Developing smart multi-sensor monitoring for tool wear in stamping process. (November 2019)

• Record Type: Journal Article
• Authors: Shanbhag, V.V.; Pereira, M.P.; Voss, B.; Ubhayaratne, I.; Rolfe, B.F.
• Is Part Of: IOP conference series. Volume 651(2019)
• Publication Date: 2019-11
• Subjects: Tool wear -- Galling -- Smart monitoring -- Condition-based maintenance; Materials science -- Periodicals; 620.1105
• DOI: 10.1088/1757-899X/651/1/012085
• Languages: English
• ISSNs: 1757-8981

Abstract

Abstract: Tool wear and galling are of significant concern in the automotive stamping industry, due to the increase in use of higher strength sheet steels in automotive structures and reduced lubrication during stamping production. There are many methods explored in the literature and applied in industry to combat wear in stamping, including new die materials and coatings, alternative lubrication systems and better predictive models. However, smart condition monitoring will continue to be relevant in conjunction with these methods because it can provide further opportunities for production quality and cost improvements, despite the advancements of these other methods. This paper explores the use of multiple sensors and multiple signal processing techniques, aimed at developing a smart multi-sensor method to monitor galling wear. The three main sensors and corresponding signal processing techniques examined are: (i) measurement of punch force signatures analyzed via Principal Component Analysis (PCA); (ii) acoustic emissions signals measured via wideband sensors and examined using time and frequency domain features; (iii) measurement of audio signals in the audible frequency range analyzed via blind signal separation techniques. For all techniques, a semi-industrial stamping test was used to provide realistic production-type conditions, albeit with accelerated wear rates. The relationship between the key outputs from the three sensor/analysis methods were directly compared to a new quantitative measure of galling wear severity. Based on these results, it was observed that a multi-sensor approach for wear condition monitoring provides an opportunity for the development of a smart monitoring tool that can actively track the progression of wear.