Planning Of Repair Research Articles

Application of three-dimensional printing in the planning and execution of aortic aneurysm repair

IntroductionThe accuracy of fenestrations in stent grafts for complex aortic aneurysms and dissections can be significantly improved using three-dimensional (3D)-printed phantoms. Standardization is enhanced by using artificial intelligence (AI) for image pre-processing before 3D printing. These methods address fallacies in centerline image analysis and manual image pre-processing. This review examines the application of 3D printing and AI in complex aortic aneurysm repair, highlighting current clinical trends.MethodsAn exhaustive literature review was performed using keywords such as “3D printing,” “Artificial intelligence,” “Thoracoabdominal aneurysm,” “Abdominal aortic aneurysm,” “Aortic arch aneurysm,” “Endovascular repair,” and “Open repair” in PubMed and Google Scholar indexes up to June 2022.ResultsThis analysis included seven studies: four focused on 3D-printed phantoms for endovascular repair of various aortic pathologies (aortic arch, thoracoabdominal aorta, juxtarenal and pararenal aorta), one on open thoracoabdominal aneurysm repair using 3D-printed models for graft construction, and two on the use of convolutional neural networks, an AI-based technology, for the pre-processing of aortic computed tomography angiography images.ConclusionThe application of 3D printing and AI-based image pre-processing in the planning of complex aortic aneurysms offers several benefits, including enhanced patient and trainee education, more accurate fenestration placement, reduced surgical time and complications, and decreased surgeon stress.

Evaluation of Surface Weathering Degree of Brick Historical Buildings Based on GLCM-Entropy Weight TOPSIS: A Case Study in the Kaifeng City Wall

ABSTRACT The identification of the weathering degree of the surface of brick historical buildings is the key to determine the rationality of the repair plan. Image processing technology is a common method for quantitative weathering assessment, and mortar joint is a difficult point that affects the weathering assessment of brick ancient buildings. Based on the image chunking method, the GLCM-entropy weight TOPSIS method is proposed to evaluate the surface weathering degree of brick ancient buildings. The method includes three parts: The image chunking technology is proposed to effectively avoid the influence of mortar joint on the evaluation results. Second, GLCM is used to extract the four texture features of contrast, homogeneity, entropy and correlation of bricks, which can effectively distinguish image differences. Finally, the features are used as the EW-TOPSIS evaluation indices for comprehensive evaluation of 500 sample images obtained from Kaifeng city wall. The evaluation result S of Kaifeng city wall is 0.452, which indicates that the degree of weathering is moderate weathering. The results show that this method solves the problem of subjectivity in weathering recognition, and the introduction of image chunking processing can effectively avoid the error caused by image quality.

Health Status Assessment of a Scraper Conveyor Mechanical System

A method leveraging cooperative game theory is proposed to determine the combined weights of the mechanical system of a scraper conveyor, with a particular mining face serving as the research object. Additionally, the immune principle is introduced to estimate the health status of the mechanical system. First, a hierarchical analysis model and an immune hierarchical analysis model are established for the mechanical system of the scraper conveyor. The analytic hierarchy process, fuzzy statistics method, and entropy weight method are employed to determine the individual weights of each component in the mechanical system. Subsequently, the combined weights of various components in the mechanical system of the scraper conveyor are calculated using the cooperative game method. Second, the health values and health status of each component are calculated based on the sequence vector of component states output by the immune principle and the comprehensive weight vector. Finally, the weight vector of the mechanical system of the scraper conveyor is obtained based on the analytic hierarchy process. The health status of the entire mechanical system is then evaluated using the health status data of each component as the initial data for the system layer. The research results demonstrate that the presented method effectively obtains the health status of the mechanical system of the scraper conveyor by considering the physical characteristics of each component. Moreover, it facilitates the effective formulation of maintenance and repair plans for various components of the system.

Interobserver and intraobserver variability among different vendors for mitral valve assessment: implications for transcatheter mitral valve repair.

