# Ensuring the consistency of information between two versions of a mechanical drawing

Alexandre Monnier Weil

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Alexandre Monnier Weil. Ensuring the consistency of information between two versions of a mechanical drawing. ACM/IEEE 27th International Conference on Model Driven Engineering Languages and Systems (MODELS 2024) Doctoral Symposium, Sep 2024, Linz (AUSTRIA), Austria. 10.1145/3652620.3688200. hal-04717933

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# Ensuring the consistency of information between two versions of a mechanical drawing

Alexandre Monnier Weil*

alexandre.monnier@univ-grenoble-alpes.fr

Kaizen Solutions

Montbonnot Saint Martin, France

## Abstract

Mechanical drawings are crucial in industry for manufacturing and information retrieval. Companies face a recurring issue with managing their hand-drawn drawings produced in the mid-20th century. To address this, the engineers convert hand-drawn drawings into CAD drawings. This transformation process must be error-free to avoid material waste and significant costs. To ensure this, quality assurance engineers carefully verify the final CAD drawings to ensure all information is preserved. This verification process is time-consuming and laborious, often requiring the dedicated effort of two engineers for an entire week per drawing.

We propose to automate this verification step to identify dissimilarities, defined as divergences causing inconsistencies between CAD and original hand-drawn versions. Identifying these dissimilarities will reduce the effort of the engineers to perform the necessary corrections.

In this article, we outline our planned approach to identify dissimilarities between mechanical drawings. Our approach is divided into two aspects: interpreting the drawings to extract information and comparing the drawings based on this information. We present an initial contribution by proposing a formalism to describe the information contained in mechanical drawings, distinguishing between pure geometric shape and annotations.

## CCS Concepts

* Software and its engineering → Model-driven software engineering; * Applied computing → Computer-aided design; * Computing methodologies → Computer vision problems.

## Keywords

Mechanical Drawing, Model-Driven Engineering, Graph Matching, Graph Neural Network, Interpretation, Comparison

## ACM Reference Format:

Alexandre Monnier Weil. 2024. Ensuring the consistency of information between two versions of a mechanical drawing. In *ACM/IEEE 27th International Conference on Model Driven Engineering Languages and Systems*

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MODELS Companion '24, September 22-27, 2024, Linz, Austria

© 2024 Copyright held by the owner/author(s). Publication rights licensed to ACM.

ACM ISBN 979-8-4007-0622-6/24/09

https://doi.org/10.1145/3652620.3688200

(MODELS Companion '24), September 22-27, 2024, Linz, Austria. ACM, New York, NY, USA, 6 pages. https://doi.org/10.1145/3652620.3688200

## 1 Problem

Mechanical drawings are extensively used in industry for various purposes, including manufacturing parts and information retrieval about components. Many companies face a recurring issue with managing hand-drawn mechanical drawings produced in the mid-20th century. To address this issue, engineers are transforming these historical drawings into 3D models of parts using Computer-Aided Design (CAD) software. These 3D models represent the geometric information of the parts, allowing engineers to produce different views with 2D drawings. They also include additional information through annotations, such as dimensions, surface roughness, and more. Examples of annotations are illustrated in Figure 2.

<figure><img src="image_2.png" /><figcaption>Figure 1: Mechanical drawings from our partner. Left: Hand-drawn. Right: CAD. Blurred for confidentiality.</figcaption></figure>

This process of transforming hand-drawn drawings into CAD drawings is critical because it must be error-free. Any mistake can propagate through the production process, resulting in material waste and significant costs. Quality assurance engineers carefully verify the final CAD drawings to ensure all information is preserved. This verification process is time-consuming and laborious, often requiring the dedicated effort of two engineers for an entire week per drawing. We propose to automate this verification step; specifically, we aim to identify dissimilarities between two drawings. We define a dissimilarity as a divergence of information that leads to inconsistencies in the CAD drawing with respect to the original hand-drawn version. Identifying these dissimilarities will reduce the effort of the engineers to perform the necessary corrections.

