Computer Aided Process Planning Research Articles

BrepMFR: Enhancing machining feature recognition in B-rep models through deep learning and domain adaptation

Feature Recognition (FR) plays a crucial role in modern digital manufacturing, serving as a key technology for integrating Computer-Aided Design (CAD), Computer-Aided Process Planning (CAPP) and Computer-Aided Manufacturing (CAM) systems. The emergence of deep learning methods in recent years offers a new approach to address challenges in recognizing highly intersecting features with complex geometric shapes. However, due to the high cost of labeling real CAD models, neural networks are usually trained on computer-synthesized datasets, resulting in noticeable performance degradation when applied to real-world CAD models. Therefore, we propose a novel deep learning network, BrepMFR, designed for Machining Feature Recognition (MFR) from Boundary Representation (B-rep) models. We transform the original B-rep model into a graph representation as network-friendly input, incorporating local geometric shape and global topological relationships. Leveraging a graph neural network based on Transformer architecture and graph attention mechanism, we extract the feature representation of high-level semantic information to achieve machining feature recognition. Additionally, employing a two-step training strategy under a transfer learning framework, we enhance BrepMFR's generalization ability by adapting synthetic training data to real CAD data. Furthermore, we establish a large-scale synthetic CAD model dataset inclusive of 24 typical machining features, showcasing diversity in geometry that closely mirrors real-world mechanical engineering scenarios. Extensive experiments across various datasets demonstrate that BrepMFR achieves state-of-the-art machining feature recognition accuracy and performs effectively on CAD models of real-world mechanical parts.

Adaptive recognition of machining features in sheet metal parts based on a graph class-incremental learning strategy

The integration of computer-aided design (CAD), computer-aided process planning (CAPP), and computer-aided manufacturing (CAM) systems is significantly enhanced by employing deep learning-based automatic feature recognition (AFR) methods. These methods outperform traditional, rule-based approaches, particularly in handling the complexities of intersecting features. However, existing deep learning-based AFR methods face two major challenges. The initial challenge stems from the frequent utilization of voxelized or point-cloud representations of CAD models, resulting in the unfortunate loss of valuable geometric and topological information inherent in original Boundary representation (B-Rep) models. The second challenge involves the limitation of supervised deep learning methods in identifying machining features that are not present in the predefined dataset. This constraint renders them suboptimal for the continually evolving datasets of real industrial scenarios. To address the first challenge, this study introduces a graph-structured language, Multidimensional Attributed Face-Edge Graph (maFEG), crafted to encapsulate the intricate geometric and topological details of CAD models. Furthermore, a graph neural network, Sheet-metalNet, is proposed for the efficient learning and interpretation of maFEGs. To tackle the second challenge, a three-component incremental learning strategy is proposed: an initial phase of pre-training and fine-tuning, a prototype sampling-based replay, and a stage employing knowledge distillation for parameter regularization. The effectiveness of Sheet-metalNet and its complementary incremental learning strategy is evaluated using the open-source MFCAD++ dataset and the newly created SMCAD dataset. Experimental results show that Sheet-metalNet surpasses state-of-the-art AFR methods in machining feature recognition accuracy. Moreover, Sheet-metalNet demonstrates adaptability to dynamic dataset changes, maintaining high performance when encountering newly introduced features, thanks to its innovative incremental learning strategy.

Computer Aided Process Planning for Sheet Metal Cutting Operations in the Manufacturing Industry

This paper describes computer-aided process planning for sheet metal cutting operations. The two designs to production stages that is highlighted are sheet metal processing and machining. There are four modules in this object's system. Virtual factory environments, feature-based designs, process planning, and process-based feature mapping. Feature-based design is utilized for the conception, modeling, and representation of the components for manufacturing applications. Whenever it involves sheet metal cutting operations in the manufacturing sector, computer-aided process planning, is extremely important for streamlining manufacturing procedures. The provides a general introduction to computer-aided process planning as it relates to sheet metal cutting, emphasizing its importance in boosting productivity, saving costs, and raising product quality. The main goal of computer-aided process planning for sheet metal cutting is to integrate computer technology, CAD/CAM systems, and sophisticated algorithms to automate and streamline the planning process. Based on design requirements, material characteristics, and production limitations, this method enables manufacturers to produce exact and ideal cutting plans. To create blanking and piercing holes, stamped or punched die are utilized in generative shape design; the generative computer-aided process planning system is created in C++ and used in various case studies presented in the present work. The application of computer-aided process planning for sheet metal cutting has several advantages, including greater output, less material waste, shortened lead times, and improved competitiveness in the industrial sector. The potential of computer-aided process planning to revolutionize sheet metal cutting operations, making them more efficient and cost-effective while ensuring high-quality final products is highlighted in the present research.

