sensor failures or uncertain operating environments. Our objective is to summarize and assess the research efforts that address data quality in data-centric CPS/IoT industrial applications. We systematically review the state-of-the-art data quality techniques for CPS and IoT in Industry 4.0 through a systematic literature review (SLR) study. We pose three research questions, define selection and exclusion criteria for primary studies, and extract and synthesize data from these studies to answer our research questions. Our most significant results are (i) the list of data quality issues, their sources, and application domains, (ii) the best practices and metrics for managing data quality, (iii) the software engineering solutions employed to manage data quality, and (iv) the state of the data quality techniques (data repair, cleaning, and monitoring) in the application domains. The results of our SLR can help researchers obtain an overview of existing data quality issues, techniques, metrics, and best practices. We suggest research directions that require attention from the research community for follow-up work.

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Section: Rq1 -What Is Data Quality For Cps and Iot In Industry 40?mentioning

confidence: 99%

A Systematic Review of Data Quality in CPS and IoT for Industry 4.0

et al. 2023

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“…Their approach combines data from production across supply chains with customer data and other quality data to classify the engines' quality. The approach of Weiss et al is also concerned with the prediction of product quality along a supply chain, but considering microprocessors [40]. In this context, the main challenge is the lengthy production process and the availability of only little measurement data.…”

Section: Machine Learning For Quality Managementmentioning

confidence: 99%

Toward Automated Support of Complaint Handling Processes: An Application in the Medical Technology Industry

2021

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Complaints about finished products are a major challenge for companies in the medical technology industry, where product quality is directly related to public health and therefore strictly regulated. In this paper, we examine how available data can be used to provide automated support to the complaint handling processes in the medical technology companies. We identify the automation potentials in the 8D reference process for complaint management and discuss their organizational and technical challenges. Using data from a large manufacturer of medical products, we show how partial process automation can be achieved in practice by designing, implementing, and evaluating a deep learning-based prototype for automatically suggesting a likely error code for future complaints, given their textual description. Our approach is able to assign the correct error code for more than 75% of all cases and outperforms the conventional classification approaches used as a baseline comparison. Our results show that partial automation of a complaint management process by means of deep learning can be achieved in practice.

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“…Contrary to monitoring the production process for diagnostic purposes, the focus of this paper [4] has been on predicting the quality of the final product. Specifically, the attention has been directed towards the semiconductor industry, where the main intention has been to anticipate the performance of the complete microprocessors.…”

Section: Introductionmentioning

confidence: 99%

A Review of Artificial Intelligence Techniques for Quality Control in Semiconductor Production

Das

2024

IJCE

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Purpose: Exploring AI techniques to improve the quality control of semiconductor production brings numerous advantages, such as enhanced precision, heightened efficiency, and early detection of issues, cost reduction, continuous enhancement, and a competitive edge. These benefits establish this area of research and its practical application in the semiconductor industry as valuable and worthwhile. Methodology: It aims to highlight the advancements, methodologies employed, and outcomes obtained thus far. By scrutinizing the current state of research, the primary objective of this paper is to identify significant challenges and issues associated with AI approaches in this domain. These challenges encompass data quality and availability, selecting appropriate algorithms, interpreting AI models, and integrating them with existing production systems. It is vital for researchers and industry professionals to understand these challenges to effectively address them and devise effective solutions. Moreover, it aims to lay the groundwork for future researchers, offering them a theoretical framework to devise potential solutions for enhancing quality control in semiconductor production. This review aims to drive a research on the semi-conductor production with the AI techniques to enhance the Quality control. Findings: The main findings to offer research is more efficient and accurate approach compared to traditional manual methods, leading to improved product quality, reduced costs, and increased productivity. Armed with this knowledge, future researchers can design and implement innovative AI-driven solutions to enhance quality control in semiconductor production. Unique contribution to theory, policy and practice: Overall, the theoretical foundation presented in this paper will aid researchers in developing novel solutions to improve quality control in the semiconductor industry, ultimately leading to enhanced product reliability and customer satisfaction.

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Improving quality control by early prediction of manufacturing outcomes

Cited by 17 publications

References 19 publications

A Systematic Review of Data Quality in CPS and IoT for Industry 4.0

A Systematic Review of Data Quality in CPS and IoT for Industry 4.0

Toward Automated Support of Complaint Handling Processes: An Application in the Medical Technology Industry

A Review of Artificial Intelligence Techniques for Quality Control in Semiconductor Production

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