Figure 1 effectively illustrates an example of the transformation from hand-drawn drawings to CAD drawings, which was

<footnote>*Also with Univ. Grenoble Alpes, CNRS, Grenoble INP (Institute of Engineering Univ. Grenoble Alpes), LIG.</footnote>
performed without any errors. The CAD rendered blueprints include an isometric view of the 3D model used in the creation of the drawings.

<figure><img src="image_3.png" /><figcaption>Figure 2: Examples of annotation correspondence.</figcaption></figure>

By examining the drawings, we can notice several differences. The annotation conventions differ between the two versions, such as the representation of surface roughness, which is shown by a specific number of triangles on the hand-drawn drawing but by a numerical value on the CAD drawing, as seen in Figure 2. Additionally, the placement of annotations is different between the versions. For example, in Figure 2, the dimension values are not in the same location, and the arrowheads in the hand-drawn drawing point outward, while in the CAD drawing, they point inward.

However, in this example, the transformation process is regarded by engineers as having no errors. This shows that, to solve the problem, our focus should be on comparing higher-level information contained in the drawings rather than exclusively on geometric information. This complexity shows that an interpretation step of the drawings is necessary before considering comparison.

## 2 Expected contributions

The anticipated contributions of this research to practical applications, aimed at addressing the identified problem, are as follows:

* **Graph construction:** Definition of a formalism and algorithm to represent the information in drawings as a graph.

* **Drawing interpretation:** The drawing interpretation process is divided into two main parts. The first part, **generic segmentation**, involves developing a methodology for segmenting annotations and pure geometry (i.e., non-annotations). The second part, **incremental categorization of annotations**, involves a method for incrementally adding new examples to enhance annotation detection. This approach addresses evolving standards and the lack of strict conventions in hand-drawn drawings, ensuring the system can adapt to new requirements.

* **Interpretation-Based comparison:** Development of a method to compare mechanical drawings based on their interpretation. The objective of this method is to highlight dissimilarities to assist engineers to perform necessary corrections.

* **Integrated methodology validation:** Combine all the developed methodologies with image processing techniques to evaluate the overall validity of the approach. This comprehensive validation ensures that the integrated methodology

meets the required standards and is effective for practical applications, providing a robust solution for real-world use.

In this research project, we initially assume that the scanned hand-drawn drawings are algorithmically cleaned of all unwanted information, such as smudges, creases, and the noise often present in scans of hand-drawn mechanical drawings. To validate our initial contributions, we will focus on the interpretation and comparison of CAD mechanical drawings exclusively for our first three contributions. In the final phase, we will treat the validity of the approach by integrating hand-drawn drawings with cleaning algorithms.

## 3 Related work

Engineering drawings are commonly used across various fields such as oil and gas, electronics, and mechanical engineering. Each domain adheres to specific conventions and unique characteristics in their engineering drawings. Digitizing these drawings involves converting their raster images into structured data adapted for analysis. Generally, digitization is done in three steps: a pre-processing step to binarize and vectorise the images; an interpretation step that includes shape detection and categorization; and, finally, a contextualization step that is the goal of the digitization process, generally addressing industrial needs [16]. Existing approaches for data extraction from raster images can be categorized into two main techniques: traditional methods and modern techniques. Traditional techniques include rule-based and heuristic systems, as well as traditional machine learning approaches with preliminary feature extraction steps [16]. More recently, advancements in deep learning have shown promises in this field. However, utilizing deep learning approaches remains challenging due to the lack of comprehensive datasets [11].

The mechanical engineering domain is no exception in the evolution of digitization. The problem of digitizing these drawings has been addressed with traditional methods [7, 22], and recent advances have incorporated deep learning, specifically Convolutional Neural Network (CNN) [5] and Graph Neural Network (GNN) [23, 26]. However, these works are limited by the challenge of integrating new annotations without retraining deep learning models and the availability of only a few examples for certain annotation types and standards. Our research aims to develop a modular interpretation system that easily adapts to new annotation standards. We will then focus on comparing drawings to identify dissimilarities.

### 3.1 Vectorisation and graph construction

Various approaches can be employed to translate the raster image of drawing into geometric shapes. We consider the vectorisation methods used for mechanical drawing, illustrated in [23, 26].