New classification of industrial robotic gripping systems for sustainable production

Robotics is an overarching trend in modern high-tech production, contributing significantly to automation. They are used in various industries to perform multiple tasks, and their number is constantly growing. Robots interact with the production object with the help of gripping systems, which are an essential component of industrial robots and manipulators designed for reliable grasping. Therefore, the process of design and rational selection of grippers for considering production conditions receives considerable attention worldwide. The article offers a comprehensive approach to the design of gripper systems as an integral element of the “gripping system – part – environment – production equipment” system to ensure further rational selection considering specific production conditions. A scientific approach to assessing the design of gripping systems was proposed to systematize knowledge in designing gripping systems. In the paper, the principal structural scheme of the robotic gripping system was developed, and the purpose of elements and design requirements were determined. Also, the sequence of stages in the process of selecting the elements of the gripping system has been proposed. The comprehensive system “gripping system – part – environment – production equipment” has been identified considering the mutual influence of structural elements. This work may be helpful to engineers and researchers while designing new gripping systems or selecting the most suitable one from the database. It can improve the rational selection of the element base and the structure of the gripping system by systematizing the experience in the gripper system design. Moreover, due to modern trends in automation and digitalization, the presented classification and coding system for gripping systems can be used in Computer Aided Process Planning and Computer Aided Gripping Systems Design systems. It can help to realize the approach “from the part geometry to the gripping systems design”. Also, it will ensure the production planning stage’s effectiveness due to reducing the time for robotic gripping systems’ design and increasing production safety, flexibility, autonomy, and performance.

Automated Fiber Placement Laminate Level Optimization for Mitigation of Through Thickness Defect Stacking

Manufacturing composite structures with Automated Fiber Placement (AFP) requires detailed process planning that is rigorous and time consuming. To facilitate, accelerate and perpetuate process planning knowledge, the Computer Aided Process Planning (CAPP) tool was developed. CAPP assists process planners in identifying optimal layup strategies for each ply of a laminate. This paper expands the established framework for analyzing defect stack-up through thickness of a laminate. Four different combinatorial optimization algorithms are implemented and evaluated: genetic algorithm, differential evolution, particle swarm, and greedy search. The algorithms identify optimal combinations of ply-level layup strategies by analyzing defect stacking using two objective functions. These approaches are evaluated through a digital case study performed on a complex tool surface. The result is a streamlined methodology for comparing different laminate-level manufacturing strategies and minimizing the through thickness defect stack-up.

AAGNet: A graph neural network towards multi-task machining feature recognition

Machining feature recognition (MFR) is an essential step in computer-aided process planning (CAPP) that infers manufacturing semantics from the geometric entities in CAD models. Traditional rule-based MFR methods struggle to handle intersecting features due to the complexity of representing their variable topological structures. This motivates the development of deep-learning-based methods, which can learn from data and overcome the limitations of rule-based methods. However, some existing deep learning methods compromise geometric and topological information when using certain representations such as voxel or point cloud. To address these challenges, we propose a novel graph neural network, named AAGNet, for automatic feature recognition using a geometric Attributed Adjacency Graph (gAAG) representation that preserves topological, geometric, and extended attributes from neutral boundary representation (B-Rep) models. Furthermore, some existing methods (such as UV-Net, Hierarchical CADNet) lack the capability of machining feature instance segmentation, which is a sub-task of feature recognition that requires the network to identify different machining features and the B-Rep face entities that constitute them, and it is a crucial task for subsequent process planning. AAGNet is designed as a multi-task network that can perform semantic segmentation, instance segmentation, and bottom face segmentation simultaneously for recognizing machining features, the faces associated with those features, and their bottom faces. The AAGNet is evaluated on various open-source datasets, including MFCAD, MFCAD++, and the newly introduced MFInstSeg dataset with over 60,000 STEP files and machining feature instance labels. The experimental results demonstrate that AAGNet outperforms other state-of-the-art methods in terms of accuracy and complexity, showing its potential as a flexible solution for MFR in CAPP.