Pre-procedural imaging is critical for transcatheter mitral valve repair planning in patients with mitral valve disease. As differences among various measurement techniques for valve evaluation are still poorly understood, we sought to assess the intra- and interobserver agreement of complex measurements derived from a prototype mitral evaluation tool (Siemens) and a commercially available tool (CVI42) using both saddle- and D-shaped mitral annulus techniques. Multiphasic cardiac computed tomography angiography data were loaded into each software. Three expert readers independently measured the annuli on systolic- and diastolic-phase images using both tools. Measurement agreement between the tools was assessed with t tests, with p ≤ 0.05 considered statistically significant. Intraclass correlation coefficient (ICC) was used for interobserver agreement. Bland-Altman plots were used to assess for systematic differences. Ten patients (mean age: 61.9 ± 9.9years, 70%males) were included, with either normal mitral valve (n = 5) or with primary mitral regurgitation (n = 5). The intraobserver comparison showed statistically significantly different circumference and planar surface areas only for one reader in each group. However, there was significant difference (p ≤ 0.05) between measurements for intercommissural distance, septolateral distance, and intertrigone distance. The interobserver agreement was good (ICC, 0.60-0.74) to excellent (ICC, 0.75-1.00). There were systematic differences in mitral annulus area parameters for both saddle- and D-shaped annulus. Both tools effectively assess mitral annulus with good-to-excellent interobserver agreement. As there were systematic differences in the annular area and distance, use of the same software should be considered in clinical and research workflows.

COMPREHENSIVE ANALYSIS OF METHODS IN PLANNING TECHNICAL MAINTENANCE AND REPAIR OF EQUIPMENT

This article focuses on issues related to the comprehensive analysis of methods for planning equipment maintenance and repair.Considerable attention in the article is paid to the methods of V. V. Semenov, which allow developing schedules for equipment maintenance and repair, help to detect vulnerable areas in operation and carry out preventive measures before equipment malfunctions occur. The methods of V. V. Semenov not only play an important role in the standardization processes, which significantly affects the improvement of the quality of maintenance and repair work, but also touch upon the issue of the quality of repair work. The methods take into account the features of all types of equipment, which contributes to the use of individual approaches to each equipment. However, we have identified shortcomings in the methods, which are reflected in the table; the main one was identified and established as the use of the book value of the equipment, adopted as the basis for calculating standards. Market-oriented methods are used for high-quality planning of equipment maintenance and repair. The author sees the solution to problems in planning equipment maintenance and repair in the fact that these methods use the market value of equipment as a basis for calculating standards, the advantages of which are described in detail in the article. The methods developed by the author determine financial planning and create a reasonable basis for budgeting for equipment maintenance and repair, in a market economy they take into account changes in the market value of equipment, contribute to more accurate economic and financial forecasting, and allow more attention to be paid to assessing the financial risks associated with the operation of obsolete equipment and requiring the development of a strategy to reduce them. In conclusion, it should be noted that the author's methods under consideration are of primary importance in determining the equipment cost estimate, taking into account the costs of equipment maintenance and repair, which contributes to improved investment planning. Thus, based on the comprehensive analysis of the methods, it can be concluded that the use of market-oriented methods in planning contributes to effective asset management and an increase in the overall profitability of industrial enterprises.

Geospatial-based Mining Geometry Analysis to Improve Operational Efficiency of PT AMM Jobsite Mifa Bersaudara (2024)

Coal mining activities require the establishment of good and correct mine geometry to support effective and safe operations. However, many companies face challenges in ensuring that the geometry formed conforms to set standards. This research highlights the limited use of UAV technology in mine geometry optimization. This research aims to analyze geometry shaping at mine sites using geospatial data and raise awareness of the importance of good geometry in mining. The methods used include a combination of geographic information systems (GIS) and aerial surveys with UAVs. The mine geometry information was presented in map form to improve understanding of the geometry formation at each mine site. The results showed a 14% increase in geometry achievement, from 67% to 81%, after the application of the geospatial-based mine geometry information map. This reflects a significant increase in the formation of geometry in accordance with applicable rules. This finding shows the great potential of utilizing geospatial methods in the mining industry to improve the achievement of good geometry. In addition, this research can improve mine safety, the effectiveness of mine site inspections, and more organized repair planning, thus providing practical benefits to PT AMM's operations.

