Conference Agenda
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Agenda Overview |
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D352: AI-ASSISTED EVALUATION AND ESTIMATION IN ENGINEERING DESIGN
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Multi-agent generative AI for concept evaluation: consistency, knowledge integration and human alignment 1Offenburg University of Applied Sciences, Germany; 2Otto von Guericke University Magdeburg, Germany; 3Anhalt University of Applied Sciences, Germany; 4Texas A&M University, United States of America; 5Singapore University of Technology and Design, Singapore Early-stage concept evaluation is critical for selecting viable designs. This study introduces a multi-agent generative AI framework for assessing concepts across four configurations: AI with retrieval-augmented knowledge, AI without external knowledge, human experts, and a hybrid approach. The findings show that AI panels tend to produce uniform evaluation patterns, while retrieval-augmented knowledge alters rating behaviour without leading to closer alignment with human judgement. Hybrid setting achieved closest alignment, indicating AI is effective when combined with expert interpretation. The product singularity: universal AI framework for multimodal product understanding, evaluation, and benchmarking Indian Institute of Science, Bangalore, India Suboptimal product design and compliance failures lead to economic losses. While AI excels in domain-specific tasks like defect detection, existing solutions lack cross-domain reasoning and explainability. This paper presents Product Singularity, a universal AI framework that integrates multimodal data (images, text, etc) for comprehensive product evaluation across quality, safety, performance, ergonomics, and compliance. A proof-of-concept in consumer bottles validated by experts achieved 90% agreement and reduced evaluation time. Its modular design supports adaptation to other product categories. Automatic assessment of rust level on screws using convolutional neural networks 1Università Politecnica delle Marche, Italy; 2Polytech Marseille, France; 3Circular Momentum, Denmark This paper presents a deep learning-based approach to automatically classify the rust level of screws using ResNet-18 and MobileNetV3 convolutional neural networks. A controlled salt-spray chamber was used to simulate corrosion on metal screws over 0h, 48h, 96h, and 168h of exposure. Images were processed with a circle-detection algorithm to extract individual screws, followed by data augmentation and training. The final models achieved a classification accuracy greater than 94% on the validation set. Life cycle cost estimation in product-service systems: a review of machine learning methods Leibniz University Hannover, Germany Cost planning for Product-Service Systems faces rising complexity, making life-cycle cost estimates essential. This paper investigates how machine learning (ML) can be applied for life-cycle cost estimation in product development. A literature review was conducted to identify ML-based methods, classify them across life cycle phases, and compare them against traditional methods. Results show that traditional models remain transparent but limited in early stages, while ML methods achieve higher accuracy in data-rich phases. A clear research gap exists for hybrid models and end-of-life costing. | ||
