Data-driven model development for quality prediction in forming technology

Today, design and operation of manufacturing processes heavily rely on the use of models, some analytical, empirical or numerical i.e. finite element simulations. Models do reflect reality as best as their design and structure may appear, but in many cases, they are based on simplifying assumptions and abstractions. Reality in production, i.e. reflected by measures such as forces, deflections, travels, vibrations etc. during the process execution, is tremendously characterised by noise and fluctuations revealing a stochastic nature. In metal forming such kind of impact on produced product today in detail is neither explainable nor supported by the aforementioned models. In industrial manufacturing the game to deal with process data changed completely and engineers learned to value the high significance of information included in such digital signals. It should be acknowledged that process data gained from real process environments in many cases contain plenty of technological information, which may lead to increase efficiency of production, to reduce downtime or to avoid scrap. For this reason, authors started to focus on process data gained from numerous metal forming technologies and sheet metal blanking in order to use them for process design objectives. The supporting idea was found in a potential combination of conventional process design strategies with new models purely based on digital signals captured by sensors, actuators and production equipment in general. To utilise established models combined with process data, the following obstacles have to be addressed: (1) acquired process data is biased by sensor artifacts and often lacks data quality requirements; (2) mathematical models such as neural networks heavily rely on high quantities of training data with good quality and sufficient context, but such quantities often are not available or impossible to gain; (3) data-driven black-box models often lack interpretability of containing results, further opposing difficulties to assess their plausibility and extract new knowledge. In this paper, an insight on usage of available data science methods like feature-engineering and clustering on metal forming and blanking process data is presented. Therefore, the paper is complemented with recent approaches of data-driven models and methods for capturing, revealing and explaining previously invisible process interactions. In addition, authors follow with descriptions about recent findings and current challenges of four practical use cases taken from different domains in metal forming and blanking. Finally, authors present and discuss a structure for data-driven process modelling as an approach to extent existing data-driven models and derive process knowledge from process data objecting a robust metal forming system design. The paper also aims to figure out future demands in research in this challenging field of increasing robustness for such kind of manufacturing processes.

Section: Wrapper Methods Filter Methodsmentioning

confidence: 99%

Perspectives on data-driven models and its potentials in metal forming and blanking technologies

Liewald

Bergs

Groche

et al. 2022

“…Kirchen et al developed a model for predicting the homogeneity of the thickness of tailored blanks during a rolling process using an incremental regression method. Experimental results showed that the regression model was able to accuratly predict the sheet thickness with a maximum deviation of 5% [41]. Hao et al developed an interaction model that uses a linear regression model to quantify the onset of wear during a multistage manufacturing process, considering product quality degradation and the interaction between a current stage and a subsequent stage [42].…”

Section: Data-driven Methods For Tool Wear Predictionmentioning

confidence: 99%

Towards a systematical approach for wear detection in sheet metal forming using machine learning

Kubik

Becker

Molitor

et al. 2022

Wear is one of the decisive factors for the economic efficiency of sheet metal forming processes. Thereby, progressive wear phenome lead on the one hand to a poor workpiece quality and on the other hand to tool failure resulting in high machine downtimes. This trend is intensified by processing high-strength materials and the reduction of lubricant up to dry forming. In this context, data-driven monitoring methods such as machine learning (ML) provide the potential of detecting wear at an early stage to overcome manual and cost-intensive process inspections. The presented study aims to provide a ML based inline quantification of wear states within sheet metal forming processes. The development of this monitoring approach is based on a procedure model the Knowledge Discovery in Time series and image data in Engineering Epplications (KDT-EA) which is validated on two forming processes, blanking and roll forming, that strongly differ in their physical process behavior and their acquired process data. The presented inline quantification allows an estimation of wear states with a deviation of less than 0.83% for the blanking process and 2.21% for the roll forming process from the actual wear state. Furthermore, it is shown that combining different feature extraction methods as well as a compensation of unbalanced data using data augmentation techniques are able to improve the performance of the investigated ML models.

“…Due to massive improvements in artificial intelligence, quality prediction approaches increased in other manufacturing processes as well. For example, the utilization of unsupervised methods for a quality monitoring in metallic powder presses [8] or quality prediction regression models in rolling by Kirch et al [9]. Another in-process quality monitoring approach is proposed by BauerdicK for machine tools [10].…”

Section: Quality Predictionmentioning

confidence: 99%

Improving quality prediction in radial-axial ring rolling using a semi-supervised approach and generative adversarial networks for synthetic data generation

Fahle

Gläser

Kneißler

et al. 2021

As artificial intelligence and especially machine learning gained a lot of attention during the last few years, methods and models have been improving and are becoming easily applicable. This possibility was used to develop a quality prediction system using supervised machine learning methods in form of time series classification models to predict ovality in radial-axial ring rolling. Different preprocessing steps and model implementations have been used to improve quality prediction. A semi-supervised approach is used to improve the prediction and analyze, to what extend it can improve current research in machine learning for quality prediciton. Moreover, first research steps are taken towards a synthetic data generation within the radial-axial ring rolling domain using generative adversarial networks.