KR-102960254-B1 - System and method for generating process scheduling for remanufactured products based on 3D precise scanning and CAD drawings

KR102960254B1KR 102960254 B1KR102960254 B1KR 102960254B1KR-102960254-B1

Abstract

A 3D precision scan and CAD drawing-based remanufactured product machining process scheduling generation system according to an embodiment of the present invention comprises: a DWG data input unit that receives a CAD drawing (DWG) file uploaded by a user, parses shape entities, dimensions, tolerances, materials, and structural information by layer of a product, converts them into geometric objects, and normalizes them; a multi-sensor fusion unit that collects external shape data and internal defect data through at least two sensors among an optical scanner, a laser scanner, and an X-ray CT, generates an integrated 3D model by performing coordinate system alignment, noise removal, and data fusion, and automatically classifies the location, size, shape, and depth of defects; a data mapping unit that mutually maps the layer, tolerance, and machining attribute data of the CAD drawing with the multi-sensor fusion data to automatically identify a restoration target area, aligns coordinate systems, and generates an integrated data model; and an AI machining path optimization unit that automatically generates a machining path (G-code) for CNC and 3D printing using the integrated data model as input, and calculates an optimized path reflecting tool collision avoidance, material consumption reduction, and equipment load balance using a machine learning-based optimization algorithm. It includes: a work order creation unit that automatically creates a work order according to a standard industrial format based on the above-mentioned optimized processing path, including a work sequence, equipment allocation, quality inspection guidelines, and material requirements; a digital twin simulation unit that simulates a process in a digital twin environment based on the above-mentioned work order to verify tool collisions, processing time, and material consumption, calculates quality grades, success probabilities, and risk indices in conjunction with a quality prediction model, and automatically modifies the processing path in the event of an anomaly; and a communication interface unit that automatically reflects the above-mentioned work order and prediction results in production planning, material management, and quality management by bidirectionally linking with external production management systems such as ERP, MES, and WMS.

Inventors

김형민
임자영
이영수

Assignees

주식회사 에이치랩

Dates

Publication Date: 20260507
Application Date: 20250911

Claims (10)

A DWG data input unit that receives a CAD drawing file uploaded by a user, parses product shape entities, dimensions, tolerances, materials, and layer-specific structural information, converts them into geometric objects, and normalizes them; A multi-sensor fusion unit that collects external shape data and internal defect data through at least two sensors among an optical scanner, a laser scanner, and an X-ray CT, generates an integrated 3D model by performing coordinate system alignment, noise removal, and data fusion, and automatically classifies the location, size, shape, and depth of defects; A data mapping unit that mutually maps the layer, tolerance, and machining attribute data of the above CAD drawing with the above integrated 3D model to automatically identify a restoration target area and generates an integrated data model including coordinate and shape information of the restoration target area; An AI machining path optimization unit that automatically generates machining paths (G-code) for CNC and 3D printing based on the above integrated data model, and calculates an optimized machining path reflecting tool collision avoidance, material consumption reduction, and equipment load balance using a machine learning-based optimization algorithm; A work order creation unit that automatically creates a work order according to a standard industrial format based on an optimized processing path, including a work sequence, equipment allocation, quality inspection guidelines, and material requirements; A digital twin simulation unit that simulates a process in a digital twin environment based on the above work order to verify tool collision, machining time, and material consumption, calculates quality grade, success probability, and risk index in conjunction with a quality prediction model, and automatically modifies the machining path in the event of an anomaly; and A communication interface unit comprising a bidirectional linkage of at least one external production management system among ERP, MES, and WMS to automatically reflect the above work order and forecast results in production planning, material management, and quality management. 3D precision scan and CAD drawing-based remanufactured product processing schedule generation system.
In paragraph 1, The above multi-sensor fusion unit Characterized by performing high-precision coordinate system alignment by applying the ICP (Iterative Closest Point) algorithm and a deep learning-based point matching technique in parallel to the collected data, 3D precision scan and CAD drawing-based remanufactured product processing schedule generation system.
In paragraph 1, The above multi-sensor fusion unit Characterized by further including an automatic defect characteristic classification unit that automatically classifies the location, size, shape, and depth of defects and assigns defect IDs from an integrated 3D model generated by fusing collected surface data and internal defect data. 3D precision scan and CAD drawing-based remanufactured product processing schedule generation system.
In paragraph 1, The above AI processing path optimization unit is characterized by collecting real-time sensor feedback for each piece of equipment, performing path recalculation reflecting errors occurring during processing, and learning the data to continuously update the optimization profile for each piece of equipment. 3D precision scan and CAD drawing-based remanufactured product processing schedule generation system.
In paragraph 1, The above digital twin simulation unit is characterized by simulating a machining path in a virtual environment, simultaneously evaluating the possibility of tool collision, estimated machining time, material consumption, and quality grade, and automatically correcting the machining path when an anomaly is detected. 3D precision scan and CAD drawing-based remanufactured product processing schedule generation system.
In paragraph 5 The above digital twin simulation unit is characterized by further including a machine learning-based quality prediction model that simultaneously calculates the remanufacturing success probability, quality grades (A~D), and risk index. 3D precision scan and CAD drawing-based remanufactured product processing schedule generation system.
In paragraph 1, The above work order is Characterized by including processing path data (G-code), work sequence and process flow, estimated processing time and production schedule, equipment allocation information, quality inspection guidelines and material information, 3D precision scan and CAD drawing-based remanufactured product processing schedule generation system.
In paragraph 1, The above work order creation department Characterized by dynamically adjusting work sequences and equipment allocations by reflecting equipment availability, urgency, and material inventory status in real time in conjunction with the aforementioned external production management system. 3D precision scan and CAD drawing-based remanufactured product processing schedule generation system.
In paragraph 1, The above digital twin simulation unit is characterized by including a virtual reality-based verification function that provides a process simulation screen to an operator through AR glasses or a VR device and intuitively displays risk factors that may occur during processing based on the simulation results. 3D precision scan and CAD drawing-based remanufactured product processing schedule generation system.
A step of receiving a CAD drawing file uploaded by a user in a DWG data input section, parsing the product's shape entities, dimensions, tolerances, materials, and layer-specific structural information, converting them into geometric objects, and normalizing them; A step of collecting external shape data and internal defect data of a product using at least two of an optical scanner, a laser scanner, and an X-ray CT device in a multi-sensor fusion unit; A step of generating an integrated 3D model by performing coordinate system alignment, noise removal, and data normalization on the collected data, and automatically classifying the location, size, shape, and depth of defects from the integrated 3D model; A step of automatically identifying a restoration target area by mapping CAD drawing data with the integrated 3D model, and generating an integrated data model including coordinate and shape information of the restoration target area; A step of automatically generating a machining path (G-code) for CNC machining and 3D printing using the above integrated data model as input, and calculating an optimized path that reflects tool collision avoidance, reduction of machining time, reduction of material consumption, and equipment load balancing using a machine learning-based algorithm; A step of automatically creating a work order including a work sequence, equipment allocation information, quality inspection guidelines, and material requirements based on the above-mentioned optimized processing path; A step of simulating a process in a virtual environment based on the above work order to verify tool collision, machining time, and material consumption, calculating an expected quality grade, success probability, and risk index in conjunction with a quality prediction model, and automatically modifying the above machining path if an anomaly is detected; A method comprising the step of linking confirmed work orders and forecast results with at least one external production management system among ERP, MES, and WMS. Method for generating remanufactured product processing schedules based on 3D precision scanning and CAD drawings.

