Image-based Artificial Intelligence empowered surrogate model and shape morpher for real-time blank shape optimisation in the hot stamping process

• URL: http://arxiv.org/abs/2212.05885v1
• Date: Thu, 1 Dec 2022 20:17:48 GMT
• Title: Image-based Artificial Intelligence empowered surrogate model and shape morpher for real-time blank shape optimisation in the hot stamping process
• Authors: Haosu Zhou, and Nan Li
• Abstract summary: This research develops an image-based Artificial-intelligence-empowered surrogate modelling (IAISM) approach. The IAISM is trained to predict the full thinning field of the as-formed component given an arbitrary blank shape. As a high-accuracy and generalisable surrogate modelling and optimisation tool, the proposed pipeline is promising to be integrated into a full-chain digital twin.
• Score: 3.264571107058741
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: As the complexity of modern manufacturing technologies increases, traditional trial-and-error design, which requires iterative and expensive simulations, becomes unreliable and time-consuming. This difficulty is especially significant for the design of hot-stamped safety-critical components, such as ultra-high-strength-steel (UHSS) B-pillars. To reduce design costs and ensure manufacturability, scalar-based Artificial-Intelligence-empowered surrogate modelling (SAISM) has been investigated and implemented, which can allow real-time manufacturability-constrained structural design optimisation. However, SAISM suffers from low accuracy and generalisability, and usually requires a high volume of training samples. To solve this problem, an image-based Artificial-intelligence-empowered surrogate modelling (IAISM) approach is developed in this research, in combination with an auto-decoder-based blank shape generator. The IAISM, which is based on a Mask-Res-SE-U-Net architecture, is trained to predict the full thinning field of the as-formed component given an arbitrary blank shape. Excellent prediction performance of IAISM is achieved with only 256 training samples, which indicates the small-data learning nature of engineering AI tasks using structured data representations. The trained auto-decoder, trained Mask-Res-SE-U-Net, and Adam optimiser are integrated to conduct blank optimisation by modifying the latent vector. The optimiser can rapidly find blank shapes that satisfy manufacturability criteria. As a high-accuracy and generalisable surrogate modelling and optimisation tool, the proposed pipeline is promising to be integrated into a full-chain digital twin to conduct real-time, multi-objective design optimisation.

Related papers

• Solver-Independent Automated Problem Formulation via LLMs for High-Cost Simulation-Driven Design [31.21353236291861]
  APF is a framework for solver-independent, automated problem formulation via large language models.<n>It overcomes the difficulty of constructing suitable fine-tuning datasets in the absence of high-cost solver feedback.<n>APF significantly outperforms the existing methods in both the accuracy of requirement formalization and the quality of resulting radiation efficiency curves.
  arXiv  Detail & Related papers  (2025-12-21T10:40:36Z)
• When AI Bends Metal: AI-Assisted Optimization of Design Parameters in Sheet Metal Forming [0.4347560796121297]
  This paper presents an AI-assisted workflow that reduces expert involvement in parameter optimization through the use of Bayesian optimization.<n>A deep learning model provides an initial parameter estimate, from which the optimization cycle iteratively refines the design until a termination condition is met.<n>We demonstrate our approach, based on a sheet metal forming process, and show how it enables us to accelerate the exploration of the design space while reducing the need for expert involvement.
  arXiv  Detail & Related papers  (2025-11-27T10:31:24Z)
• AnaFlow: Agentic LLM-based Workflow for Reasoning-Driven Explainable and Sample-Efficient Analog Circuit Sizing [1.2617078020344616]
  A novel agentic AI framework for sample-efficient and explainable analog circuit sizing is presented.<n>The AnaFlow framework is demonstrated for two circuits of varying complexity and is able to complete the sizing task fully automatically.<n>The inherent explainability makes this a powerful tool for analog design space exploration and a new paradigm in analog EDA.
  arXiv  Detail & Related papers  (2025-11-05T18:24:01Z)
• A Multidisciplinary Design and Optimization (MDO) Agent Driven by Large Language Models [7.509742996975501]
  We present a Multidisciplinary Design and Optimization (MDO) Agent driven by Large Language Models (LLMs)<n>The agent semi-automates the end-to-end workflow by orchestrating three core capabilities: (i) natural-language-driven parametric modeling, (ii) retrieval-augmented generation (RAG) for knowledge-grounded conceptualization, and (iii) intelligent orchestration of engineering software for performance verification and optimization.