Geometric feature extraction in manufacturing based on a knowledge graph

In times of global crises, the resilience of production chains is becoming increasingly important. If a supply chain is interrupted, a cost-effective solution must be established quickly. In the context of Industry 4.0, the concept of smart manufacturing offers a solution for fast and automated decision-making in production planning. The core idea of smart manufacturing is the digitalization of the product life cycle and the linking of individual phases of this cycle. Computer Aided Process Planning (CAPP) plays an important role as the connecting element between design and manufacturing. An important prerequisite for CAPP is the automated analysis of 3D models of components. The aim of this work is the development of an automatic feature recognition (AFR) -method to recognize geometric manufacturing features and their properties from 3D-models and then store them in a knowledge base. In that way, the result of the design can be automatically analysed and compared with manufacturing information afterwards in order to achieve an automated process planning. Geometric and topological information of a 3D model (STEP-AP242 format) generated by CAD systems is extracted by a Python-script developed and stored in an ontology-based knowledge base. The extracted product data is analysed using a Python-script to identify manufacturing features. To provide a comprehensive extensibility of the model, geometric features are defined according to a layered and hierarchical structure.

A hybrid framework for manufacturing feature recognition from CAD models of 3-axis milling parts

With the rapid development of computing technologies, the CAX software are widely used in mechanical engineering. The part model is designed in computer-aided design (CAD) software, the planning process is planned in computer-aided process planning (CAPP) software, and the machining process is simulated in computer-aided manufacturing (CAM) software. However, there are still digital gaps between design, process, and manufacturing, which affect the integration and efficiency of engineering information. Manufacturing features are considered digital carriers that can bridge the gap. The knowledge of design, process, and manufacturing can be integrated on manufacturing features to achieve the information flow across different engineering applications. However, the existing approaches cannot solve the feature recognition problem very well. The recognition criterion of these approaches do not have the generalization ability. And they also do not deal with the interacting features appropriately. To improve the performance of feature recognition of 3-axis milling parts, this paper proposed a hybrid feature recognition framework based on graph approach and rule approach. The hybrid framework solves the recognition problem of interacting features with the layered decomposition method. And the general wire criterion is proposed to improve the robustness. In addition, the general calculation method of adjacency attributes is also the original contribution. The final experimental results show that the proposed hybrid framework works well and can be applied to the actual engineering applications.

Different build strategies and computer-aided process planning for fabricating a functional component through hybrid-friction stir additive manufacturing

ABSTRACT Hybrid Additive Manufacturing (HAM) technologies leverage the strength of Additive Manufacturing (AM) and Subtractive Manufacturing (SM) in fabricating complex geometries with precision. Friction Stir Welding (FSW) has been acknowledged for joining sheets of similar or dissimilar materials with improved mechanical/metallurgical properties. However, due to the inherent pinhole defect and complicated toolpath planning, this process has not been explored much for fabricating the objects in a layer-by-layer manner. Therefore, this work investigates Computer Aided Process Planning (CAPP) for realizing fully functional metallic parts through a HAM system. This HAM system uses a Sheet Lamination (SL) process, i.e. Friction Stir Additive Manufacturing (FSAM), synergistically coupled with an SM process, i.e. machining. To realize complicated geometries through the proposed HAM system, three different build strategies are developed viz. Near-net block fabrication, Near-net shape fabrication via ‘form-then-bond approach’ and ‘bond-then-form approach.’ Each build strategy consists of a detailed CAPP that includes toolpath planning, tooling aspects, simulated outcome and so on. Further, these build strategies are discussed for realizing the parts with Functionally Graded Materials (FGM) & embedded structures. Finally, a monolithic object of aluminium has been fabricated as a case study to demonstrate one of the aforementioned build strategies.