Features of Terrestrial Laser 3D Scanning Technology for Pavement Condition Assessment

Abstract. Problem. Traditional methods of road condition monitoring have limitations in terms of data accuracy and completeness, particularly in the detection of surface defects. There is a need for methods that provide accurate, rapid data collection to support effective maintenance and repair planning. Goal. The goal of this study is to develop and optimise a methodology for terrestrial 3D laser scanning and to provide practical recommendations for its application in road surface monitoring. Methodology. The proposed methodology examines the effectiveness of terrestrial laser scanning on road segments, addressing parameters such as optimal station count, placement and scanning mode to ensure accurate and comprehensive data collection. The Trimble TX6 scanner is used as an example, with specifications including a range of up to 120 metres, 2 mm accuracy and an adjustable scanning speed of up to 500,000 points per second. Results. The research identifies optimal setups for different road segments, covering short (~100–150 m) and long (up to 1000 m) sections, as well as complex road infrastructure such as multi-level interchanges. The results demonstrate the ability of the 3D scanning method to detect various linear and areal pavement defects. Originality. This study advances the field by developing specific recommendations for station positioning and scanning protocols, addressing blind spots, and ensuring data integrity for 3D modelling. Practical value. The application of this technology supports detailed analysis of pavement condition, significantly increasing the efficiency of data processing and improving repair planning and material estimation. The results are beneficial for transport infrastructure management, enabling more reliable monitoring and maintenance practices

ANALYSIS OF PASSENGER CARS FAILURES

The article is devoted to the technical condition analysis of the structural elements of passenger cars of the "Passenger Company" branch. The cars were operated by the regional branch "Odessa Railway" of JSC "Ukrzaliznytsia". The vast majority of domestic passenger cars have been in operation since the end of the last century and have already exhausted their resource. The service life of such cars has been repeatedly extended taking into account the condition of the body and frame. But the issues of the condition of the internal equipment of carriages, the level of its environmental safety, the compliance of comfort systems with modern requirements of the European Union have always remained outside the attention of researchers. The comfort systems in passenger cars, which were designed and manufactured in the 70-90s of the last century, do not meet modern EU requirements. This concerns both environmental standards and material and labor costs for their maintenance in working condition. The low level of comfort causes dissatisfaction among passengers, who choose other carriers. The existing passenger economy system must ensure repair and restoration work during the technical maintenance of cars between trips. To carry out these works, it is necessary to have a sufficient number of spare parts and equipment. During operational planning, heads of the department must take into account the labor intensity of the performed work. Currently, the calculation of these indicators is carried out using some averaged values. They do not take into account either the degree of wear of the car equipment, nor the place and nature of its operation. The lack of reliable information about the actual level of wear and damage of life support systems leads to unjustified overspending of material resources. In order to ensure the rational operation of the Passenger Company departments that prepare passenger trains for trip, it is necessary to create a solid scientific basis for the rational planning of repair and restoration work during maintenance based on factual information on the technical condition of the elements of the internal equipment and life support systems of passenger cars. At the same time, the required number of spare parts and equipment must be provided to restore operability when preparing a car for a trip. The results of the study will be used by the administration of the Passenger Company to develop organizational and technical solutions to restore the functionality of the cars.

PROSTHESIS REPAIR OF ORAL IMPLANTS BASED ON ARTIFICIAL INTELLIGENC`E FINITE ELEMENT ANALYSIS

Dentists often suggest dental implants to replace missing teeth; nevertheless, mechanical issues can develop with these implants, which could lead to prosthesis replacement or repairs. When investigating implant systems' mechanical characteristics and stress distribution, finite element analysis (FEA) is a popular computational tool. In biomechanical investigations, this strategy is widely used. However, traditional FEA methods can be tedious and require expert expertise for accurate simulation and translation of results. To automate and simplify the process of mending oral implant prostheses, the article suggests a new framework called AI-FEA. The three primary parts that make up the suggested AI-FEA framework are 1. An AI-powered model creation module that utilizes data from medical imaging to autonomously construct 3D finite element designs that are unique to each patient. Utilizing deep learning approaches, this module segments and reconstructs three-dimensional geometries from computed tomography (CT) or cone-beam CT data using material properties and boundary conditions. 2. A FEA solver that runs simulations to test the way the implant system handles different loads. This component uses state-of-the-art numerical methods to model the implant and bone interface and determine stress distributions. 3. An AI-based decision support system that takes all that data and recommends the best way to fix the prosthesis. Combining FEA findings with patient-specific variables, this decision support system uses machine learning algorithms educated on an extensive dataset of implant failure instances and repair results to provide the optimal repair strategy. For patients experiencing issues with oral implants, the suggested AI-FEA framework might mean huge time and skill savings in prosthesis repair planning, leading to better, more individualized care.

Simultaneous Implantation of Maxillary Fibroskeletal Lesions: A Case Report and Literature Review.

