Method for generating post-processing processes for additive manufacturing based on part classification and feature mapping

Abstract

This invention relates to a method for generating post-processing procedures in additive manufacturing based on part classification and feature mapping. The method includes: classifying additively manufactured parts into four categories: shafts, gears, plates, and rings, and defining corresponding general basic processes and parameters for each category; performing refined analysis and standardized coding of the part's machining features to establish a part feature library; constructing a process parameter library within the same processing equipment, where features, processes, and parameters are mapped one-to-one; and building a post-processing procedure generation model based on the part feature library and the process parameter library. The model embeds part classification logic, feature coding, and a feature-process-parameter mapping data table. When used, the post-processing procedure generation model calls the mapping data table based on the part's machining features and outputs the post-processing procedures and corresponding equipment process parameters. This invention achieves standardized and intelligent generation of additive manufacturing post-processing procedures, significantly reducing reliance on the experience of professional personnel.

Description

Technical Field

[0001] This invention relates to the field of additive manufacturing post-processing technology, specifically to a method for generating additive manufacturing post-processing processes based on part classification and feature mapping. Background Technology

[0002] Currently, equipment mechanical parts manufactured by metal additive manufacturing and polymer additive manufacturing generally suffer from problems such as rough surface, residual support, internal powder residue, insufficient dimensional accuracy, and poor molding consistency. Post-processing is required to complete the final delivery of the parts.

[0003] Existing additive manufacturing post-processing processes suffer from the following technical shortcomings: First, current additive manufacturing post-processing relies entirely on engineers' experience, requiring manual judgment of part types and machining characteristics, and manual writing of process flows and parameters, demanding extremely high levels of expertise from operators. Second, equipment parts are diverse, including shafts, gears, plates, rings, and other structures. Different types of parts have different processing focuses, and traditional processes lack unified classification standards, resulting in chaotic processes and poor versatility. Third, subtle differences in part features have a significant impact on post-processing parameters. For example, hole size, internal and external threads, chamfer specifications, keyway dimensions, and surface roughness requirements all need to be matched with different machining operations. Fourth, the same processing equipment lacks a standardized parameter library for different feature parts, leading to high costs of manual trial and error, poor machining consistency, and low stability in mass production. Summary of the Invention

[0004] To address the aforementioned problems, this invention proposes a method for generating additive manufacturing post-processing processes based on part classification and feature mapping. The aim is to rapidly generate standardized post-processing flow charts and parameters, thereby improving batch processing consistency. The specific technical solution is as follows: A method for generating post-processing techniques in additive manufacturing based on part classification and feature mapping includes the following steps: S1. Parts Classification: Collect and statistically analyze the additively manufactured parts, classifying them into four major categories according to their structural form: shaft parts, gear parts, plate parts, and ring parts; and formulate common basic processes and parameters for post-processing for each major category of parts. S2. Part Feature Analysis and Feature Library Construction: Perform refined feature analysis on the classified parts to extract the processing features of the parts; standardize and encode all processing features to establish a part feature library; S3. Equipment process parameter library construction: Within the same processing equipment, set corresponding equipment process parameters for different processing steps; based on the single features and combined features in the part feature library, bind the corresponding post-processing steps and equipment process parameters so that different roughness, different hole diameters, different thread specifications, different chamfers, and different keyway sizes are matched with the corresponding post-processing steps and equipment process parameters respectively, and construct a process parameter library with one-to-one mapping of features, post-processing steps, and equipment process parameters. S4. Construct a post-processing generation model based on the part feature library and process parameter library. The model embeds part classification logic, feature encoding, and feature-process-parameter mapping data table. S5. The post-processing process generation model calls the mapping data table based on the machining characteristics of the part and outputs the post-processing procedures and corresponding equipment process parameters.

[0005] Furthermore, in S1, the basic processes for shaft parts include wire EDM to remove the substrate, rough turning, semi-finish turning, and finish turning; the basic processes for gear parts include wire EDM to remove the substrate, rough turning, semi-finish turning, and finish turning; the basic processes for plate parts include wire EDM to remove the substrate, rough milling, and finish milling; and the basic processes for ring parts include wire EDM to remove the substrate, rough turning, semi-finish turning, and finish turning.

[0006] Furthermore, in S2, the machining features include through holes, blind holes, open holes of different diameters, internal and external threaded holes, chamfers, fillets, keyways, surface roughness grades, wall thickness, stepped shaft diameters, and tooth profile parameters.

[0007] Furthermore, in S3, the equipment process parameters include spindle speed, feed rate, depth of cut, polishing pressure, and heat treatment time.

[0008] Furthermore, the post-processing generation model also includes a human-computer interaction module, a database editing module, and a qualified data feedback module.

[0009] Furthermore, the raw materials for additively manufactured parts are metal alloys, polymer resins, or nylon powder.

