Fixture layout is a complex task that significantly impacts manufacturing costs and requires the expertise of well-trained engineers. While most research approaches to automating the fixture layout process use optimization or rule-based frameworks, this paper presents a novel approach using supervised learning. The proposed framework replicates the 3-2-1 locating principle to layout fixtures for sheet metal designs. This principle ensures the correct fixing of an object by restricting its degrees of freedom. One main novelty of the proposed framework is the use of topographic maps generated from sheet metal design data as input for a convolutional neural network (CNN). These maps are created by projecting the geometry onto a plane and converting the Z coordinate into gray-scale pixel values. The framework is also novel in its ability to reuse knowledge about fixturing to lay out new workpieces and in its integration with a CAD environment as an add-in. The results of the hyperparameter-tuned CNN for regression show high accuracy and fast convergence, demonstrating the usability of the model for industrial applications. The framework was first tested using automotive b-pillar designs and was found to have high accuracy (approximate to 100%) in classifying these designs. The proposed framework offers a promising approach for automating the complex task of fixture layout in sheet metal design.

Introduction: Digitization is a crucial step towards achieving automation in production quality control for mechanical products. Engineering drawings are essential carriers of information for production, but their complexity poses a challenge for computer vision. To enable automated quality control, seamless data transfer between analog drawings and CAD/CAM software is necessary.

Methods: This paper focuses on autonomous text detection and recognition in engineering drawings. The methodology is divided into five stages. First, image processing techniques are used to classify and identify key elements in the drawing. The output is divided into three elements: information blocks and tables, feature control frames, and the rest of the image. For each element, an OCR pipeline is proposed. The last stage is output generation of the information in table format.

Results: The proposed tool, called eDOCr, achieved a precision and recall of 90% in detection, an F1-score of 94% in recognition, and a character error rate of 8%. The tool enables seamless integration between engineering drawings and quality control.

Discussion: Most OCR algorithms have limitations when applied to mechanical drawings due to their inherent complexity, including measurements, orientation, tolerances, and special symbols such as geometric dimensioning and tolerancing (GD&T). The eDOCr tool overcomes these limitations and provides a solution for automated quality control.

Conclusion: The eDOCr tool provides an effective solution for automated text detection and recognition in engineering drawings. The tool's success demonstrates that automated quality control for mechanical products can be achieved through digitization. The tool is shared with the research community through Github.

The digitalization of engineering drawings is a pivotal step toward automating and improving the efficiency of product design and manufacturing systems (PDMSs). This study presents eDOCr2, a framework that combines traditional OCR and image processing to extract structured information from mechanical drawings. It segments drawings into key elements-such as information blocks, dimensions, and feature control frames-achieving a text recall of 93.75% and a character error rate (CER) below 1% in a benchmark with drawings from different sources. To improve semantic understanding and reasoning, eDOCr2 integrates Vision Language models (Qwen2-VL-7B and GPT-4o) after segmentation to verify, filter, or retrieve information. This integration enables PDMS applications such as automated design validation, quality control, or manufacturing assessment. The code is available on Github.

This study emphasizes the importance of automatic synthetic data generation in data-driven applications, especially in the development of a 3D computer vision system for engineering contexts such as brownfield factory projects, where no data is readily available. Key points: (1) A successful integration of a synthetic data generator with the S3DIS dataset, leading to a significant enhancement in object detection of previous classes and enabling recognition of new ones; (2) A proposal for a CAD-based configurator for efficient and customizable scene reconstruction from LiDAR scanner point clouds.