Metrological provisions for nonstandard production quality-control equipment

Abstract Battery technology is a key component in current electric vehicle applications and an important building block for upcoming smart grid technologies. The performance of batteries depends largely on quality control during their production process. Defects introduced in the production of electrodes can lead to degraded performance and, more importantly, to short circuits in final cells, which is highly safety-critical. In this paper, we propose an inspection system architecture that can detect defects, such as missing coating, agglomerates, and pinholes on coated electrodes. Our system is able to acquire valuable production quality control metrics, like surface roughness. By employing photometric stereo techniques, a shape from shading algorithm, our system surmounts difficulties that arise while optically inspecting the black to dark gray battery coating materials. We present in detail the acquisition concept of the proposed system architecture, and analyze its acquisition-, as well as, its surface reconstruction performance in experiments. We carry these out utilizing two different implementations that can operate at a production speed of up to 2000 mm/s at a resolution of 50 µm per pixel. In this work we aim to provide a system architecture that can provide a reliable contribution to ensuring optimal performance of produced battery cells.

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Using Impact Testing for Production Quality Control

Special Topics in Structural Dynamics & Experimental Techniques, Volume 5 - Conference Proceedings of the Society for Experimental Mechanics Series ◽

10.1007/978-3-030-47709-7_5 ◽

2020 ◽

pp. 49-57

Author(s):

Shawn Richardson ◽

Jason Tyler ◽

Randall Spears ◽

Mark Richardson

Keyword(s):

Quality Control ◽

Production Quality ◽

Impact Testing ◽

Production Quality Control

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Blockchain in manufacturing quality control: A computer simulation study

PLoS ONE ◽

10.1371/journal.pone.0247925 ◽

2021 ◽

Vol 16 (3) ◽

pp. e0247925

Author(s):

Pooi-Mun Wong ◽

Shreya R. K. Sinha ◽

Chee-Kong Chui

Keyword(s):

Quality Control ◽

Data Security ◽

Control Process ◽

Production Quality ◽

Consensus Algorithm ◽

Defect Tolerance ◽

Production Quality Control ◽

Peer Supervision ◽

Quality Control Process ◽

Quality Controller

Blockchain has been applied to quality control in manufacturing, but the problems of false defect detections and lack of data transparency remain. This paper proposes a framework, Blockchain Quality Controller (BCQC), to overcome these limitations while fortifying data security. BCQC utilizes blockchain and Internet-of-Things to form a peer-to-peer supervision network. This paper also proposes a consensus algorithm, Quality Defect Tolerance (QDT), to adopt blockchain for during-production quality control. Simulation results show that BCQC enhances data security and improves defect detections. Although the time taken for the quality control process increases with the number of nodes in blockchain, the application of QDT allows multiple inspections on a workpiece to be consolidated at a faster pace, effectively speeding up the entire quality control process. The BCQC and QDT can improve the quality of parts produced for mass personalization manufacturing.

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