Functional clustering methods for resistance spot welding process data in the automotive industry

Capezza, Christian; Centofanti, Fabio; Lepore, Antonio; Palumbo, Biagio

doi:10.1002/asmb.2648

Cited by 10 publications

(4 citation statements)

References 47 publications

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“…The DRC is considered the most important of these parameters to describe the quality of the RSW process. Further details on how the typical behaviour of a DRC is related to the physical and metallurgical development of a spot weld are provided by Capezza et al (2021b). The RSW process quality is directly affected by electrode wear since the increase in weld numbers leads to changed electrical, thermal and mechanical contact conditions at electrode and sheet interfaces (Manladan et al, 2017).…”

Section: Real-case Studymentioning

confidence: 99%

“…(Martín et al, 2014). Among on-line measurements of RSW process parameters, the socalled dynamic resistance curve (DRC) is recognized as the full technological signature of the metallurgical development of a spot weld (Dickinson et al, 1980;Capezza et al, 2021b) and, thus, can be used to characterize the quality of a finished sub-assembly. Figure 1 shows 100 DRCs corresponding to 10 different spot welds, measured in mΩ, that are acquired during the RSW process from the real-case study presented in Section 4.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Robust Multivariate Functional Control Charts

Capezza¹,

Centofanti²,

Lepore³

et al. 2022

Preprint

Self Cite

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Profile monitoring assesses the stability over time of one or multiple functional quality characteristics to quickly detect special causes of variation that act on a process. In modern Industry 4.0 applications, a huge amount of data is acquired during manufacturing processes that are often contaminated with anomalous observations in the form of both casewise and cellwise outliers. Because anomalous observations can seriously affect the monitoring performance, profile monitoring methods that are able to successfully deal with outliers are needed. To this aim, we propose a new framework, referred to as robust multivariate functional control charts (RoMFCC), that is able to monitor multivariate functional data while being robust to both functional casewise and cellwise outliers. The RoMFCC relies on four main elements: (I) a univariate filter to identify functional cellwise outliers to be replaced by missing components; (II) a robust functional data imputation method of missing values; (III) a casewise robust dimensionality reduction; (IV) a monitoring strategy for the multivariate functional quality characteristic. An extensive Monte Carlo simulation study is performed to quantify the monitoring performance of the RoMFCC by comparing it with some competing methods already available in the literature. Finally, a motivating real-case study is presented where the proposed framework is used to monitor a resistance spot welding process in the automotive industry.

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Section: Real-case Studymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Robust Multivariate Functional Control Charts

Capezza¹,

Centofanti²,

Lepore³

et al. 2022

Preprint

Self Cite

View full text Add to dashboard Cite

show abstract

“…Assuring the quality of this process is crucial to guarantee the solidity of the assembled vehicle. Several offline and online tests were proposed to evaluate the quality of the final welded workpieces [27][28][29].…”

Section: Quality Control Scenariomentioning

confidence: 99%

IoT Helper: A Lightweight and Extensible Framework for Fast-Prototyping IoT Architectures

et al. 2021

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Industry 4.0 is focused on the task of creating Smart Factories, which require the automation of traditional industrial processes and the fully connection and integration of different systems and devices. However, despite the wide availability of tools and technology, developing intelligent applications in the industry framework remains a complex and expensive task. This paper proposes a lightweight, extensible and scalable framework called IoT Helper to facilitate the adoption of IoT and IIoT solutions both in industry and domotics. The framework is designed to be highly flexible and declarative in nature, thus allowing for a wide range of configurations with minimal user efforts. To emphasize the practical applicability or our proposal, we present two real-life use cases where the framework was successfully adopted. We also investigate a crucial aspect of these applications, i.e., what level of scalability can be achieved with a lean generic framework based on inexpensive components such as ours. Comprehensive experimental results show the excellent cost-to-performance ratio of our solution. We consider this to be an important contribution because it paves the way for a more widespread adoption of IIoT-enabling technologies in industry.

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“…[4]). In the automotive industry, although destructive testing may be applied on samples, the quality of the process is monitored also by collecting and comparing process variables such as the clamping force, current, and voltage to dynamic thresholds or to fixed threshold values [10]. To this end, state-of-the-art systems [11] and approaches exist and aim, beyond implementing a non-destructive inspection [5] of the process, to integrate knowledge and quality assurance aspects into them by using technologies and frameworks from the industry 4.0 pool such as artificial intelligence, cyber-physical systems, and the internet of things [12].…”

Section: Introductionmentioning

confidence: 99%

Quality Assurance in Resistance Spot Welding: State of Practice, State of the Art, and Prospects

Stavropoulos,

Sabatakakis

2024

Metals

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Resistance spot welding is a process with high variability regarding the quality of the produced joints. This means that key performance indicators (KPIs) such as geometrical and mechanical features as well as failure modes can deviate from the initial design even if the same process parameters are used. The industry has developed quality assurance programs and quality control methods for tracking these KPIs; however, most of them are based on offline or/and destructive practices. On the other hand, state-of-the-art approaches have made online quality assessment feasible and proved its necessity if a 100% quality rate is required. However, limited attention has been given to “closing the loop” and providing feedback for preventing and correcting process anomalies that cause quality variations in real time. In this study, the main gaps between the state of practice and the state of the art are discussed in the context of quality assurance for resistance spot welding. Finally, the role and importance of digital twins by taking into consideration the entire welding ecosystem in quality assurance are discussed in order to form the prospects for the road ahead.

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Functional clustering methods for resistance spot welding process data in the automotive industry

Cited by 10 publications

References 47 publications

Robust Multivariate Functional Control Charts

Robust Multivariate Functional Control Charts

IoT Helper: A Lightweight and Extensible Framework for Fast-Prototyping IoT Architectures

Quality Assurance in Resistance Spot Welding: State of Practice, State of the Art, and Prospects

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