[0010] The beneficial effects of this invention are as follows: 1. Additive manufacturing parts are divided into four categories: shafts, gears, plates, and rings. General basic machining processes corresponding to each category are established, which solves the problems of messy parts and lack of process standards in the industry. At the same time, the micro-features such as holes, threads, chamfers, keyways, and roughness are refined and standardized in terms of feature analysis and coding, and a part feature library is built. The one-to-one mapping of features, processes, and process parameters is realized within the same processing equipment, which effectively avoids manual trial and error and significantly improves the consistency and stability of batch processing.

[0011] 2. The database editing module allows for the addition, deletion, or modification of part feature libraries and process parameter libraries. Combined with the qualified data feedback module, data can be imported back after the parts have passed processing, enabling bidirectional iterative optimization through manual editing and data feedback, thus giving the system continuous expansion and self-evolution capabilities. Attached Figure Description

[0012] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0013] Figure 1 This is a flowchart of the additive manufacturing post-processing generation method based on part classification and feature mapping described in this invention; Figure 2 This is a schematic diagram of the human-computer interaction module described in this invention. Detailed Implementation

[0014] The present invention provides the following specific implementation schemes: like Figure 1-2 As shown, this invention provides a method for generating additive manufacturing post-processing techniques based on part classification and feature mapping, comprising the following steps: S1. Part Classification: Collect and statistically analyze additively manufactured parts, classifying them into four main categories based on structural form and process attributes: shaft parts, gear parts, plate parts, and ring parts. For each major part category, summarize the corresponding general post-processing basic procedures, clarifying the process sequence, processing purpose, and applicable processing equipment for each category. Simultaneously, combining experimental data and industry processing standards, determine the basic process parameters for each procedure, including grinding feed rate, machining speed, polishing pressure, machining tolerances, and surface roughness requirements, forming a set of basic process parameters for each category, providing a general process benchmark for subsequent process matching.

[0015] S2. Part Feature Analysis and Feature Library Construction: Perform refined feature analysis on the classified parts, decompose the geometric structure, dimensional tolerances, surface quality, mechanical properties and special structural features, extract the processing features of the parts, standardize and encode each feature, and store them in a structured manner to establish a part feature library.

[0016] S3. Equipment Process Parameter Library Construction: Using basic processes of major categories as a framework, customized processing parameters corresponding to different features are associated. Within the same processing equipment, corresponding equipment process parameters are set for different processing processes. Based on single features and combined features in the part feature library, corresponding post-processing processes and equipment process parameters are bound, so that different roughness, different hole diameters, different thread specifications, different chamfers, and different keyway sizes are matched with corresponding post-processing processes and equipment process parameters. A hierarchical storage structure is used to construct a process parameter library that maps part features, major process categories, and equipment process parameters one by one.

[0017] S4. Based on the part feature library and process parameter library, a post-processing process generation model is constructed. The model includes a category selection module, which is used to automatically retrieve the corresponding basic process after the part category is manually selected; a process parameter mapping module, which can automatically load the general processing parameters of the category; a feature matching module, which can retrieve specific customized process parameters according to the special structure of the selected part; a process intelligent arrangement module, which can integrate general processes and feature parameters, automatically sort the process flow, and generate a complete post-processing process plan; and a data export module, which is used to export the process documents in a standardized manner and interface with the production management system and processing equipment.

[0018] S5. The post-processing process generation model calls the mapping data table based on the machining characteristics of the part and outputs the post-processing procedures and corresponding equipment process parameters.

[0019] Furthermore, in S1, the basic processes for shaft parts include wire EDM to remove the substrate, rough turning, semi-finish turning, and finish turning; the basic processes for gear parts include wire EDM to remove the substrate, rough turning, semi-finish turning, and finish turning; the basic processes for plate parts include wire EDM to remove the substrate, rough milling, and finish milling; and the basic processes for ring parts include wire EDM to remove the substrate, rough turning, semi-finish turning, and finish turning.

[0020] Furthermore, in S2, the machining features include through holes, blind holes, open holes of different diameters, internal and external threaded holes, chamfers, fillets, keyways, surface roughness grades, wall thickness, stepped shaft diameters, and tooth profile parameters.

[0021] Furthermore, in S3, the equipment process parameters include spindle speed, feed rate, depth of cut, polishing pressure, and heat treatment time.

[0022] Furthermore, the post-processing generation model also includes a human-computer interaction module, a database editing module, and a qualified data feedback module. The database editing module can add features, delete redundant features, modify the process parameters corresponding to existing features, and optimize mapping relationships. The qualified data feedback module can import the feature data and process parameters of the processed qualified parts back into the database.

[0023] Furthermore, the raw materials for additively manufactured parts are metal alloys, polymer resins, or nylon powder.

