Overview and comparison of different sensor positions and measuring methods for the process force measurement in stamping operations

Groche, Peter; Hohmann, Johannes; Übelacker, David

doi:10.1016/j.measurement.2018.11.058

Cited by 21 publications

(13 citation statements)

References 18 publications

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“…Investigations on diagnostic monitoring primarily focus on the detection of correlations between process variables and abnormal process conditions. In most cases, force sensors are therefore integrated within the tool [21,22]. The findings of these investigations primarily apply to the protection of machines and tools and define thresholds and envelopes where a decision is made between acceptable and harmful conditions [23,24].…”

Section: Monitoring Of Property Deviationsmentioning

confidence: 99%

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Exploitation of force displacement curves in blanking—feature engineering beyond defect detection

Kubik

Hohmann

Groche

2021

Int J Adv Manuf Technol

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With the current tendency of mass production towards customer-oriented serial production, blanking processes are facing new challenges. They require an increase in knowledge about faulty process conditions and their influence on the quality of a component as well as an instruction for a target-oriented adaptation of the process. The aim of this study is therefore to identify property deviations based on force-displacement curves and to establish correlations between the quality of the component and features of force-displacement curves. For this purpose, parameter variations are carried out on a high-speed press and features are extracted from these measured time series. Afterwards, the correlation between varying process parameters and features is carried out to obtain a conclusion about the condition of the component. The results of these studies for a regression analysis form the basis for a decision support system to identify deviations of the component as well as faulty process conditions. The paper shows that a reliable correlation between the quality of the component and force-displacement curves is possibly based on the feature engineering approach even under industrial boundary conditions. This also applies to the simultaneous modification and variations within a limited range of several process parameters.

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Section: Monitoring Of Property Deviationsmentioning

confidence: 99%

“…Literature has shown that qualitative differences within force-displacement curves caused by varying boundary conditions of the process can be determined [21,22]. However, it is not possible to conclude property deviations from these variations or their influence on the quality of the component.…”

Section: Feature Engineeringmentioning

confidence: 99%

Exploitation of force displacement curves in blanking—feature engineering beyond defect detection

Kubik

Hohmann

Groche

2021

Int J Adv Manuf Technol

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“…The performance of the classification model depends on the quality of the data set and related to this to their acquisition procedure (Calmano et al 2013;Groche et al 2019). It is decisive which sensor types are selected, which measuring method is used and which position is chosen inside the tool or the press.…”

Section: Model Performance Depending On Sensor Position and Typementioning

confidence: 99%

“…In contrast, small variations of process variables influence the time signals recorded by sensors less strongly. This leads to high demands on the quality of the acquired data even at high stroke rates in order to be able to physically differentiate effects in the process from noise even at minor variations (Groche et al 2019).…”

Section: Introductionmentioning

confidence: 99%

Smart sheet metal forming: importance of data acquisition, preprocessing and transformation on the performance of a multiclass support vector machine for predicting wear states during blanking

2021

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In consequence of high cost pressure and the progressive globalization of markets, blanking, which represents the most economical process in the value chain of manufacturing companies, is particularly dependent on reducing machine downtimes and increasing the degree of utilization. For this purpose, it is necessary to be able to make a real-time prediction about the current and future process conditions even at high production rates. Therefore, this study investigates the influence of data acquisition, preprocessing and transformation on the performance of a multiclass support vector machine to classify abrasive wear states during blanking based on force signals. The performance of the model was quantitatively evaluated based on the model accuracy and the separability of the classes. As a result, it was shown, that the deviation of time series represents the key parameter for the resulting performance of the classification model and strongly depends on the sensor type and position, the preprocessing procedure as well as the feature extraction and selection. Furthermore, it is shown that the consideration of domain knowledge in the phases of data acquisition, preprocessing and transformation improves the performance of the classification model and is essential to successfully implement AI projects. Summarizing the findings of this study, trustworthy data sets play a crucial role for implementing an automated process monitoring as a basis for resilient manufacturing systems.

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“…The primary component is the development of data acquisition systems that collect production data on a product-to-product basis. Few examples are machine vision systems for ring rolling [3,4], deepdrawing [5] and sheet rolling [6]; integrated sensors for local force measurement [7]; and toolintegrated distance measurement [8] and skid line detection [9] for stamping of car body parts. The development of such industrial metrology systems has led to the publication of several large metal forming datasets [8][9][10][11].…”

Section: Introductionmentioning

confidence: 99%

Exploiting data in smart factories: real-time state estimation and model improvement in metal forming mass production

2019

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Modern production systems have numerous sensors that produce large amounts of data. This data can be exploited in many ways, from providing insight into the manufacturing process to facilitating automated decision making. These opportunities are still underexploited in the metal forming industry, due to the complexity of these processes. In this work, a probabilistic framework is proposed for simultaneous model improvement and state estimation in metal forming mass production. Recursive Bayesian estimation is used to simultaneously track the evolution of process state and to estimate the deviation between the physics-based model and the real process. A sheet bending mass production process is used to test the proposed framework. A metamodel of the process is built using proper orthogonal decomposition and radial basis function interpolation. The model is extended with a deviation model in order to account for the difference between model and real process. Particle filtering is used to track the state evolution and to estimate the deviation model parameters simultaneously. The approach is tested and analysed using a large number of simulations, based on pseudo-data obtained from a numerical sheet bending model.

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Overview and comparison of different sensor positions and measuring methods for the process force measurement in stamping operations

Cited by 21 publications

References 18 publications

Exploitation of force displacement curves in blanking—feature engineering beyond defect detection

Exploitation of force displacement curves in blanking—feature engineering beyond defect detection

Smart sheet metal forming: importance of data acquisition, preprocessing and transformation on the performance of a multiclass support vector machine for predicting wear states during blanking

Exploiting data in smart factories: real-time state estimation and model improvement in metal forming mass production

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