arXiv subjects

Qiang Zou

Publications and source records attributed to Qiang Zou.

At least 37 records · Page 2Linked to original sources

arXiv2025

A Quasi-Optimal Shape Design Method for Lattice Structure Construction

Lattice structures, known for their superior mechanical properties, are widely used in industries such as aerospace, automotive, and biomedical. Their advantages primarily lie in the interconnected struts at the micro-scale. The robust construction of these struts is crucial for downstream design and manufacturing applications, as it provides a detailed shape description necessary for precise simulation and fabrication. However, constructing lattice structures presents significant challenges, particularly at nodes where multiple struts intersect. The complexity of these intersections can lead to robustness issues. To address this challenge, this paper presents an optimization-based approach that simplifies the construction of lattice structures by cutting struts and connecting them to optimized node shapes. By utilizing the recent Grey Wolf optimization method -- a type of meta-heuristic method -- for node shape design, the approach ensures robust model construction and optimal shape design. Its effectiveness has been validated through a series of case studies with increasing topological and geometric complexity.

cs.CE↗

arXiv2024

A review of geometric modeling methods in microstructure design and manufacturing

Microstructures, characterized by intricate structures at the microscopic scale, hold the promise of important disruptions in the field of mechanical engineering due to the superior mechanical properties they offer. One fundamental technique of microstructure design and manufacturing is geometric modeling, which generates the 3D computer models required to run high-level procedures such as simulation, optimization, and process planning. There is, however, a lack of comprehensive discussions on this body of knowledge. The goal of this paper is to compile existing microstructure modeling methods and clarify the challenges, progress, and limitations of current research. It also concludes with future research directions that may improve and/or complement current methods, such as compressive and generative microstructure representations. By doing so, the paper sheds light on what has already been made possible for microstructure modeling, what developments can be expected in the near future, and which topics remain problematic.

cs.CG↗

arXiv2024

Intelligent CAD 2.0

Integrating modern artificial intelligence (AI) techniques, particularly generative AI, holds the promise of revolutionizing computer-aided design (CAD) tools and the engineering design process. However, the direction of "AI+CAD" remains unclear: how will the current generation of intelligent CAD (ICAD) differ from its predecessor in the 1980s and 1990s, what strategic pathways should researchers and engineers pursue for its implementation, and what potential technical challenges might arise? As an attempt to address these questions, this paper investigates the transformative role of modern AI techniques in advancing CAD towards ICAD. It first analyzes the design process and reconsiders the roles AI techniques can assume in this process, highlighting how they can restructure the path humans, computers, and designs interact with each other. The primary conclusion is that ICAD systems should assume an intensional rather than extensional role in the design process. This offers insights into the evaluation of the previous generation of ICAD (ICAD 1.0) and outlines a prospective framework and trajectory for the next generation of ICAD (ICAD 2.0).

cs.HC↗

arXiv2024

Constraints Matrices and Convergence Proof of TPMS2STEP

TPMS is consistently described in the functional representation (F-rep) format, while modern CAD/CAM/CAE tools are built upon the boundary representation (B-rep) format. To solve this issue, translating TPMS to STEP is needed, called TPMS2STEP. This paper provides constraint matrices and convergence proof of TPMS2STEP so that $C^2$ continuity and an error bound of $2ε$ on the deviation can be ensured during the translation.

cs.CG↗

arXiv2024

SplineGen: a generative model for B-spline approximation of unorganized points

This paper presents a learning-based method to solve the traditional parameterization and knot placement problems in B-spline approximation. Different from conventional heuristic methods or recent AI-based methods, the proposed method does not assume ordered or fixed-size data points as input. There is also no need for manually setting the number of knots. It casts the parameterization and knot placement problems as a sequence-to-sequence translation problem, a generative process automatically determining the number of knots, their placement, parameter values, and their ordering. Once trained, SplineGen demonstrates a notable improvement over existing methods, with a one to two orders of magnitude increase in approximation accuracy on test data.

cs.CE↗

arXiv2024

3D-PreMise: Can Large Language Models Generate 3D Shapes with Sharp Features and Parametric Control?