  arXiv  Detail & Related papers  (2025-10-06T01:26:55Z)
• Feedback-Driven Tool-Use Improvements in Large Language Models via Automated Build Environments [70.42705564227548]
  We propose an automated environment construction pipeline for large language models (LLMs)<n>This enables the creation of high-quality training environments that provide detailed and measurable feedback without relying on external tools.<n>We also introduce a verifiable reward mechanism that evaluates both the precision of tool use and the completeness of task execution.
  arXiv  Detail & Related papers  (2025-08-12T09:45:19Z)
• GENIAL: Generative Design Space Exploration via Network Inversion for Low Power Algorithmic Logic Units [1.5845117761091052]
  We introduce GENIAL, a machine learning-based framework for the automatic generation and optimization of arithmetic units.<n>We show that GENIAL is consistently more sample efficient than other methods, and converges faster towards optimized designs.<n>We also demonstrate the versatility of our approach by achieving significant improvements on Finite State Machines.
  arXiv  Detail & Related papers  (2025-07-25T06:34:59Z)
• AutoChemSchematic AI: A Closed-Loop, Physics-Aware Agentic Framework for Auto-Generating Chemical Process and Instrumentation Diagrams [2.5875933818780363]
  Current AI methods cannot auto-generate PFDs or PIDs, despite their critical role in scaling chemical processes.<n>We present a closed loop, aware physics framework for the automated generation of industrially viable PFDs and PIDs.
  arXiv  Detail & Related papers  (2025-05-30T13:32:00Z)
• Intelligent4DSE: Optimizing High-Level Synthesis Design Space Exploration with Graph Neural Networks and Large Language Models [3.8429489584622156]
  We propose CoGNNs-LLMEA, a framework that integrates a graph neural network with task-adaptive message passing and a large language model-enhanced evolutionary algorithm. As a predictive model, CoGNNs directly leverages intermediate representations generated from source code after compiler front-end processing, enabling prediction of quality of results (QoR) without invoking HLS tools. CoGNNs achieves state-of-the-art prediction accuracy in post-HLS QoR prediction, reducing mean prediction errors by 2.8$times$ for latency and 3.4$times$ for resource utilization compared to baseline models
  arXiv  Detail & Related papers  (2025-04-28T10:08:56Z)
• Accelerated Gradient-based Design Optimization Via Differentiable Physics-Informed Neural Operator: A Composites Autoclave Processing Case Study [0.0]
  We introduce a novel Physics-Informed DeepONet (PIDON) architecture to effectively model the nonlinear behavior of complex engineering systems. We demonstrate the effectiveness of this framework in the optimization of aerospace-grade composites curing processes achieving a 3x speedup. The proposed model has the potential to be used as a scalable and efficient optimization tool for broader applications in advanced engineering and digital twin systems.
  arXiv  Detail & Related papers  (2025-02-17T07:11:46Z)
• GraphCompNet: A Position-Aware Model for Predicting and Compensating Shape Deviations in 3D Printing [46.76421610124468]
  This paper introduces a data-driven algorithm for modeling and compensating shape deviations in additive manufacturing (AM) Recent advancements in machine learning (ML) have improved compensation precision, but issues remain in generalizing across complex geometries and adapting to position-dependent variations. We present a novel approach for powder bed fusion processes, using GraphCompNet, which is a computational framework combining graph-based neural networks with a generative adversarial network (GAN)-inspired training process.
  arXiv  Detail & Related papers  (2025-02-11T20:22:00Z)
• PROFIT: A Specialized Optimizer for Deep Fine Tuning [8.303565426823674]
  We present PROFIT, one of the first designed to incrementally fine-tune converged models on new tasks and/or datasets.<n> PROFIT takes the properties of a converged model into account explicitly to regularize the optimization process.<n>It outperforms fine-tuning methods in various tasks, from image classification to multimodal language model training to large-scale motion prediction.
