AI-based Heat Line Generation for Autonomous Manufacturing of Curved Plates

다국어 초록 (Multilingual Abstract)

The line heating process in the curved plate forming process has traditionally faced challenges in standardization and automation due to its complex thermo-mechanical nature and reliance on the tacit knowledge of skilled experts. To overcome these limitations, this paper proposes a deep learning-based heat line generation algorithm to realize the automation of the curved plate forming process. The overall concept is to learn the relationship between curved hull forming information and heat line patterns using an image-to-image translation-based deep learning technique. In a pre-processing step, to adopt an image-to-image translation techniques with fluent features for efficient training, point cloud data of the curved plates is converted into image-typed multi-channel data, and heat line position information is converted to the 2-channel-based image-typed data based on density and angle. The proposed network architecture integrates the encoder-decoder structure of U-Net with the Atrous Spatial Pyramid Pooling (ASPP) module of DeepLabv3+ to effectively extract geometric features across multiple scales. Finally, the heat lines are reconstructed by first estimating the center positions of each heat line from the predicted density map and then extracting the corresponding heat-line trajectories using the angle map. Quantitative and qualitative evaluation results demonstrate that the proposed model predicts a similar shape and value of density map in x and y domains compared to the ground truth. Implementation in a real curved hull forming process using hot working on actual steel plates with the heat lines generated by the proposed algorithm shows that the degree of completion is significantly improved toward the target curvature, with the completion rate increasing from 61.71% to 69.29%. These results confirm that the proposed deep learning model is applicable to real-world manufacturing environments and can contribute to enhancing the production efficiency of the curved hull forming process.

국문 초록 (Abstract)

기존의 선상 가열 공정은 복잡한 열-기계적 특성과 작업자의 경험에 대한 높은 의존성으로 인해 공정의 표준화 및 자동화에 난항을 겪고 있다. 따라서, 본 연구는 이러한 한계를 극복하기 위해 곡가공 형성 과정의 자동화를 실현하기 위해 딥러닝 기반 가열선 생성 알고리즘을 제안한다. 전체적인 접근 방식은 곡면 형상 정보와 가열선의 패턴 간의 관계를 도출하기 위해 image-to-image 변환 기법을 사용하여 곡면의 형상 정보와 가열선의 패턴 사이 존재하는 관계를 학습하는 것이다.
전처리 단계에서 image-to-image 변환을 채택하고 풍부한 입출력 데이터를 정의하기 위해, 곡부재의 포인트 클라우드 데이터는 이미지 형상의 다중 채널 데이터로 변환되고, 가열선 정보는 밀도 및 각도 정보를 가지는 2채널 이미지 데이터로 정의된다. 제안된 네트워크는 3차원 형상 정보와 목표 형상 간의 기하학적 차이를 분석하기 위해 U-Net의 인코더-디코더 구조와 DeepLabv3+의 ASPP 모듈을 결합하여 설계된다. 마지막으로, 모델이 예측한 밀도 및 각도 정보로부터 각각의 가열선에 대해 예측한 중심점과 그에 대응하는 각도 정보를 통해 가열선이 재구성된다.
정량 및 정성 평가 결과는 제안된 모델이 x, y 도메인에서 정의된 밀도 정답 데이터와 유사한 분포를 예측함을 입증한다. 또한, 실제 강판을 이용한 열 가공 실험에서 제안된 알고리즘으로 생성된 가열선을 적용한 결과, 곡부재의 가공 완성도가 61.71%에서 69.29%로 유의미하게 향상됨을 보인다. 이러한 결과는 제안된 딥러닝 모델이 실제 제조 현장에 적용 가능하며, 곡가공 공정의 생산 효율성을 제고하는데 실질적으로 기여할 수 있음을 시사한다.

목차 (Table of Contents)

• Table of Content
• List of Figures.................................................................................................I
• List of Tables..................................................................................................III
• Abstract (English)..........................................................................................IV
• Chapter I: Introduction..................................................................................1
• I.1: Motivation..................................................................................................1
• I.2: Problem definition....................................................................................2
• I.3: Objective...................................................................................................4
• I.4: Contributions............................................................................................5
• Chapter II: Related Works: Hot Working Methods.....................................6
• II.1: Guideline and manual-based approaches............................................6
• II.2: Finite element method-based approaches .........................................7
• II.3: Existing artificial intelligence-based approaches...............................10
• II.4: Summary of related works.....................................................................13
• Chapter III: Deep Learning-based Heat Line Generation .........................15
• III.1: Data acquisition ......................................................................................16
• III.1.1: Data description...................................................................................16
• III.1.2: Data pre-processing...........................................................................17
• (a): Definition of features..............................................................................18
• (b): Definition of labels..................................................................................20
• III.2: Deep learning structure........................................................................24
• III.2.1: Concept of U-Net ...............................................................................25
• III.2.2: Concept of DeepLabv3+...................................................................27
• III.2.3: Proposed network architecture ......................................................29
• III.3: Heat line generation..............................................................................32
• III.3.1: Extraction of heat line properties: density and angle ...................33
• III.3.2: Heat line number prediction ............................................................34
• III.3.3: Angle-based final heat line reconstruction ...................................38
• Chapter IV: Validation ..................................................................................40
• IV.1: Model performance evaluation ..........................................................41
• (a): Quantitative evaluation .........................................................................41
• (b): Qualitative evaluation ...........................................................................43
• IV.2: Experimental evaluation .....................................................................48
• Chapter V: Conclusion .................................................................................50
• Reference .......................................................................................................51
• Abstract (Korea) ...........................................................................................55
• Acknowledgement........................................................................................57