Failure Analysis
Assembly Line
Rolls-Royce Finds New-Engine Benefits in Old Test Data
The goal, according to Peter Wehle, head of innovation, research and testing at RRD, is to use this information to reduce new-engine weight and mass, while maintaining structural integrity.
Both parties are hopeful that using ML and AI will significantly reduce the number of sensors needed to obtain present and future data, thereby saving RRD millions of euros annually. According to Mahalingam, the software lets engineers choose the data they want from a data silo, select the algorithms they want to employ and decide whether or not they want to use a neural network to train an ML model.
Wehle notes that the disruptive tool is based on the interaction between a communication endpoint of the engine simulation and neighboring points. It carefully analyzes the effects of loads on physical structures.
Sensor-based leakage detection in vacuum bagging
A majority of aircraft components are nowadays manufactured using autoclave processing. Essential for the quality of the component is the realization of an airtight vacuum bag on top of the component to be cured. Several ways of leakage detection methods are actually used in industrial processes. They will be dealt with in this paper. A special focus is put on a new approach using flow meters for monitoring the air flow during evacuation and curing. This approach has been successfully validated in different trials, which are presented and discussed. The main benefit of the method is that in case of a leakage, a defined limit is exceeded by the volumetric flow rate whose magnitude can be directly correlated to the leakage’s size and position. In addition, the potential of this method for the localization of leakages has been investigated and is discussed.
Haschenburger, A., Menke, N. & Stüve, J. Sensor-based leakage detection in vacuum bagging. Int J Adv Manuf Technol (2021).
SEM-EDS Failure Analysis in Tire Manufacturing
Tires are often considered as low-tech commodities. However, contrary to popular perception, tires are actually highly engineered structural composites. Tires contain many rubber compounds (up to 20, with several types of microstructures) that provide different levels of grip and traction. Fillers are added to the main polymer matrix to facilitate rubber reinforcement.
Tire failures often occur due to a lower or decreased material quality, and an optimal and homogenous dispersion of all the different fillers is a key factor for a higher material quality. Analytical techniques like SEM-EDS are required to understand the root cause of a failure but the material contrast obtained from a backscattered electron image is not enough to distinguish between the large variety of materials employed.
This application note demonstrates that the live quantitative elemental analysis of Axia ChemiSEM provides an efficient and easy way to characterize the different fillers, despite their similar compositional contrast.
Application of AI to Oil Refineries and Petrochemical Plants
Artificial intelligent (AI), machine learning, data science, and other advanced technologies have been progressing remarkably, enabling computers to handle labor- and time-consuming tasks that used to be done manually. As big data have become available, it is expected that AI will automatically identify and solve problems in the manufacturing industry. This paper describes how AI can be used in oil refineries and petrochemical plants to solve issues regarding assets and quality.