Virtual Quality Gates in Manufacturing Systems: Framework, Implementation and Potential

Filz, Marc-André; Gellrich, Sebastian; Turetskyy, Artem; Wessel, Jacob; Herrmann, Christoph; Thiede, Sebastian

doi:10.3390/jmmp4040106

Cited by 17 publications

(9 citation statements)

References 58 publications

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“…In this context, Hürkamp et al show the applicability and potentials of fast and accurate data-driven surrogates of FEM simulations for the design of process chains [24] and as virtual quality gates [17]. In addition, Filz et al [7] discuss the design and potentials of virtual quality gates for future manufacturing, considering FEM surrogate modeling as a promising approach.…”

Section: Integrated Computational Product and Production Engineeringmentioning

confidence: 99%

“…In this context, digital twins are even able to implement a databased control of the production [5,6]. Several approaches for (virtual) quality gates in manufacturing systems based on process and machine data can be found for single processes, but very sparse approaches for process chains [7]. If process chains are considered, the data-driven models widely draw on product data such as intermediate and final product properties.…”

Section: Introductionmentioning

confidence: 99%

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Machine learning and simulation-based surrogate modeling for improved process chain operation

Hürkamp

Gellrich

Dér

et al. 2021

Int J Adv Manuf Technol

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In this contribution, a concept is presented that combines different simulation paradigms during the engineering phase. These methods are transferred into the operation phase by the use of data-based surrogates. As an virtual production scenario, the process combination of thermoforming continuous fiber-reinforced thermoplastic sheets and injection overmolding of thermoplastic polymers is investigated. Since this process is very sensitive regarding the temperature, the volatile transfer time is considered in a dynamic process chain control. Based on numerical analyses of the injection molding process, a surrogate model is developed. It enables a fast prediction of the product quality based on the temperature history. The physical model is transferred to an agent-based process chain simulation identifying lead time, bottle necks and quality rates taking into account the whole process chain. In the second step of surrogate modeling, a feasible soft sensor model is derived for quality control over the process chain during the operation stage. For this specific uses case, the production rejection can be reduced by 12% compared to conventional static approaches.

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Section: Integrated Computational Product and Production Engineeringmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Machine learning and simulation-based surrogate modeling for improved process chain operation

Hürkamp

Gellrich

Dér

et al. 2021

Int J Adv Manuf Technol

Self Cite

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“…Filz, et al [19], provide a comprehensive overview of virtual quality gates in manufacturing systems. This method supports the reduction of the total cost of quality while ensuring high transparency about process-product relationships in production.…”

Section: Quality Managementmentioning

confidence: 99%

“…This can only be achieved by understanding the dynamics and interdependencies within the process chain. Furthermore, the "context of product quality, transparency and understanding of process-product relationships, which describe interactions and the impact of the manufacturing processes on the resulting intermediate product features and vice versa, are of great importance" [19]. The concept of quality gates is therefore to systematically divide the manufacturing chain into different quality-relevant decision points.…”

Section: Quality Managementmentioning

confidence: 99%

Life-Cycle Oriented Risk Assessment Using a Monte Carlo Simulation

et al. 2021

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State of the art mechatronic systems are complex assemblies of various parts and sub-systems. In such an interconnected system, even relatively cheap parts can have a major impact on the overall performance due to unexpected failure. Hence, lifecycle management has major implications on the successful modification of existing products. Potential savings due to changes in production and procurement must be compared to the implied risk of products failing in the field due to these changes. This work documents a generic approach for risk assessment based on the distribution of the expected savings and incident costs over the whole lifecycle. To do so, a stochastic model is introduced to quantify the expected savings and costs given a non-risk-free product modification. Using a Monte Carlo simulation, the effects of uncertainty are incorporated into the risk management. The model and simulation are deployed within an industrial use case. The application demonstrates both the appropriateness of the tool and its useability.

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“…Filz et al [5] introduced the CPPS concept of virtual quality gates that allow for the data-based prediction of product quality properties as not only the basis for improved planning and operation of production processes, but also process chains. Based on an overall framework with different design options, several case studies were shown to demonstrate applicability and potential.…”

mentioning

confidence: 99%