A digital twin for composite parts manufacturing : Effects of defects analysis based on manufacturing data

Zambal, Sebastian; Eitzinger, Christian; Clarke, M.; Klintworth, J.W.; Mechin, Pierre-Yves

doi:10.1109/indin.2018.8472014

Cited by 42 publications

(28 citation statements)

References 9 publications

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“…To provide guidance for practical development and implementation, Liu et al [23] developed a conceptual framework for DT-enabled collaborative data management for metal additive manufacturing systems, where a cloud DT communicates with distributed edge DTs in different product life-cycle stages. Zamba et al [24] proposed DT for the manufacturing of structural parts made from carbon fiber composite materials. In order to optimize production process, Sujová et al [25] developed the DT of the real assembly line.…”

Section: Literature Reviewmentioning

confidence: 99%

Implementation of Digital Twin for Engine Block Manufacturing Processes

et al. 2020

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The digital twin (DT) is undergoing an increase in interest from both an academic and industrial perspective. Although many authors proposed and described various frameworks for DT implementation in the manufacturing industry context, there is an absence of real-life implementation studies reported in the available literature. The main aim of this paper is to demonstrate feasibility of the DT implementation under real conditions of a production plant that is specializing in manufacturing of the aluminum components for the automotive industry. The implementation framework of the DT for engine block manufacturing processes consists of three layers: physical layer, virtual layer and information-processing layer. A simulation model was created using the Tecnomatix Plant Simulation (TPS) software. In order to obtain real-time status data of the production line, programmable logic control (PLC) sensors were used for raw data acquisition. To increase production line productivity, the algorithm for bottlenecks detection was developed and implemented into the DT. Despite the fact that the implementation process is still under development and only partial results are presented in this paper, the DT seems to be a prospective real-time optimization tool for the industrial partner.

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Section: Literature Reviewmentioning

confidence: 99%

Implementation of Digital Twin for Engine Block Manufacturing Processes

et al. 2020

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“…Fiber steering can also provoke the twisting of tows [11,16,27,31]. Consequently gaps arise in the immediate vicinity of the twisted tow [80,81]. As secondary imperfections, fiber waviness might evolve in adjacent plies [31].…”

Section: Type V-twisted Towmentioning

confidence: 99%

Manufacturing-Induced Imperfections in Composite Parts Manufactured via Automated Fiber Placement

2019

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The automated fiber placement process (AFP) enables the manufacturing of large and geometrical complex fiber composite structures with high quality at low cycle times. Although the AFP process is highly accurate and reproducible, manufacturing induced imperfections in the produced composite structure occur. This review summarizes and classifies typical AFP-related manufacturing defects. Several methodologies for evaluating the effects of such manufacturing defects from the literature are reviewed. This review paper presents recent scientific contributions and discusses proposed experimental and simulation-based methodologies. Among the identified ten defect classes, gaps and overlaps are predominant. This paper focuses then on methods for modelling and assessing gaps and overlaps. The state of the art in modelling gaps and overlaps and assessing their influence on mechanical properties is presented. Finally, research gaps and remaining issues are identified.

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“…Measurements within the processes as well as sensor data in the tool or on its surface [15] further improve the accuracy of the digital twin. For the manufacturing of structural components, several data-driven approaches for virtual quality inspection have been proposed, for example, metal casting [16,17], extrusion of tubes [18], injection molding [19] as well as automated dry material placement for composite structures [20].…”

Section: Introductionmentioning

confidence: 99%

“…These approaches usually do not generate a three dimensional distribution of structural properties (with the exception of Zambal et al [20], where a FEM is calculated offline following the quality prediction by a data-driven model). They also do not support a model-based understanding of the process dependencies and the internal properties.…”

Section: Introductionmentioning

confidence: 99%

Combining Simulation and Machine Learning as Digital Twin for the Manufacturing of Overmolded Thermoplastic Composites

Hürkamp

Gellrich

Ossowski

et al. 2020

JMMP

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The design and development of composite structures requires precise and robust manufacturing processes. Composite materials such as fiber reinforced thermoplastics (FRTP) provide a good balance between manufacturing time, mechanical performance and weight. In this contribution, we investigate the process combination of thermoforming FRTP sheets (organo sheets) and injection overmolding of short FRTP for automotive structures. The limiting factor in those structures is the bond strength between the organo sheet and the overmolded thermoplastic. Within this process chain, even small deviations of the process settings (e.g., temperature) can lead to significant defects in the structure. A cyber physical production system based framework for a digital twin combining simulation and machine learning is presented. Based on parametric Finite-Element-Method (FEM) studies, training data for machine learning methods are generated and a FEM surrogate is developed. A comparison of different data-driven methods yields information on the estimation accuracy of task-specific data-driven methods. Finally, in accordance with experimental cross tension tests, the investigated FEM surrogate model is able to predict the interface bond strength quality in dependence of the process settings. The visualization into different quality domains qualifies the presented approach as decision support.

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A digital twin for composite parts manufacturing : Effects of defects analysis based on manufacturing data

Cited by 42 publications

References 9 publications

Implementation of Digital Twin for Engine Block Manufacturing Processes

Implementation of Digital Twin for Engine Block Manufacturing Processes

Manufacturing-Induced Imperfections in Composite Parts Manufactured via Automated Fiber Placement

Combining Simulation and Machine Learning as Digital Twin for the Manufacturing of Overmolded Thermoplastic Composites

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