Computer Aided Process Planning for Rough Machining Based on Machine Learning with Certainty Evaluation of Inferred Results

Process planning is well known as the key toward achieving highly efficient machining. However, it is difficult to standardize machining skills for process planning, which depend heavily on skilled operators. Hence, in a previous study, a computer aided process planning (CAPP) system using machine learning is developed to determine the operation parameters for finish machining of dies and molds. On the other hand, in rough machining, it is assumed that some machining operations are conducted sequentially using a respective tool according to the workpiece shape, which induces a much higher complexity in process planning. Therefore, in this study, machine learning is adopted to determine operation parameters for rough machining. The developed CAPP system converts the removal volume into a voxel model and infers a machining operation for each voxel. The inferred machining operation is visualized using different colors and identified corresponding to the voxel. Finally, the removal volume is classified using three different machining operations. However, machine learning is said to have a critical problem in that it is difficult to understand the reasons for the inferred results. Hence, it is necessary for the CAPP system to demonstrate the certainty level of the determined operation parameters. Thus, this study proposes a method for calculating the degree of certainty. If an artificial neural network is trained sufficiently, similar inferred results would always be obtained. Consequently, by using the Monte Carlo dropout to delete weights at random, the certainty level is defined as the variance of the inferred results. To verify the usefulness of the CAPP system, a case study is conducted by assuming rough machining of dies and molds. The results confirm that the machining operations are inferred with high accuracy, and the proposed method is effective for evaluating the certainty of the inferred results.

A Semi-Supervised Learning Framework for Machining Feature Recognition on Small Labeled Sample

Automated machining feature recognition is an essential component linking computer-aided design (CAD) and computer-aided process planning (CAPP). Deep learning (DL) has recently emerged as a promising method to improve machining feature recognition. However, training DL-based recognition models typically require annotating large amounts of data, which is time-consuming and labor-intensive for researchers. Additionally, DL models struggle to achieve satisfactory results when presented with small labeled datasets. Furthermore, existing DL-based approaches require significant memory and processing time, thus hindering their real-world application. To address these challenges, this paper presents a semi-supervised learning framework that leverages both labeled and unlabeled data to learn meaningful visual representations. Specifically, self-supervised learning is utilized to extract prior knowledge from a large dataset without annotations, which is then transferred to improve downstream feature recognition tasks. Furthermore, we apply lightweight network techniques to two established feature recognizers, FeatureNet and MsvNet, to develop reduced-memory, computationally efficient models termed FeatureNetLite and MsvNetLite, respectively. To validate the effectiveness of the proposed approaches, we conducted comparative studies on the FeatureNet dataset. With only one training sample per class, MsvNetLite outperformed MsvNet by about 19%, whereas FeatureNetLite outperformed FeatureNet by approximately 20% in machining feature classification. On a common X86 CPU, MsvNetLite gained 6.68× improvement in speed over MsvNet, and FeatureNetLite was 2.49× faster than FeatureNet. The proposed semi-supervised learning framework shows a significant improvement in machining feature recognition on small labeled data while achieving the optimal balance between recognition accuracy and inference speed compared to other DL-based approaches.

Automated manufacturability analysis and machining process selection using deep generative model and Siamese neural networks

Industry 4.0 calls for highly autonomous manufacturing process planning. Significant effort has been devoted over the years to generative Computer Aided Process Planning (CAPP), which aims to generate process plans for new designs without human intervention. This goal has not been realized to date due to several reasons, such as poor scalability and the difficulty in modeling manufacturing process capability, which encapsulates the part shape, quality, and material property transformation capabilities of the process. In our prior work, the shape transformation capabilities of lathe-based machining operations were modeled as latent probability distributions using a data-driven deep learning-based generative machine learning approach, from which visualizations of realistic machinable features could be sampled to assist manual process selection. In this paper, a Siamese Neural Network (SNN) is integrated with Autoencoder-based deep generative models of machining operations to enable automated comparison of the query part shapes with sampled outputs. This enables automated manufacturability analysis and machining process selection necessary for generative CAPP. The paper also demonstrates that the proposed Autoencoder and Siamese Neural Network (AE-SNN) achieves a class-average process selection accuracy of 89 %, and a manufacturability analysis accuracy of 100 %, which outperforms a discriminative model trained on the same dataset.