These methods typically start with a skeletonization step of the mechanical drawing using thinning algorithms [3, 25]. Thinning reduces the lines in the drawing to a single-pixel width while preserving their geometric properties, thus creating a skeleton of the drawing. Next, a tracing algorithm is used to detect lines in the raster image. Following the tracing, an optional geometric refinement step is applied. This step simplifies the group of initial line by fitting them with more complex and richer geometric entities like Bezier curves. This refinement reduces the number of shapes to describe the drawing.
We distinguish vectorisation and graph construction as distinct steps. We will explore graph construction in more detail in section 4.1.

## 3.2 Segmentation using GNN

GNNs are used for numerous tasks where the input data can be represented as a graph, such as social network analysis [12], recommendation systems [24], molecular structure prediction [4], and even image segmentation [15].

Recent studies have adapted GNNs to segment and interpret mechanical drawings [23, 26]. However, training a GNN requires a significant amount of example data for each type of annotation. For certain annotation standards, this data is not easily accessible in a usable format. Additionally, retraining a GNN is costly, making it difficult to incrementally extend recognition using the knowledge of engineers. To address this issue, the authors propose integrating a GNN to segment the annotations from the rest of the drawing and then using heuristics to detect different annotations [26]. Building on their work, we propose to employ graph matching techniques to enhance annotation recognition. We discuss potential methods for graph matching in the next section.

## 3.3 Graph matching techniques

Graph matching involves associating one graph with another. There are various methods that can be classified into two categories: exact methods, where the graphs perfectly match, or approximate methods, that allow for a certain degree of tolerance [19]. We are particularly interested in the approximate methods as they are easily adaptable and do not require training.

Graph matching uses graph edit distance to quantify similarity between graphs by minimizing operations like insertions, deletions, and substitutions of nodes and edges [18], despite its NP-hard complexity. Linear-time approximations provide a balance between accuracy and efficiency [20, 21].

Another approach, we consider, is plan recognition, which has been applied to graph matching, particularly for recognizing UML diagrams [1]. This approach involves defining a set of editing rules to transform one graph into another. Similarly to graph edit distance, this method calculates a distance between two graphs based on the total cost of actions required for the transformation. The advantage of using this approach over traditional graph edit distance is that the transformation rules can be tailored to the specific problem, and it is also possible to track the sequence of actions that enabled the transformation. This tracking provides a more detailed understanding of how one graph is transformed into another, which can be particularly useful for explainability.

## 3.4 Comparison and Similarity

Several studies focus on the similarity of technical drawings, generally aimed at facilitating the querying of large databases of such drawings.

One approach proposes to use networks similar to Siamese networks to learn a similarity score between two mechanical drawings by analyzing their raster images [2]. However, this method does not localize differences and lacks control over whether the neural

network prioritizes geometric information or higher-level details in the drawing.

Another approach clusters mechanical drawings based on extracted features, transforming them into an N-dimensional space to identify similarities and group similar designs together [14]. This method does not enable precise comparison of information nor effectively highlights the differences between the drawings.

An alternative method for comparing and highlighting differences in mechanical drawings uses a graph matching technique [17]. However, this technique has only been tested within the context of Piping and Instrumentation Diagrams (P&IDs). Due to the significantly different nature of information representation between mechanical drawings and P&IDs, this method would need to be adapted for our specific use case.

## 4 Expected approach

Our approach is designed in three successive steps. Figure 3 illustrates this methodology, with the ultimate objective of identifying dissimilarities between drawings.

<figure><img src="image_4.png" /><figcaption>Figure 3: Methodology pipeline for the comparison of mechanical drawings.</figcaption></figure>

Firstly, we extract the geometric information and represent it through a graph using a vectorisation algorithm. This step focuses on capturing the raw geometric data.

Secondly, we distinguish pure geometric shapes from annotations. Although annotations are represented geometrically, they should be regarded as metadata. They use a different visual language to convey additional information and specific instructions, enriching the understanding of the mechanical drawings. The goal of this interpretation step is to differentiate between pure geometry and annotations. At the end of this step, we construct a new interpreted graph where groups of nodes are connected with hyperedges that represent annotations. An example of hyperedges is illustrated in Figure 6.

