Browse Topic: Manufacturing
In Automobile manufacturing, maintaining the Quality of parts supplied by vendor is crucial & challenging. This paper introduces a digital tool designed to monitor trends for critical parameters of these parts in real-time. Utilizing Statistical Process Control (SPC) graphs, the tool continuously tracks Quality trend for critical parts and process parameters, predicting potential issues for proactive improvements even before parts are supplied. The tool integrates data from all Supplier partners across value chain into a single ecosystem, providing a comprehensive view of their performance and the parts they supply. Suppliers input data into a digital application, which is then analyzed in the cloud using SPC techniques to generate potential alerts for improvement. These alerts are automatically sent to both Suppliers and relevant personnel at the OEM, enabling proactive measures to address any Quality deviations. 100% data is visualized in an integrated dashboard which acts as a
To obtain real-time tire wear status during vehicle operation, this paper proposes a tire wear detection method based on signal analysis. Firstly, PVDF piezoelectric thin film sensors are pasted in the center of the airtight layer of tires with different degrees of wear to collect tire stress data under different working conditions. Secondly, filter and extract the time-domain and frequency-domain feature information of the collected data to construct a feature dataset. Finally, a deep regression model is established to train the feature dataset and achieve real-time detection of tire damage status. The results indicate that the prediction algorithm based on signal analysis and feature extraction achieves a maximum error of 0.3mm in tire wear detection, demonstrating high accuracy in tire wear detection. Providing tire information for safe driving of vehicles has high industrial application value.
Opening a tailgate can cause rain that has settled on its surfaces to run off onto the customer or into the rear loadspace, causing annoyance. Relatively small adjustments to tailgate seals and encapsulation can effectively mitigate these effects. However, these failure modes tend to be discovered relatively late in the design process as they, to date, need a representative physical system to test – including ensuring that any materials used on the surface flow paths elicit the same liquid flow behaviours (i.e. contact angles and velocity) as would be seen on the production vehicle surfaces. In this work we describe the development and validation of an early-stage simulation approach using a Smoothed Particle Hydrodynamics code (PreonLab). This includes its calibration against fundamental experiments to provide models for the flow of water over automotive surfaces and their subsequent application to a tailgate system simulation which includes fully detailed surrounding vehicle geometry
The initial powder used for the manufacturing of NdFeB permanent magnets is usually prepared through rapid cooling, either by melt spinning or strip casting. The powders produced by these two methods are suitable for different applications: while melt-spun powder is a good initial material for bonded and hot-deformed magnets, strip-cast powder is normally used for sintered magnets. To investigate the suitability of using strip-cast powder to manufacture hot-deformed magnets, NdFeB powder prepared by strip casting was hot pressed (without particle alignment) and compared with melt-spun powder prepared under the same conditions (700 °C, 45 MPa, 90 min). Although the processing parameters are the same (pressed in the same mold), the magnetic properties of the magnets made from the two powders are significantly different. Surprisingly, the magnet made from the strip-cast powder (after ball milling) shows comparable magnetic properties to those of isotropic magnets, with coercivity (HcJ) of
Image-based machine learning (ML) methods are increasingly transforming the field of materials science, offering powerful tools for automatic analysis of microstructures and failure mechanisms. This paper provides an overview of the latest advancements in ML techniques applied to materials microstructure and failure analysis, with a particular focus on the automatic detection of porosity and oxide defects and microstructure features such as dendritic arms and eutectic phase in aluminum casting. By leveraging image-based data, such as metallographic and fractographic images, ML models can identify patterns that are difficult to detect through conventional methods. The integration of convolutional neural networks (CNNs) and advanced image processing algorithms not only accelerates the analysis process but also improves accuracy by reducing subjectivity in interpretation. Key studies and applications are further reviewed to highlight the benefits, challenges, and future directions of
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