To investigate a clinical case of simultaneous implantation therapy for a patient with fibroskeletal disease of the jaw, and to provide a clinical basis for the treatment of such patients by clinical prosthetists. The clinical data of a patient with fibroskeletal disease of the jaw undergoing implant treatment were collected, and the clinical repair plan for this patient was discussed by reviewing the literature. The patient, a female, 49 years old, right upper posterior tooth missing for 1 year, through the maxillofacial surgery consultation, according to the imaging data, the maxillary fibroskeletal lesions were considered. The tumor was removed, bone regeneration was guided, and microimplants were implanted at the same time. Cone Beam Computed Tomography was performed at 3, 9, and 20 months postoperatively, and according to clinical evaluation, the patient did not show any signs of recurrence. At the same time, a literature search was conducted to summarize the data on simultaneous implantation therapy for fibroskeletal lesions of the jaw. The treatment plan of tumor removal → guided bone regeneration → simultaneous implantation of microimplants can effectively achieve implant repair and reduce the failure rate of implants in patients with jaw fibroskeletal lesions.

Empowering model repair: a rule-based approach to graph repair without side effects—extended version

Working with models can lead to inconsistencies, e.g., due to erroneous or contradictory actions during concurrent modeling processes. Modern modeling environments typically tolerate inconsistencies and support their detection. However, at a later stage of development, models are expected to be consistent, meaning their inconsistencies should be considered and resolved. The process of resolving model inconsistencies is commonly referred to as model repair. Our approach to model repair is semi-automatic in the sense that the repair tool computes appropriate repair plans and the modeler decides which path to take. The speciality of our approach is that the repair process can register any small improvement in the model. This allows the interaction with the user to be optimized, resulting in an approach with a high level of automation on the one hand and flexible configuration options on the other. The approach focuses on providing repair plans that do not have side effects, i.e., the computed repair plans do not inadvertently introduce a new inconsistency of already repaired constraints into the model. Since models often have a graph-like structure, we present our approach to model repair based on graphs. Our approach is completely formal—we use the algebraic graph transformation approach to prove its correctness. We also present a prototype implementation of our repair approach based on the Eclipse Modeling Framework and Henshin, a model transformation engine based on graph transformation, to perform the actual model repair. A first performance evaluation shows that graphs with up to 1000 nodes can be repaired in about 10 s.

Analysis of spoilage change over improvements on decorator machines with quality control groups (Pareto and fishbone diagrams)

Every company, including PT, has goals they'd like to accomplish. One of the businesses in the two-piece can sector is CPC. Up until now, PT's goal has been to switch products. The CPC is still not fulfilled; specifically, from January 2022 to May 2023, the product replacement time is still 42 minutes, resulting in the waste of 1043 cans, although the objective was set at 30 minutes for the replacement of the product and 800 cans for spoiling (wasted cans). The current issue is that 800 cans are wasted, and the goal product replacement time is 30 minutes. A quality control group was employed in this study's methodology (Pareto and fishbone diagrams). The primary goal of this study is to identify the underlying cause of the 42-minute product replacement time and the wasted 1043 cans. The analysis's findings demonstrated that the issue of resetting the plate is the one that frequently arises while switching out products. Several underlying causes of the reset plate issue were discovered after additional investigation, one of which was the unclear marking on the plate, which led to imprecise results when the plate was installed on the cylinder. Damages the can printing and necessitates a second plate installation, which wastes time and leads to spoilage. Consequently, a repair plan was created and put into action by changing the plate's markings to make installation easier to understand. This led to a quicker process of replacing the product (41 minutes before repair, 32 minutes after repair), as well as a decrease in the number of wasted cans (850 cans before repair, 828 cans after repair).

Improved Early Detection of Tube Leaks Faults in Pulverised Coal-fired Boiler Using Deep Feed Forward Neural Network

Boiler tube leaks significantly reduce the operational availability of power units, yet their early detection and prediction have not been fully realised in the industry. This paper introduces a novel approach employing deep feedforward neural networks for early detection of boiler tube leaks in pulverised coal-fired boilers. Early detection enhances repair planning, minimising downtime and production losses. It also improves monitoring and control of boiler tube failures, thereby optimising power plant operations and revenue. Diverse deep neural network models were developed and rigorously tested by leveraging 9 years of operational data (2012–2020). Exhaustive hyper-parameter optimisation, involving seven parameters, substantially improved predictive accuracy. By achieving training and testing accuracies of 82.8% to 99.3%, the study assessed their ability to detect boiler tube leaks over the same 9-year span, providing insights into leak detection capabilities. The models notably predicted all 12 identified tube leak events, averaging a 14-day lead time before boiler shutdown. In addition to leak prediction, a leak detection matrix was devised to analyse residual behaviour and reduce the likelihood of false alarms. However, the models’ predictive performance was observed to be limited to the following year, with satisfactory results for 2021 only. Incorporating the 2021 data into retraining significantly improved the predictions for 2022. The study concludes that while the models demonstrate robust short-term prediction capabilities, they require continuous retraining to maintain accuracy and relevance. This ongoing refinement is essential for keeping the models up-to-date and reliable in predicting future boiler tube leaks.