[0024] Taking the post-processing of metal additive manufacturing shaft parts as an example, it includes the following steps: Step 1: Categorize the printed metal shaft parts. The basic process is set as follows: wire EDM to remove the substrate → rough machining of the rotating surface and end face → semi-finish machining of the rotating surface and end face → finish machining of the rotating surface and end face. Step 2: Extract the features of shaft parts: surface roughness Ra1.6, 20mm diameter open hole, 45° chamfer, and 8mm flat keyway; Step 3: Retrieve parameter library: Surface roughness Ra1.6, basic process called to "finish turning", hole diameter 20mm matched with reaming speed 1200r / min, feed rate 0.15mm / r; 45° chamfer matched with cutting depth 0.3mm; keyway matched with special milling parameters; Step 4: Select the shaft type, Ra1.6, aperture φ20, 45° chamfer, and flat keyway through the human-computer interaction module; Step 5: Generate the post-processing model to generate the process sequence, equipment speed, pressure, and processing time. Export the process parameters with one click and use them directly in production.

[0025] This invention categorizes additive manufacturing parts into four main types: shafts, gears, plates, and rings, and establishes corresponding general basic machining processes for each category, solving the problems of disorganized parts and lack of standardized processes in the industry. Simultaneously, it performs refined feature analysis and standardized coding on microscopic features such as holes, threads, chamfers, keyways, and surface roughness, constructing a part feature library. Within the same processing equipment, it achieves a one-to-one mapping of features, processes, and process parameters, effectively avoiding manual trial and error and significantly improving the consistency and stability of batch processing. Furthermore, the database editing module allows for adding, deleting, or modifying parts feature libraries and process parameter libraries. Combined with a qualified data feedback module, data is imported back after parts have passed processing, enabling bidirectional iterative optimization through manual editing and data feedback, giving the system continuous expansion and self-evolution capabilities.

[0026] The foregoing has shown and described the basic principles, main features, and advantages of the present invention. Those skilled in the art should understand that the present invention is not limited to the above embodiments. The embodiments and descriptions in the specification are merely illustrative of the principles of the invention. Various changes and modifications can be made to the invention without departing from its spirit and scope, and all such changes and modifications fall within the scope of the claimed invention.

Claims

1. A method for generating post-processing techniques in additive manufacturing based on part classification and feature mapping, characterized in that, Includes the following steps: S1. Parts Classification: Collect and statistically analyze additively manufactured parts, classifying them into four major categories according to their structural form: shaft parts, gear parts, plate parts, and ring parts; and formulate general basic machining processes and basic process parameters for each category of parts. S2. Part Feature Analysis and Feature Library Construction: Perform refined feature analysis on the classified parts to extract the processing features of the parts; standardize and encode all processing features to establish a part feature library; S3. Equipment process parameter library construction: Within the same processing equipment, set corresponding equipment process parameters for different processing steps; based on the single features and combined features in the part feature library, bind the corresponding post-processing steps and equipment process parameters so that different roughness, different hole diameters, different thread specifications, different chamfers, and different keyway sizes are matched with the corresponding post-processing steps and equipment process parameters respectively, and construct a process parameter library with one-to-one mapping of features, post-processing steps, and equipment process parameters. S4. Construct a post-processing generation model based on the part feature library and process parameter library. The model embeds part classification logic, feature encoding, and feature-process-parameter mapping data table. S5. The post-processing process generation model calls the mapping data table based on the machining characteristics of the part and outputs the post-processing procedures and corresponding equipment process parameters.

2. The additive manufacturing post-processing generation method based on part classification and feature mapping according to claim 1, characterized in that: In S1, the basic machining processes for shaft parts include wire EDM to remove the substrate, rough turning, semi-finish turning, and finish turning; the basic machining processes for gear parts include wire EDM to remove the substrate, rough turning, semi-finish turning, and finish turning; the basic machining processes for plate parts include wire EDM to remove the substrate, rough milling, and finish milling; and the basic machining processes for ring parts include wire EDM to remove the substrate, rough turning, semi-finish turning, and finish turning.

3. The additive manufacturing post-processing generation method based on part classification and feature mapping according to claim 1, characterized in that: In S2, machining features include through holes, blind holes, open holes of different diameters, internal and external threaded holes, chamfers, fillets, keyways, surface roughness grades, wall thickness, stepped shaft diameters, and tooth profile parameters.

4. The additive manufacturing post-processing generation method based on part classification and feature mapping according to claim 1, characterized in that: In S3, the equipment process parameters include spindle speed, feed rate, depth of cut, polishing pressure, and heat treatment time.

5. The additive manufacturing post-processing generation method based on part classification and feature mapping according to claim 1, characterized in that: The post-processing model also includes a human-computer interaction module, a database editing module, and a qualified data feedback module.

6. The additive manufacturing post-processing generation method based on part classification and feature mapping according to claim 1, characterized in that: The raw materials for additive manufacturing parts are metal alloys, polymer resins, or nylon powder.

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