Recent advancements in implicit 3D representations and generative models have markedly propelled the field of 3D object generation forward. However, it remains a significant challenge to accurately model geometries with defined sharp features under parametric controls, which is crucial in fields like industrial design and manufacturing. To bridge this gap, we introduce a framework that employs Large Language Models (LLMs) to generate text-driven 3D shapes, manipulating 3D software via program synthesis. We present 3D-PreMise, a dataset specifically tailored for 3D parametric modeling of industrial shapes, designed to explore state-of-the-art LLMs within our proposed pipeline. Our work reveals effective generation strategies and delves into the self-correction capabilities of LLMs using a visual interface. Our work highlights both the potential and limitations of LLMs in 3D parametric modeling for industrial applications.

cs.GR↗

arXiv2023

Visualizing symmetry-breaking electronic orders in epitaxial Kagome magnet FeSn films

Kagome lattice hosts a plethora of quantum states arising from the interplay of topology, spin-orbit coupling, and electron correlations. Here, we report symmetry-breaking electronic orders tunable by an applied magnetic field in a model Kagome magnet FeSn consisting of alternating stacks of two-dimensional Fe3Sn Kagome and Sn2 honeycomb layers. On the Fe3Sn layer terminated FeSn thin films epitaxially grown on SrTiO3(111) substrates, we observe trimerization of the Kagome lattice using scanning tunneling microscopy/spectroscopy, breaking its six-fold rotational symmetry while preserving the transitional symmetry. Such a trimerized Kagome lattice shows an energy-dependent contrast reversal in dI/dV maps, which is significantly enhanced by bound states induced by Sn vacancy defects. This trimerized Kagome lattice also exhibits stripe modulations that are energy-dependent and tunable by an applied in-plane magnetic field, indicating symmetry-breaking nematicity from the entangled magnetic and charge degrees of freedom in antiferromagnet FeSn.

cond-mat.mtrl-sci↗

arXiv2023

Tuning quantum paramagnetism and d-wave superconductivity in single-layer iron chalcogenides by chemical pressure

By substituting S into single-layer FeSe/SrTiO3, chemical pressure is applied to tune its paramagnetic state that is modeled as an incoherent superposition of spin-spiral states. The resulting electronic bands resemble an ordered checkerboard antiferromagnetic structure, consistent with angle-resolved photoemission spectroscopy measurements. Scanning tunneling spectroscopy reveals a gap evolving from U-shaped for FeSe to V-shaped for FeS with decreasing size, attributed to a d-wave superconducting state for which nodes emerge once the gap size is smaller than the effective spin-orbit coupling.

cond-mat.supr-con↗

arXiv2023

XVoxel-Based Parametric Design Optimization of Feature Models

Parametric optimization is an important product design technique, especially in the context of the modern parametric feature-based CAD paradigm. Realizing its full potential, however, requires a closed loop between CAD and CAE (i.e., CAD/CAE integration) with automatic design modifications and simulation updates. Conventionally the approach of model conversion is often employed to form the loop, but this way of working is hard to automate and requires manual inputs. As a result, the overall optimization process is too laborious to be acceptable. To address this issue, a new method for parametric optimization is introduced in this paper, based on a unified model representation scheme called eXtended Voxels (XVoxels). This scheme hybridizes feature models and voxel models into a new concept of semantic voxels, where the voxel part is responsible for FEM solving, and the semantic part is responsible for high-level information to capture both design and simulation intents. As such, it can establish a direct mapping between design models and analysis models, which in turn enables automatic updates on simulation results for design modifications, and vice versa -- effectively a closed loop between CAD and CAE. In addition, robust and efficient geometric algorithms for manipulating XVoxel models and efficient numerical methods (based on the recent finite cell method) for simulating XVoxel models are provided. The presented method has been validated by a series of case studies of increasing complexity to demonstrate its effectiveness. In particular, a computational efficiency improvement of up to 55.8 times the existing FCM method has been seen.