An exhaustive review on intelligent computer-aided process planning in context with various optimisation techniques (OPTE-2021-1994)

Computer-aided process planning (CAPP) is a major technology that aims to integrate computer-aided design (CAD) and computer-aided manufacturing (CAM). Nowadays most of the industries are keen to produce superior quality products with significant reduction in manufacturing time and production cost to meet the customer requirements. CAPP optimisation uses various techniques to serve its purpose and deliver the market ready product. Considerable research has been carried-out on automatic CAPP approaches by various researchers. In this paper an attempt has been made to present the contemporary review on CAPP system in context to the genetic algorithms (GAs), feature-based system (FBS), knowledge-based system (KBS), artificial neural networks (ANN) and STEP-complaint (STEP-C) technologies in the past two decades. The present review focuses on critical analysis and graphically represents the research trends of CAPP system over the years. The structure of the current work contains CAPP introduction, classification, optimisation approaches and conclusions along with recommendations to adopt CAPP system. The paper discusses the types of CAPP systems (generative and variant), computer-aided process planning methods/techniques and past two decades overview on CAPP optimisation techniques.

A Novel Approach of Operation Sequencing Problem in Computer Aided Process Planning

A Novel Approach of Operation Sequencing Problem in Computer Aided Process Planning

A hybrid learning framework for manufacturing feature recognition using graph neural networks

Manufacturing feature recognition is a critical issue in intelligent manufacturing, which can extract valuable geometric information from solid models for humans not to be subservient to machines and automation. Features can bridge the information gap between computer-aided design (CAD), computer-aided process planning (CAPP), computer-aided engineering (CAE), and computer-aided manufacturing (CAM). Based on the concept of feature, the whole process of digital seamless connection from design to manufacture can be implemented. However, the existing methods have not solved the feature recognition problem well. These methods have some limitations, such as lack of learning ability/low learning efficiency, lack of expandability, low accuracy, and so on. To improve the performance of the feature recognition, a hybrid learning framework based on Graph neural network (GNN) termed DeepFeature is proposed. First, a method that can extract features from CAD solid model is used. Then, a scheme for the construction and storage of feature datasets is developed. The feature samples in the dataset are sufficient, and the representation of each feature is different. Next, DeepFeature models are constructed, and the models are trained and driven by the samples in feature datasets. Finally, the features of the parts are classified based on rules and GNN models. The interacting features with multiple base planes are decomposed into several isolated features for feature classification. The experimental results show that the proposed hybrid learning framework not only has high feature recognition accuracy but also has good robustness in handling interacting features.

MBD-Based Machining Feature Recognition and Process Route Optimization

Machining feature recognition is considered the key connecting technique to the integration of Computer-Aided Design (CAD) and Computer-Aided Process Planning (CAPP), and decision-making of the part processing scheme and the optimization of process route can effectively improve the processing efficiency and reduce the cost of product machining cost. At present, for the recognition of machining features in CAD models, there is a lack of a systematic method to consider process information (such as tolerance and roughness) and an effective process route optimization method to plan part processing procedures. Here we represent a novel model processing feature recognition method, and, on the basis of feature processing plan decision, realize the optimization of the process route. On the basis of a building model Attributed Adjacency Graph (AAG) based on model geometry, topology, and process information, we propose an AAG decomposition and reconstruction method based on Decomposed Base Surface (DBS) and Joint Base Surface (JBS) as well as the recognition of model machining features through Attributed Adjacency Matrix-based (AAM) feature matching. The feature machining scheme decision method based on fuzzy comprehensive evaluation is adopted, and the decision is realized by calculating the comprehensive evaluation index. Finally, the Machining Element Directed Graph (MEDGraph) is established based on the constraint relationship between Machining Elements (MEs). The improved topological sorting algorithm lists the topological sequences of all MEs. The evaluation function is constructed with the processing cost or efficiency as the optimization objective to obtain the optimal process route. Our research provides a new method for model machining feature recognition and process route optimization. Applications of the proposed approach are provided to validate the method by case study.

