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Technical Paper

Revolutionizing Mechanical Engineering: Harnessing the Power of Machine Learning and AI

2024-10-17
2024-28-0034
In the contemporary landscape, the convergence of Artificial Intelligence (AI) and Machine Learning (ML) technologies plays a pivotal role in reshaping the mechanical manufacturing sector. This work illuminates the multifaceted applications of AI and ML in mechanical engineering, specifically focusing on defect detection, quality inspection, and the advancement of workplace safety protocols. Beyond industrial realms, this integration extends into everyday life through the widespread adoption of AI-driven smart appliances like dishwashers and sweepers, symbolizing a harmonious fusion of technology and mechanical manufacturing. As AI and ML technologies permeate daily existence, their role goes beyond ensuring production precision; they significantly elevate job productivity and enhance workplace safety standards.
Technical Paper

Automatic mode identification for TBIW / Powertrain

2024-10-17
2024-28-0011
This paper addresses the critical task of global mode identification in the NVH domain, particularly focusing on the escalating complexity from subsystem to TBIW levels. Accurate identification of global modes for a full vehicle system demands substantial expertise and is integral to NVH post-processing. Our study introduces a novel tool/methodology developed by the IDIADA team for efficient Global/Local mode identification in subsystems or TBIW level models. Leveraging data extracted from .op2 files, including strain energy and displacement, the tool employs AI methodologies to generate easily interpretable graphs and pie charts. Compatible with major post processors like Hyper View/Meta post viewer, the Python-based tool operates efficiently via cloud technology, significantly reducing prediction time. The output not only predicts global mode numbers but also provides crucial insights into subsystem contributions, aiding in mode shape and continuity improvements.
Technical Paper

Enhancing BEV Energy Management: Neural Network-Based System Identification for Thermal Control Strategies

2024-07-02
2024-01-3005
Modeling thermal systems in Battery Electric Vehicles (BEVs) is crucial for enhancing energy efficiency through predictive control strategies, thereby extending vehicle range. A major obstacle in this modeling is the often limited availability of detailed system information. This research introduces a methodology using neural networks for system identification, a powerful technique capable of approximating the physical behavior of thermal systems with minimal data requirements. By employing black-box models, this approach supports the creation of optimization-based control strategies, such as Model Predictive Control (MPC) and Reinforcement Learning-based control (RL). The system identification process is executed using MATLAB Simulink, with virtual training data produced by a Simulink models to establish the method's feasibility. The neural networks utilized for system identification are implemented in MATLAB code.
Technical Paper

Making Modal Analysis Easy and More Reliable – Reference Points Identification by Experimental Prestudy

2024-06-12
2024-01-2931
Though modal analysis is a common tool to evaluate the dynamic properties of a structure, there are still many individual decisions to be made during the process which are often based on experience and make it difficult for occasional users to gain reliable and correct results. One of those experience-based choices is the correct number and placement of reference points. This decision is especially important, because it must be made right in the beginning of the process and a wrong choice is only noticeable by chance in the very end of the process. Picking the wrong reference points could result in incomplete modal analysis outcomes, as it might make certain modes undetectable, compounded by the user's lack of awareness about these missing modes. In the paper an innovative approach will be presented to choose the minimal number of mandatory reference points and their placement.
Technical Paper

Frequency-Based Substructuring for Virtual Prediction and Uncertainty Quantification of Thin-Walled Vehicle Seat Structures

2024-06-12
2024-01-2946
Dynamic substructuring enables the dynamic behavior analysis of intricate systems. In this context, the precise description of individual subsystem interfaces is crucial. Coupling components through virtual points is suitable, especially when it comes to experimental substructuring. The complex contact situations that arise from joint descriptions in thin-walled structures, like those found in vehicle seats, present a challenging task. This investigation aims to visualize the complex coupling of thin-walled structures by applying the virtual point transformation. Individual subsystems are analyzed through experiments and coupled using the Lagrange multiplier frequency-based substructuring to achieve this goal. For validation purposes, a completely assembled vehicle seat has been investigated. Identification of the connecting elements between the substructures is achieved using decoupling techniques.
Technical Paper

Generating Reduced-Order Image Data and Detecting Defect Map on Structural Components Using Ultrasonic Guided Wave Scan

2024-06-01
2024-26-0416
The paper presents a theoretical framework for the detection and first-level preliminary identification of potential defects on aero-structure components by employing ultrasonic-guided wave-based structural health monitoring strategies, systems and tools. In particular, we focus our study on ground inspection using a laser-Doppler scan of the surface velocity field, which can also be partly reconstructed or monitored using point sensors and actuators structurally integrated. Using direct wavefield data, we first question the detectability of potential defects of unknown location, size, and detailed features. Defects could be manufacturing defects or variations, which may be acceptable from a design and qualification standpoint; however, those may cause significant background signal artefacts in differentiating structure progressive damage or sudden failure like impact-induced damage and fracture.
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