cs.CE↗

arXiv2023

Tomonaga Luttinger liquid in the topological edge channel of multilayer FeSe

A two dimensional topological insulator exhibits helical edge states topologically protected against single particle backscattering. Such protection breaks down, however, when electron electron interactions are significant or when edge reconstruction occurs, leading to suppressed density of states (DOS) at Fermi level that follows universal scaling with temperature and energy, characteristic of Tomonaga Luttinger liquid (TLL). Here, we grow multilayer FeSe on SrTiO3 by molecular beam epitaxy, and observe robust edge states at both {100}Se and {110}Se steps using scanning tunneling microscopy/spectroscopy. We determine the DOS follows a power law, resulting in Luttinger parameter K of 0.26 +/- 0.02 and 0.43 +/- 0.07 for {100}Se and {110}Se edges, respectively. The smaller K for the {100}Se edge also indicates strong correlations, attributed to ferromagnetic ordering likely present due to checkerboard antiferromagnetic fluctuations in FeSe. These results demonstrate TLL in FeSe helical edge channels, providing an exciting model system for novel topological excitations arising from superconductivity and interacting helical edge states.

cond-mat.mtrl-sci↗

arXiv2023

A survey of path planning and feedrate interpolation in computer numerical control

This paper presents a brief survey (in Chinese) on path planning and feedrate interpolation. Numerical control technology is widely employed in the modern manufacturing industry, and related research has been emphasized by academia and industry. The traditional process of numerical control technology is mainly composed of tool path planning and feedrate interpolation. To attain the machining of high speed and precision, several problems in tool path planning and feedrate interpolation are usually transformed into mathematical optimization models. To better undertake the research on the integrated design and optimization idea of tool path planning and feedrate interpolation, it is necessary to systematically review and drawn on the existing representative works. We will introduce the relevant methods and technical progress of tool path planning and feedrate interpolation in CNC machining successively, including tool path planning based on end milling, tool orientation optimization, G-code processing and corner transition, feedrate planning of parameter curves, and some new machining optimization methods proposed recently.

math.OC↗

arXiv2023

A note on solid modeling: history, state of the art, future

Solid modeling is a technique underlying CAD software as we see it today, and its theories and algorithms are among the most fundamental milestones in the historical development of CAD. Basically, it has answered the question of what geometric information a computer should store and how to store/manipulate them in order for the computer to aid the processes of design and manufacturing. This paper provides a brief review (in Chinese) on the historical development of solid modeling, its fundamental research problems, as well as their challenges and state of the art. It then concludes with three prospective trends of solid modeling, especially the promising paradigm shift from "Computer-Aided Design" to "Computer-Automated Design".

cs.GR↗

arXiv2023

Deciphering alloy composition in superconducting single-layer FeSe1-xSx on SrTiO3(001) substrates by machine learning of STM/S data

Scanning tunneling microscopy (STM) is a powerful technique for imaging atomic structure and inferring information on local elemental composition, chemical bonding, and electronic excitations. However, traditional methods of visual inspection can be challenging for such determination in multi-component alloys, particularly beyond the dilute limit due to chemical disorder and electronic inhomogeneity. One viable solution is to use machine learning to analyze STM data and identify patterns and correlations that may not be immediately apparent through visual inspection alone. Here, we apply this approach to determine the Se/S concentration in superconducting single-layer FeSe1-xSx alloy epitaxially grown on SrTiO3(100) substrate by molecular beam epitaxy. First, defect-related dI/dV tunneling spectra are identified by the K-means clustering method, followed by singular value decomposition to distinguish between those from S and Se. Such analysis provides an efficient and reliable determination of local elemental composition, and further reveals correlations of nanoscale chemical inhomogeneity to superconductivity in single-layer iron chalcogenide films.

cond-mat.supr-con↗

arXiv2023

Revealing intrinsic vortex-core states in Fe-based superconductors through machine-learning-driven discovery