Machining feature recognition based on a novel multi-task deep learning network

• Propose a novel learning-based machining feature recognition method that can effectively determine the faces that constitute machining features. • Construct a novel multi-task deep learning network based on point cloud data that can realize machining feature segmentation, machining feature identification, and bottom face identification simultaneously. • Propose a novel loss function that can be applied to the tasks of multi-label classification in deep learning. • Promote the application of deep learning in the manufacturing industry. Machining feature recognition is an essential task to realize the integration of computer-aided design (CAD), process planning (CAPP), and manufacturing (CAM) systems. In general, traditional rule-based machining feature recognition approaches are inflexible and computationally expensive. Moreover, it's not easy to design rules for intersecting machining features. To this end, various learning-based approaches were proposed in recent years. However, some of the existing learning-based approaches are complicated, time-consuming, and hard to deal with intersecting machining features, while some only approximately locate the position of the machining features rather than accurately segmenting them from the parts, which is not conducive to subsequent process planning. To address the above issues, a novel multi-task network named Associatively Segmenting and Identifying Network (ASIN) based on point cloud data is proposed for machining feature recognition. The proposed network performs three tasks: clusters the part faces with high similarities into machining features whose type are unidentified, predicts the semantic class (e.g. hole, pocket) for each face to identify the type of each machining feature, and identifies the bottom faces of the machining features. Through the three tasks, ASIN can realize machining feature segmentation, machining feature identification, and bottom face identification simultaneously. The final machining feature recognition results are obtained by combining the results of the above three tasks. After being trained with a large labeled point cloud dataset of 3D CAD models and a novel loss function, ASIN can be used to recognize machining features. Experimental results demonstrate that the proposed approach can effectively segment the machining features by determining the faces that compose them, and performs well in recognizing intersecting machining features.

Design of intelligent manufacturing system based on digital twin for smart shop floors

ABSTRACT The operation and management of smart shop floors depends on the integration and interaction of various information systems. In this paper, a framework of intelligent manufacturing system based on digital twin (DT-IMS) is designed for smart shop floors. Intelligent manufacturing system is composed of DT layer, process layer (Computer Aided Design and Computer Aided Process Planning), management layer, (Product Lifecycle Management), planning and scheduling layer (Enterprise Resource Planning and Manufacturing Execution System), perception layer (Supervisory Control And Data Acquisition), control layer (Programmable Logic Controller and Distributed Control System) and equipment layer. A digital twin model with high fidelity is established from the multidimensional dimension of geometry, physics, movement, behavior, rule, constraint and communication. Then, utilizing the historical data of each layer of information systems and the real-time data collected by industrial Internet of Things technology drive the digital twin model for simulation to realize the real-time function of process, production planning and scheduling and monitoring of the smart shop floor. The results show that the implementation of DT-IMS in an industrial company illustrates the potential advantages for process design, production planning and scheduling, monitoring and control with reduced complexity and uncertainty of the shop floor.

Bayesian optimization for process planning selections in automated fiber placement

Since the arrival of Automated Fiber Placement (AFP) in the 1980s and 1990s, the manufacturing technique has been progressively improving in quality and reliability of the process and resulting structures. The state-of-the-art of AFP is allowing for the manufacture of structures with increasing geometrical complexity. To adequately manufacture such structures, a large effort in process planning is required to reduce defect occurrence to the best extent possible. Many optimization methodologies have been established for AFP in terms of the design and process parameters. However, limited work has been accomplished in terms of process planning optimization. The work presented in the latter will develop a Bayesian Optimization (BO) technique for the optimization of process planning with the use of the Computer Aided Process Planning (CAPP) software and its connection with Vericut Composite Programming (VCP). A case study is performed on a doubly curved tool surface and the optimal solution is compared with as-manufactured results for validation. A predictive tool is also developed to rank layup strategies based on defect importance. Results show a substantial improvement in defect occurrence and allow a process planner to immediately see the performance of various layup strategies and starting point combinations.

Management Control and Integration Technology of Intelligent Production Line for Multi-Variety and Complex Aerospace Ring Forgings: A Review

Large and complex ring forgings are key structural parts of the aerospace field, and their quality is closely related to the reliability of aerospace vehicles. However, high-quality production of aerospace ring forgings faces many problems, such as the long process design cycle and impoverished consistency, the difficulties of real-time detection under the severe time-varying state of the deformation process, the complexity of high-quality non-destructive testing under multitudinous defects, and the cumbersome management control of the multi-source and multi-dimensional heterogeneous data. Considering the current situation of multi-variety and multi-batch production for aerospace ring forgings, establishing an intelligent production line is a crucial means to solving the above problems and realizing the standardization and premiumization of key aerospace components. Therefore, management control and integration technology of the intelligent production line play a crucial role. An analysis, including the research progress of the intelligent computer-aided process planning (CAPP) system, the real-time detection and control system, the product quality testing system, and the intelligent management control and integration system, is systematically reviewed in this work. Through intelligently managing and controlling the integrated systems of the production line, the production efficiency of ring forgings can be effectively improved, and the production energy consumption can be remarkably reduced, which is of great significance for enhancing the manufacturing technology level of aerospace products.