Data-Based Quality Management in the Internet of Production

Schmitt, Robert; Ellerich, Max; Schlegel, Peter; Ngo, Quoc Hao; Emonts, Dominik; Montavon, Benjamin; Buschmann, Daniel; Lauther, Rebecca; Klasen, Nils; Kiesel, Raphael

doi:10.1007/978-3-662-64448-5_20

Cited by 2 publications

(3 citation statements)

References 26 publications

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“…ZDM emphasizes defect prevention and prediction [39]. Predictive quality is the optimization of product and process-related quality through data-driven predictions as a basis for decision-making and action [40]. The target variables are interdependent, so high process quality is a necessity but not a sufficient condition for high product quality.…”

Section: Zero-defect Manufacturing (Zdm)mentioning

confidence: 99%

Enhance the Injection Molding Quality Prediction with Artificial Intelligence to Reach Zero-Defect Manufacturing

Silva

Marques²,

Faustino

et al. 2022

Processes

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With the spread of the Industry 4.0 concept, implementing Artificial Intelligence approaches on the shop floor that allow companies to increase their competitiveness in the market is starting to be prioritized. Due to the complexity of the processes used in the industry, the inclusion of a real-time Quality Prediction methodology avoids a considerable number of costs to companies. This paper exposes the whole process of introducing Artificial Intelligence in plastic injection molding processes in a company in Portugal. All the implementations and methodologies used are presented, from data collection to real-time classification, such as Data Augmentation and Human-in-the-Loop labeling, among others. This approach also allows predicting and alerting with regard to process quality loss. This leads to a reduction in the production of non-compliant parts, which increases productivity and reduces costs and environmental footprint. In order to understand the applicability of this system, it was tested in different injection molding processes (traditional and stretch and blow) and with different materials and products. The results of this document show that, with the approach developed and presented, it was possible to achieve an increase in Overall Equipment Effectiveness (OEE) of up to 12%, a reduction in the process downtime of up to 9% and a significant reduction in the number of non-conforming parts produced. This improvement in key performance indicators proves the potential of this solution.

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Section: Zero-defect Manufacturing (Zdm)mentioning

confidence: 99%

Enhance the Injection Molding Quality Prediction with Artificial Intelligence to Reach Zero-Defect Manufacturing

Silva

Marques²,

Faustino

et al. 2022

Processes

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show abstract

“…To achieve this, CPPS enables (semi‐) autonomous decision‐making between single assets that ideally leads to shorter response time and optimal resource utilization due to transparent and comprehensive providence of relevant information 2 . The main prerequisite for enabling CPPS is the availability of data and information and the capability for non‐humans to understand and interpret those data and information to gain knowledge and derive adequate actions 3 . Therefore, essential building blocks for realizing CPPS are Digital Twins and Digital Shadows.…”

Section: Introductionmentioning

confidence: 99%

“…2 The main prerequisite for enabling CPPS is the availability of data and information and the capability for non-humans to understand and interpret those data and information to gain knowledge and derive adequate actions. 3 Therefore, essential building blocks for realizing CPPS are Digital Twins and Digital Shadows. While a Digital Twin represents an asset in the virtual world, a Digital Shadow supports decision-makers in their daily work by providing data-based decision-support to a specific task.…”

Section: Introductionmentioning

confidence: 99%

Challenges and research agenda for realizing a seamless integration of Digital Shadows into the production domain

Sapel

Hopmann

2022

Engineering Reports

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One vision of industry 4.0 is an autonomous shopfloor configuration. A possibility to transfer this vision to reality is realizable with Digital Shadows. As Digital Shadows are well defined in research, challenges for seamless integration in production are still an open research topic. The main challenge is the enrichment of semantics on the domain in a standardized format that enables multilateral communication between all assets. To overcome these challenge, tools have to be developed to support domain experts in building and integrating Digital Shadows without knowledge of modeling and programming. This article demonstrates the main challenges of this objective and proposes a research agenda to close this research gap.

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Data-Based Quality Management in the Internet of Production

Cited by 2 publications

References 26 publications

Enhance the Injection Molding Quality Prediction with Artificial Intelligence to Reach Zero-Defect Manufacturing

Enhance the Injection Molding Quality Prediction with Artificial Intelligence to Reach Zero-Defect Manufacturing

Challenges and research agenda for realizing a seamless integration of Digital Shadows into the production domain

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