Electronic states within superconducting vortices hold crucial information about paring mechanisms and topology. While scanning tunneling microscopy/spectroscopy(STM/S) can image the vortices, it is difficult to isolate the intrinsic electronic states from extrinsic effects like subsurface defects and disorders. We combine STM/S with unsupervised machine learning to develop a method for screening out the vortices pinned by embedded disorder in Fe-based superconductors. The approach provides an unbiased way to reveal intrinsic vortex-core states and may address puzzles on Majorana zero modes.

cond-mat.supr-con↗

arXiv2023

Realization of multiple charge density waves in NbTe2 at the monolayer limit

Abstract: Layered transition-metal dichalcogenides (TMDCs) down to the monolayer (ML) limit provide a fertile platform for exploring charge-density waves (CDWs). Though bulk NbTe2 is known to harbor a single axis 3*1 CDW coexisting with non-trivial quantum properties, the scenario in the ML limit is still experimentally unknown. In this study, we unveil the richness of the CDW phases in ML NbTe2, where not only the theoretically predicted 4*4 and 4*1 phases, but also two unexpected sqrt(28)*sqrt(28) and sqrt(19)*sqrt(19) phases, can be realized. For such a complex CDW system, we establish an exhaustive growth phase diagram via systematic efforts in the material synthesis and scanning tunneling microscope characterization. Moreover, we report that the energetically stable phase is the larger scale order (sqrt(19)*sqrt(19)), which is surprisingly in contradiction to the prior prediction (4*4). These findings are confirmed using two different kinetic pathways, i.e., direct growth at proper growth temperatures (T), and low-T growth followed by high-T annealing. Our results provide a comprehensive diagram of the "zoo" of CDW orders in ML 1T-NbTe2 for the first time and offer a new material platform for studying novel quantum phases in the 2D limit.

cond-mat.mtrl-sci↗

arXiv2022

Brief on tool path generation/optimization methods for multi-axis CNC machining

The quality of tool paths is a dominant factor in CNC machining, determining its efficiency and accuracy. This paper provides a brief review (in Chinese) on tool path planning methods reported in the literature, focusing on their categorization, motivating problems/issues, and historical development. Some promising research trends have also been stressed, especially for the paradigm shift from tool path generation towards tool path optimization.

cs.OH↗

arXiv2022

Deep Learning Assisted Optimization for 3D Reconstruction from Single 2D Line Drawings

In this paper, we revisit the long-standing problem of automatic reconstruction of 3D objects from single line drawings. Previous optimization-based methods can generate compact and accurate 3D models, but their success rates depend heavily on the ability to (i) identifying a sufficient set of true geometric constraints, and (ii) choosing a good initial value for the numerical optimization. In view of these challenges, we propose to train deep neural networks to detect pairwise relationships among geometric entities (i.e., edges) in the 3D object, and to predict initial depth value of the vertices. Our experiments on a large dataset of CAD models show that, by leveraging deep learning in a geometric constraint solving pipeline, the success rate of optimization-based 3D reconstruction can be significantly improved.

cs.CV↗

arXiv2022

A review on geometric constraint solving

This paper presents a comprehensive review of geometric constraint solving in parametric computer-aided design (CAD), with the major focus on its advances in the last 15 years. Geometric constraint solving can date back to the very first CAD prototype, Sketchpad, in the 1960s, but serious research studies were carried out only after parametric CAD was introduced in the late 1980s. In the following 30-year history of GCS research, two development stages may be identified: (1) the first 15 years (late 1980s - mid 2000s) were primarily devoted to geometric constraint decomposition for well-constrained systems or those with only structural constraint dependencies; and (2) the second 15 years (late 2000s - now) have seen research efforts shifted towards classification criteria and decomposition algorithms for general constraint systems (with and without non-structural constraint dependencies). Most existing reviews focused on the first 15 years. The problem researched in the second 15 years is, however, equally important, considering that a manually specified constraint system usually contains under- and over-constrained parts, and that such parts must be correctly detected and resolved before numerical solving can work. In this regard, this review paper covers both stages and will discusses what has already been made possible for handling general constraint systems, what developments can be expected in the near future, and which areas remain problematic.

cs.CG↗