Assessing the Condition and Damage of a Stilt Wooden Home in an Area Prone to Floods

This study was intended to assess condition and damages of a stilt wooden house located in in Keningau, Sabah, in order to develop a proper home repair and restoration plan that can be scaled up to other houses of similar design in areas that are susceptible to flood and other environmental factors which can affect the structural integrity. The findings revealed that the chosen stilt wooden house has decayed severely that many parts of the house mainly the roof, the floors, the railing and stairs were in decrepit condition which could pose danger to the occupants. The results also implied that there were various factors that led to the condition of the house namely termite infestation and moist and weakened wood which could be due to the damaged zinc roof that caused rainwater to leak into the house, direct contact with floodwater and possibly leaky plumbing. A repair and restoration work was done by focusing on the parts of the house that needed immediate fix in order to ensure comfort and safety of the household. This study may shed more light on how to repair and restore as well as redesign wooden homes in order to be more resilient towards damaged caused by environmental variables.

Towards a reduced order model for EVAR planning and intra-operative navigation

IntroductionThe pre-operative planning and intra-operative navigation of the endovascular aneurysm repair (EVAR) procedure are currently challenged by the aortic deformations that occur due to the insertion of a stiff guidewire. Hence, a fast and accurate predictive tool may help clinicians in the decision-making process and during surgical navigation, potentially reducing the radiations and contrast dose. To this aim, we generated a reduced order model (ROM) trained on parametric finite element simulations of the aortic wall-guidewire interaction. MethodA Design of Experiments (DOE) consisting of 300 scenarios was created spanning over seven parameters. Radial basis functions were used to achieve a morphological parametrization of the aortic geometry. The ROM was built using 200 scenarios for training and the remaining 100 for validation. ResultsThe developed ROM estimated the displacement of aortic nodes with a relative error below 5.5% for all the considered validation cases. From a preliminary analysis, the aortic elasticity, the stiffness of the guidewire and the tortuosity of the cannulated iliac artery proved to be the most influential parameters. ConclusionsOnce built, the ROM provided almost real-time and accurate estimations of the guidewire-induced aortic displacement field, thus potentially being a promising pre- and intra-operative tool for clinicians.

City Bus Reliability Measurement Based on Sparse Field Data Supported by Selected State Space Models

Means of transport are an important part of today’s cities. Bus transport in particular is considered to be a reliable mode of transport. In cooperation with a city’s transport company, we process in this article data collected from two fleets of buses. The data records are related to the failures of individual bus subsystems. We focus on the study of data from engine and brake subsystems, the consequences of failures of which are the most serious in relation to traffic safety. The data are seemingly austere, as the records only contain information such as “operating/fault” during a given month (no known causes, mechanisms, or other more precise time information about the failure). On the basis of such sparse data, however, it is still possible to estimate the trend or predict the development of certain measures over time. For the study and subsequent prediction, we used approaches based on state space models. Specifically, we worked with a linear trend model and a periodic component model. For both fleets of buses, we have also analyzed what the respective model and its prediction could look like if we knew selected and more detailed time information about the failures. This model therefore provides a general idea of the rate of occurrence of failure trend development, expected number of failures within single months, and respective bus subsystem failure occurrence forecasts. Based on this information, operators and entrepreneurs can rationalize the processes related to operations, maintenance, and repair planning.

