How to characterize a NDT method for weld inspection in battery cell manufacturing using deep learning

Rohkohl, Erik; Kraken, Mathias; Schönemann, Malte; Breuer, Alexander; Herrmann, Christoph

doi:10.1007/s00170-021-08553-7

Cited by 12 publications

(4 citation statements)

References 26 publications

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“…As far as LW applications are concerned, the majority of studies focus on the assessment of dimensional features such as penetration depth, mainly using vision sensors such as photodiodes, cameras, and spectrometers. Very few of them, at least in the case of battery module welding, focus on the assessment of mechanical and electrical quality or even on the identification of specific defects [55]. However, the same cannot be said for another laser LW application in electromobility, hairpin welding.…”

Section: Laser Weldingmentioning

confidence: 99%

Welding Challenges and Quality Assurance in Electric Vehicle Battery Pack Manufacturing

Stavropoulos,

Sabatakakis,

Bikas

2024

Batteries

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Electric vehicles’ batteries, referred to as Battery Packs (BPs), are composed of interconnected battery cells and modules. The utilisation of different materials, configurations, and welding processes forms a plethora of different applications. This level of diversity along with the low maturity of welding designs and the lack of standardisation result in great variations in the mechanical and electrical quality of the joints. Moreover, the high-volume production requirements, meaning the high number of joints per module/BP, increase the absolute number of defects. The first part of this study focuses on associating the challenges of welding application in battery assembly with the key performance indicators of the joints. The second part reviews the existing methods for quality assurance which concerns the joining of battery cells and busbars. Additionally, the second part of this paper identifies the general trends and the research gaps for the most widely adopted welding methods in this domain, while it renders the future directions.

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Section: Laser Weldingmentioning

confidence: 99%

Welding Challenges and Quality Assurance in Electric Vehicle Battery Pack Manufacturing

Stavropoulos,

Sabatakakis,

Bikas

2024

Batteries

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“…Studies involving deep-learning-based image processing have been listed separately (see Table 2). While most of these studies used images as input variables, Rohkohl et al [32] demonstrated the application of deep learning to analyze data from eddy current measurement as an inline method for weld seam inspection, replicating computer tomography images. The majority of the studies are based on supervised ML, aiming to predict or classify an output variable.…”

Section: Overviewmentioning

confidence: 99%

Machine Learning in Lithium‐Ion Battery Cell Production: A Comprehensive Mapping Study

Haghi

Hidalgo

Niri

et al. 2023

Batteries & Supercaps

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With the global quest for improved sustainability, partially realized through the electrification of the transport and energy sectors, battery cell production has gained ever‐increasing attention. An in‐depth understanding of battery production processes and their interdependence is crucial for accelerating the commercialization of material developments, for example, at the volume predicted to underpin future electric vehicle production. Over the last five years, machine learning approaches have shown significant promise in understanding and optimizing the battery production processes. Based on a systematic mapping study, this comprehensive review details the state‐of‐the‐art applications of machine learning within the domain of lithium‐ion battery cell production and highlights the fundamental aspects, such as product and process parameters and adopted algorithms. The compiled findings derived from multi‐perspective comparisons demonstrate the current capabilities and reveal future research opportunities in this field to further accelerate sustainable battery production.

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“…The online inspection of the weld can be implemented with a electrical resistance test after the process or with a offline CT in the laboratory. The CT can resolve very small defects when compared to other non-destructive methods [45].…”

Section: Cell Assemblymentioning

confidence: 99%

Applications and Development of X-ray Inspection Techniques in Battery Cell Production

et al. 2022

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Demand for lithium-ion battery cells (LIB) for electromobility has risen sharply in recent years. In order to continue to serve this growing market, large-scale production capacities require further expansion and the overall effectiveness of processes must be increased. Effectiveness can be significantly optimized through innovative manufacturing technology and by identifying scrap early in the production chain. To enable these two approaches, it is imperative to quantify safety- and function-critical product features in critical manufacturing steps through appropriate measurement techniques. The overview in this paper on quality control in LIB production illustrates the necessity for improved inspection techniques with X-rays to realize a fast, online measurement of inner features in large-scale cell assembly with short cycle times and to visualize inner product-process interactions for the optimization in electrolyte filling. Therefore, two new inspection techniques are presented that contribute to overcoming the aforementioned challenges through the targeted use of X-rays. First, based on the results of previous experiments in which the X-ray beam directions were deliberately varied, a online coordinate measurement of anode-cathode (AC) overhang was developed using a line detector. Second, a new concept and the results of a continuous 2D visualization of the electrolyte filling process are presented, which can be used in the future to optimize this time-critical process step. By using a X-ray-permeable and portable vacuum chamber it is possible to quantify the influence of process parameters on the distribution of the electrolyte in the LIB.

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How to characterize a NDT method for weld inspection in battery cell manufacturing using deep learning

Cited by 12 publications

References 26 publications

Welding Challenges and Quality Assurance in Electric Vehicle Battery Pack Manufacturing

Welding Challenges and Quality Assurance in Electric Vehicle Battery Pack Manufacturing

Machine Learning in Lithium‐Ion Battery Cell Production: A Comprehensive Mapping Study

Applications and Development of X-ray Inspection Techniques in Battery Cell Production

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