After interpreting the information, we can proceed to comparison to identify dissimilarities. Since the output of the interpretation is a graph, we consider graph matching algorithms for this comparison.

Throughout the entire process, the graphs are incrementally built in accordance with a metamodel we define to formalize the representation of information in the drawings. This metamodel is divided into two parts. Figures 3 and 4 show the geometric aspects and the information provided by the annotations, respectively.

### 4.1 Graph Construction

In the graph construction phase, we focus on the geometric part of the metamodel. As our data structure, we utilize graphs because
they are adapted for use with GNNs and graph matching techniques for the interpretation and comparison step of our approach. The construction of these graphs is in accordance with the defined metamodel illustrated in Figure 4.

<figure><img src="image_5.png" /><figcaption>Figure 4: Geometric metamodel.</figcaption></figure>

This metamodel shown in Figure 4 is based on two aspects: the first is the information detected through vectorisation, which identifies various shapes. The vectorisation algorithm is considered a parameter of our approach, meaning the detected shapes depend on the specific algorithm used. For example, simple algorithms may represent geometry using only segments, while more complex algorithms can detect and use additional shapes, such as circles or Bézier curves. The second aspect is the relationships between these shapes. We only include intersection relationships, as we believe they sufficiently represent the necessary information.

To construct our graph according to the metamodel, we proceed as follows: we apply a vectorisation algorithm to raster images, resulting in a list of detected shapes. We then identify intersection points between these shapes, treating each intersection point as vertices in the graph. The edges represent the references between elements. This algorithm operates with a complexity of $O(n^2)$. The resulting graph represents shapes and their spatial relationships as nodes and edges, illustrating their geometric connections.

## 4.2 Interpretation

The geometric graph constructed in the previous section contains only the geometric information of the drawings. Therefore, we will use GNNs to perform segmentation, separating pure geometry from annotations. Specifically, we will employ the methodology presented in [26], using GraphSAGE[6]. GraphSAGE is an inductive framework that generates node embeddings by sampling and aggregating features from local neighbourhoods, enabling scalable and generalizable graph representation learning. Our goal is to train the model to distinguish between annotations and non-annotations. To train the model, we will use a variety of datasets, including

automatically generated drawings and those created by industrial professionals.

The next step is to categorize each annotation using graph matching. Our approach involves determining a distance between two graphs, denoted as $d(G_1, G_2)$. To identify the most probable annotation $\hat{A}$, we will select the one with the minimal distance from the set of candidate graphs representing annotations $\mathcal{A}$. This reads:

$$ \hat{A} = \arg \min_{A \in \mathcal{A}} d(G, A) $$

To determine the most relevant distance, we plan to use methods, such as plan recognition or graph edit distance, both discussed in section 3.3.

We use the metamodel to formalize the representation of annotations, as shown in Figure 5, to represent the new information obtained through interpretation. Annotations are categorized into several abstract classes, which can be derived into concrete classes. This hierarchical structure allows for incremental extension when a new annotation is identified. Note that this part of the metamodel consists only of abstract classes and is not instantiable in its current state, but is designed to be extendable to new annotation types encountered.

<figure><img src="image_6.png" /><figcaption>Figure 5: Metamodel to represent annotations.</figcaption></figure>

Each annotation is linked to a group of shapes that geometrically describe it. In our graph, this information is represented through the construction of hyperedges to group nodes corresponding to these shapes. For example, in Figure 6, which represents a graph, the segments s1 and s2 and the intersection i1 are grouped into a hyperedge a1.

<figure><img src="image_7.png" /><figcaption>Figure 6: Example of a hyperedge grouping geometric nodes.</figcaption></figure>

To illustrate how our metamodel can be extended to concrete classes, we use the example of the abstract class Dimension, as shown on the left of Figure 7. From this abstract class, we derive specific dimensions, each with its unique meaning. Our metamodel for annotations is inspired by the use of software such as Auto-CAD and ISO standards classified under ICS codes 01.100.01 [8] and 01.100.20 [9], which respectively address technical drawings in general and mechanical drawings. The concrete classes for dimensions that we have developed are based on ISO 129 [10]. Examples of these dimensions and their associated concrete classes, conforming to our metamodel, are illustrated on the right of Figure 7.
<figure><img src="image_8.png" /><figcaption>**Figure 7:** Examples of dimensions and their associated classes conforming to the metamodel (left) and their extension with concrete dimensions (right).</figcaption></figure>

## 4.3 Comparison

The first sub-step in the comparison step involves matching the pure geometric nodes of the graphs while excluding annotations. For each node, the objective is to compute a function *f* that maps a node in the initial graph to a corresponding node in the target graph. To achieve this, we will embed each node into a representation within $\mathbb{R}^n$, as detailed in [17]. This approach transforms the nodes, allowing us to identify and match the closest nodes in this space.