Nearby allocation of emergency repair resources for multiple faults in distribution networks based on parallel CNN algorithm

In order to improve the efficiency of emergency repair for multiple faults in distribution networks, a method for allocating emergency repair resources for multiple faults in distribution networks based on parallel convolutional neural network (CNN) algorithm is studied. This method uses a matrix operation based multiple fault detection method for distribution networks. After determining the location of multiple faults based on the direction of fault power, the principle is to allocate emergency repair resources nearby for multiple faults, with the goal of minimizing economic losses caused by the fault point and minimizing fault repair time. The objective function for allocating emergency repair resources nearby is constructed, and parallel CNN algorithm is used to solve the problem by classification, to find the feasible solution with the minimum mean square error between the objective function and the feasible solution set for nearby allocation of emergency repair resources, and it is used as the optimal solution for nearby allocation of repair resources. The experimental results show that when the proposed method is used to allocate emergency repair resources for multiple faults in the distribution network, the optimal time for setting the repair plan is 0.53 and 0.96 s, respectively, with an average allocation accuracy of 91%. It has been confirmed that this method can achieve the optimal decision of resource allocation plan in a short period of time, improving the efficiency of emergency repair.

Development of an Information System for Wood Harvesting Planning

The article presents the results of developing a software system for production planning in the field of wood harvesting. The objects of research are the planning of harvesting operations, forest transport, construction, repair and maintenance of forest roads, as well as solving various related problems. The purpose of the work is to develop a native specialized software system for production planning in the field of wood harvesting, which is integrated with existing accounting and monitoring systems and is based on solving complex optimization problems of large dimensions using original specially developed methods and algorithms that take into account all the features of wood harvesting production in the Russian Federation. As a result of this work, an approach to creating such а system was developed, which includes a set of requirements for the software product, as well as formulations of the main tasks to be solved, listing the necessary initial data and information obtained as an output. The proposed approach was implemented by Russian IT-company Opti-Soft in the form of software product Opti-Wood — a multipurpose Decision Support System (DSS) designed for the use at forestry enterprises as a tool for optimizing production planning, management and control. The Opti-Wood system was developed in close contact with a large wood harvesting enterprise in the Republic of Karelia. The system is based on original algorithms for solving several optimization problems of increased complexity. Using the system allows one to increase the efficiency of logging processes by reducing costs in supply chains, the volume of warehouse stocks, the number of required vehicles; by mutual agreement of plans for harvesting operations, forest transport, road construction, etc., as well as by significantly reducing the time for taking management decisions. Increased profitability occurs due to the identification of additional options for the sale of products.

Understanding no fault found event risk in military aircraft MRO planning: A step towards robust bidding and contract finalization

No fault found (NFF) is a reported fault for which the root cause cannot be found. An NFF event not only reduces the availability of the equipment to customers but also results in financial losses to the manufacturer. To date, risk due to NFF events is not considered separately in aircraft Maintenance, Repair, and Overhaul (MRO) planning. In this paper, we have studied the NFF event risk identification, distribution, and optimization in an aircraft MRO process based on data collected over three decades of aircraft maintenance. For framework development and formulating the critical NFF risk drivers, this paper applies soft system methodology with detailed semi-structured interviews of industry experts. For NFF risk distribution and optimization, we have used agent-based simulation modeling. The quantitative assessment of more than 5000 NFF cases in an aircraft MRO organization shows that NFF events are significantly non-deterministic during the life cycle of an aircraft. The three identified critical NFF risk drivers in the research are the MRO process stage, aircraft life cycle stage, and human working skills. The MRO process stage is the time of the NFF event during repair activity. The life cycle stage represents the timeline of the NFF event in useful aircraft life. Human working skill represents the role of manpower in the NFF event solution. Further theoretical elaborations of the research lead to the optimized NFF risk break-up at the contract preparation stage and risk driver's sensitivity analysis by the aircraft assembler, operator, and sub-contractor at the commercial bid submission stage.

A case report demonstrating the utilization of computer-aided design technology and intramedullary nails for the management of femoral deformity and fracture.

To effectively address severe deformities at the fracture site in patients, meticulous preoperative preparation is essential. This involves ensuring the restoration of the normal structural force line of the affected area and prevention of any residual deformities. By utilizing E3D technology prior to surgery, creating models based on individual patient image data and performing osteotomy, the required surgical parameters can be measured, thereby reducing surgical risks and enhancing precision. This article documents a case involving a fractured femoral shaft resulting in femoral deformation. In this case, computer-aided design technology was employed for preoperative planning and data measurement to guide the corrective osteotomy and fracture fixation procedures. The E3D software utilizes advanced techniques such as customized osteotomy, virtual reduction and internal fixation insertion technology. This enables the software to accurately pre-select the correction of femoral deformities and determine the appropriate specifications and types of internal implants. As a result, the software can create precise, rational, and personalized repair plans tailored to each patient's needs.