Description

System and method for generating process scheduling for remanufactured products based on 3D precise scanning and CAD drawings The present invention relates to a system and method for generating a remanufactured product processing process scheduling based on 3D precision scanning and CAD drawings. Across all industries, remanufacturing technology is becoming increasingly important from environmental and economic perspectives. In particular, for high-value-added components in sectors such as automobiles, aviation, heavy industry, and semiconductor equipment, there are active efforts to conserve resources and reduce costs by remanufacturing old or damaged parts instead of discarding them. However, the following technical limitations exist in ensuring the efficiency and quality of the remanufacturing process. Existing remanufacturing processes rely primarily on experience-based operator judgment and partial automation. For example, methods are used where operators perform visual inspections or utilize limited 2D measuring tools to identify wear or damage to parts. However, these methods entail the following problems. For parts with complex three-dimensional shapes, it is difficult to identify the exact location and shape of defects through visual inspection or simple measurement. Manual inspection and restoration design processes are time-consuming and lead to reduced productivity in mass or multi-product, small-batch production systems. Quality variations may occur depending on the skill level of the workers, and standardization is difficult. Recently, with the advancement of 3D precision scanning technology, methods to digitize damaged parts and obtain shape data are being introduced. Using a 3D scanner allows for high-precision measurement of a part's external shape and the acquisition of point cloud data, enabling more precise analysis than traditional visual inspection. However, most currently commercialized solutions have the following limitations. First, the complexity of data post-processing The process of identifying abnormal areas by comparing scan data with CAD drawings is carried out manually or semi-automatically, so expert intervention is still required. Second, the coordinate system mismatch problem Additional work is required during the process of aligning the coordinate systems of scan data and CAD drawings, and the level of automation is low. Third, the inefficiency of generating restoration paths Even if defects are identified, there is a lack of fully automated solutions that automatically generate paths (G-code) for processing equipment such as CNC milling and 3D printing, and link them with ERP/MES systems. Next-generation solutions required in the remanufacturing industry must meet the following conditions. It is necessary to automatically identify abnormal areas based on scan data and CAD drawings, to precisely automatically align CAD coordinate systems and scan coordinate systems, to automatically generate and optimize CNC and 3D printing paths for damaged areas, and to standardize and streamline field operations by linking the generated machining paths and work instructions with manufacturing execution systems such as ERP, MES, and WMS. Based on this technical background, the present invention aims to overcome the limitations of existing technology by providing a system that integrates and utilizes 3D precision scan data and CAD drawings to automatically recognize the area to be restored and automatically generate and transmit a processing path (G-code) and a work instruction. FIG. 1 is a device configuration diagram of a system for generating a remanufactured product processing process scheduling based on 3D precision scanning and CAD drawings according to one embodiment of the present invention. Figure 2 is a detailed configuration diagram of the multi-sensor fusion unit illustrated in Figure 1. Figure 3 is a detailed configuration diagram of the AI processing path generation unit illustrated in Figure 1. Figure 4 is a detailed configuration diagram of the digital twin simulation unit illustrated in Figure 1. FIG. 5 is a flowchart illustrating a method for generating a remanufactured product processing process scheduling based on a 3D precision scan and CAD drawing according to an embodiment of the present invention. Embodiments of the present invention are described below with reference to the attached drawings so that those skilled in the art can easily implement the invention. However, the present invention may be embodied in various different forms and is not limited to the embodiments described herein. Furthermore, in order to clearly explain the present invention in the drawings, parts unrelated to the explanation have been omitted, and similar parts throughout the specification are denoted by similar reference numerals. Throughout the specification, when a part is described as being “connected” to another part, this includes not only cases where they are “directly connected” b