The next step, with the geometric parts already partially matched, is to use plan recognition to define specific rules for transforming the graphs. We can define rules with associated costs to indicate when information has been modified to achieve a match with the target graph, and cost-free rules for differences that do not result in information inconsistencies. For example:

* **Cost-Free Rule:** If a dimension annotation is placed on the opposite side of a drawing but still represents the same measurement, we treat this as a non-critical difference with no associated cost.

* **Cost Rule:** If a dimension in the initial graph differs from the target graph due to a change in measurement or an added note, a cost is assigned to this modification to reflect the informational discrepancy.

Our approach for comparison is likely to evolve with further in-depth study of the state-of-the-art in graph matching techniques for comparison.

## 5 Evaluation Plan

The evaluation of our approach will be conducted in two stages. Initially, we will focus on CAD drawings, excluding hand-drawn drawings.

* **Interpretation (CAD-exclusive):** To evaluate our methods, we will create a dataset of labeled drawings (the process of creation is detailed in section 6). A portion of this dataset will be used to train the GNN, and we will then evaluate our approach by comparing it with existing methods [5, 23, 26] using metrics such as accuracy and recall.

* **Comparison (CAD-exclusive):** The next step is to test our method using a dataset of paired CAD drawings, where each pair consists of two different versions of the same drawing. A possible evaluation metric is the mean difference $\frac{1}{N}\sum(D_i - E_i)$, where $D_i$ and $E_i$ are the numbers of detected and expected dissimilarities for each pair $i$.

* **Integrated Methodology Validation:** To evaluate transferability to hand-drawn drawings, we will detect dissimilarities between CAD and hand-drawn versions, gathering feedback from engineers on missed and correct detections. This is necessary due to the lack of annotated data for CAD and hand-drawn drawing comparisons.

## 6 Current Status

To date, we have undertaken the construction of graphs to transform raster images into graph structures. Figure 8 shows a simple example of this process.

<figure><img src="image_9.png" /><figcaption>**Figure 8:** Example of graph construction from raster image of rectangle (left) to constructed graph (right).</figcaption></figure>

Additionally, we have collected drawings to build our dataset for testing and training our deep learning algorithm for the interpretation step. We have automated the generation of mechanical drawings annotated with dimensions. We obtain 3D models from the ABC dataset [13] and use AutoCAD software to automatically generate and annotate the drawings. So far, we have generated 10,000 drawings from the ABC dataset and collected an additional 150 from our partner. These industrial drawings will provide us with higher-quality data, and we plan to collect more. All of these drawings are in DXF format, a vector format developed by Autodesk. In DXF, elements are organized into separate layers and entities based on their semantics. This structure provides a reliable ground truth. To convert these drawings into labeled graphs, we first transform them into colorized images, assigning different colors to elements (e.g., red for annotations and blue for purely geometric shapes). These colorized images are then processed to generate the graph. This method is similar to that described in [26]. Figure 9 shows the Gantt chart outlining the timeline of our research project, including our current status and upcoming goals.

<figure><img src="image_10.png" /><figcaption>**Figure 9:** Gantt chart for the timeline of our research project.</figcaption></figure>
## Acknowledgments

Thanks to my PhD supervisors, Nicolas Hili and Yves Ledru. This work was funded by Kaizen Solutions - Company No. 799348255. Thanks to Dr. Aurèle Germain, Stéphanie Hallouin, Dr. Afaf Jaber, and Dr. Benjamin Tessore for their proofreading efforts. I would also like to thank Franck Corduant and Stéphanie Hallouin, the project managers from the company.

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