Scientists Train Neural Network to Generate Process Plans from 3D Models
Researchers at the HSE FCS AI and Digital Science Institute have developed CAD2TechSpec, a framework that converts 3D models of mechanical parts into machining process plans—step-by-step instructions for machine tools. The solution aims to reduce the time required for the design and preparation of technical process documentation in mechanical engineering, aircraft manufacturing, and other high-tech industries. The study findings have been published in PeerJ Computer Science.
Today, many complex mechanical parts, such as turbine blades, are designed using specialised 3D modelling software. However, a 3D model cannot simply be sent directly to a machine tool. Engineers must manually develop a processing algorithm, taking into account the part’s geometry, the equipment's capabilities, the available tools, and applicable production standards. This process is time-consuming and requires extensive domain expertise.
The CAD2TechSpec framework addresses this challenge by taking a three-dimensional model as input and converting it into a structured description of the manufacturing process that can be understood by a CNC (Computer Numerical Control) machine. Such machines can produce parts with extremely complex geometries based on computer-generated instructions. First, the model is rendered from 28 different angles. The most informative views are then selected and combined into a multi-view image collage, which, together with a textual prompt, is fed into a multimodal language model capable of 'seeing' the images and 'understanding' the text.
The model then generates a machining plan for producing the part. To improve accuracy and reduce errors, the system uses RAG (Retrieval-Augmented Generation), which acts as a kind of reference guide for the neural network. Before generating a response, the system retrieves information from two sources: an ISO (International Organization for Standardization) database containing standards for equipment, tools, and manufacturing processes, and a library of validated solutions. If the neural network identifies a part similar to one it has encountered before, it can use an existing machining plan rather than generating one from scratch.
The system’s performance was evaluated according to three criteria: the correctness of the output file structure; the accuracy of the manufacturing process and its conformity with the geometry of the described part; and the relevance of the applied ISO standards. The experiments were conducted on 1,236 models from an open database, with the results evaluated by both automated systems and mechanical engineering experts. The best performance was achieved by the Mistral Large 3 model when used with the ISO knowledge base.
Maxim Minets
'Experiments have shown that the quality of the generated process plans depends not only on the model itself but also on the context provided to it. A knowledge base containing information on ISO standards and reference examples of parts significantly improves the quality of the generated specifications,' notes article co-author Maxim Minets, Research Assistant at the Laboratory of Methods for Big Data Analysis (LAMBDA) at HSE FCS.
The system is designed to serve as an intelligent assistant to manufacturing engineers, handling routine tasks involved in preparing preliminary process plans. Experts rated the generated plans at 86–89 points out of 100, while error analysis showed that the system’s performance could be further improved by providing the neural networks with additional training datasets.
Denis Derkach
'The solution can be applied in precision engineering, aircraft manufacturing, and other industries where preparing technical documentation accounts for a significant share of engineers’ working time. CAD2TechSpec is designed to accelerate routine tasks rather than fully automate decision-making. The final verification and approval of the process plan remain the responsibility of the human operator,' emphasises Denis Derkach, Head of the Laboratory of Methods for Big Data Analysis (LAMBDA) at HSE FCS.
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