  arXiv  Detail & Related papers  (2024-12-02T19:37:34Z)
• Automatically Learning Hybrid Digital Twins of Dynamical Systems [56.69628749813084]
  Digital Twins (DTs) simulate the states and temporal dynamics of real-world systems. DTs often struggle to generalize to unseen conditions in data-scarce settings. In this paper, we propose an evolutionary algorithm ($textbfHDTwinGen$) to autonomously propose, evaluate, and optimize HDTwins.
  arXiv  Detail & Related papers  (2024-10-31T07:28:22Z)
• Mechanistic Design and Scaling of Hybrid Architectures [114.3129802943915]
  We identify and test new hybrid architectures constructed from a variety of computational primitives. We experimentally validate the resulting architectures via an extensive compute-optimal and a new state-optimal scaling law analysis. We find MAD synthetics to correlate with compute-optimal perplexity, enabling accurate evaluation of new architectures.
  arXiv  Detail & Related papers  (2024-03-26T16:33:12Z)
• Machine Learning Insides OptVerse AI Solver: Design Principles and Applications [74.67495900436728]
  We present a comprehensive study on the integration of machine learning (ML) techniques into Huawei Cloud's OptVerse AI solver. We showcase our methods for generating complex SAT and MILP instances utilizing generative models that mirror multifaceted structures of real-world problem. We detail the incorporation of state-of-the-art parameter tuning algorithms which markedly elevate solver performance.
  arXiv  Detail & Related papers  (2024-01-11T15:02:15Z)
• End-to-End Meta-Bayesian Optimisation with Transformer Neural Processes [52.818579746354665]
  This paper proposes the first end-to-end differentiable meta-BO framework that generalises neural processes to learn acquisition functions via transformer architectures. We enable this end-to-end framework with reinforcement learning (RL) to tackle the lack of labelled acquisition data.
  arXiv  Detail & Related papers  (2023-05-25T10:58:46Z)
• An Adversarial Active Sampling-based Data Augmentation Framework for Manufacturable Chip Design [55.62660894625669]
  Lithography modeling is a crucial problem in chip design to ensure a chip design mask is manufacturable. Recent developments in machine learning have provided alternative solutions in replacing the time-consuming lithography simulations with deep neural networks. We propose a litho-aware data augmentation framework to resolve the dilemma of limited data and improve the machine learning model performance.
  arXiv  Detail & Related papers  (2022-10-27T20:53:39Z)
• FreeREA: Training-Free Evolution-based Architecture Search [17.202375422110553]
  FreeREA is a custom cell-based evolution NAS algorithm that exploits an optimised combination of training-free metrics to rank architectures. Our experiments, carried out on the common benchmarks NAS-Bench-101 and NATS-Bench, demonstrate that i) FreeREA is a fast, efficient, and effective search method for models automatic design.
  arXiv  Detail & Related papers  (2022-06-17T11:16:28Z)
• Surrogate Modelling for Injection Molding Processes using Machine Learning [0.23090185577016442]
  Injection molding is one of the most popular manufacturing methods for the modeling of complex plastic objects. We propose a baseline for a data processing pipeline that includes the extraction of data from Moldflow simulation projects. We evaluate machine learning models for fill time and deflection distribution prediction and provide baseline values of MSE and RMSE metrics.
  arXiv  Detail & Related papers  (2021-07-30T12:13:52Z)
• Enhanced data efficiency using deep neural networks and Gaussian processes for aerodynamic design optimization [0.0]
  Adjoint-based optimization methods are attractive for aerodynamic shape design. They can become prohibitively expensive when multiple optimization problems are being solved. We propose a machine learning enabled, surrogate-based framework that replaces the expensive adjoint solver.
  arXiv  Detail & Related papers  (2020-08-15T15:09:21Z)
• An Image Enhancing Pattern-based Sparsity for Real-time Inference on Mobile Devices [58.62801151916888]
  We introduce a new sparsity dimension, namely pattern-based sparsity that comprises pattern and connectivity sparsity, and becoming both highly accurate and hardware friendly. Our approach on the new pattern-based sparsity naturally fits into compiler optimization for highly efficient DNN execution on mobile platforms.
  arXiv  Detail & Related papers  (2020-01-20T16:17:36Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.