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Design of Polymeric Orthopedic Screws with Variable Stiffness and Multi-Objective optimization of Injection Molding process

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INTERNATIONAL JOURNAL OF PRECISION ENGINEERING AND MANUFACTURING 2023年第4期24卷 629-643页

作者： Yadav, Sunil Kumar Sikidar, Arnab Kalyanasundaram, Dinesh Indian Inst Technol Delhi Ctr Biomed Engn New Delhi 110016 India All India Inst Med Sci Dept Biomed Engn New Delhi 110029 India

BackgroundOrthopedic metallic screws even post healing causes 'pinching effect' and load inflammation to the bone. In this work, the design and development of a novel metallic core and polymeric shell orthopedic screw is discussed along with optimization of manufacturing *** comparative 2D finite element study for pull-out test was conducted on HA, HB, HC, and HD standards of screws for approximately identical diameters to determine the type of threading for the proposed design of the novel orthopedic screw. The polymeric shell of the orthopedic screws was manufactured by injection molding process. The material used for manufacturing the polymeric shell was poly-l-lactic acid. Injection pressure, injection velocity, packing pressure and packing time of the injection molding process were optimized for the following output responses: maximum axial pull-out strength, and maximal bending strength of the *** r-squared and the adjusted r-squared value of the developed regression model for pull-out was found to be 96% and 92%, respectively. For the bending force, the developed regression model had a r-squared and adjusted r-squared value of 87% and 75%, respectively. The regression models were maximised to obtain the desired input parameters. The optimised parameters were validated experimentally at two local maxima for both pull-out and bending *** developed screw is novel in design and a patent has been filed under US patent office with number US 2022/0000529 A1. The screws can be used by medical practitioners to avoid or minimise revision surgery and inflammation.

关键词： Orthopedic screw Injection molding process parameter optimization Poly-l-lactic acid Response surface design Mechanical characterisation

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process parameter analysis of ultrasonic vibration-assisted polishing of SiCp/Al ceramics based on optimized Hilbert trajectories

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JOURNAL OF MATERIALS RESEARCH AND TECHNOLOGY-JMR&T 2023年 27卷 7673-7684页

作者： Cui, Zhijie Chen, Liaoyuan Meng, Fanwei Ma, Zhelun Yu, Tianbiao Zhao, Ji Wang, Zixuan Northeastern Univ Sch Mech Engn & Automat Shenyang 110819 Peoples R China Northeastern Univ Liaoning Prov Key Lab High end Equipment Intellig Shenyang 110819 Peoples R China Northeastern Univ Shenyang 110819 Peoples R China

As an important ceramic matrix composite material, SiCp/Al ceramics have found extensive applications across several domains. However, the intricate nature of their material properties has resulted in a scarcity of scholarly investigations pertaining to the topic of ultrasonic vibration-assisted polishing (UVAP) of SiCp/Al ceramics. To solve this problem, a theoretical derivation of the material removal model (MRM) for UVAP SiCp/Al ceramics is presented in this paper. The selection of UVAP feeding speed is analyzed and discussed. The process parameters of UVAP of SiCp/Al ceramics are optimized based on the improved Hilbert's trajectory characteristics. According to the experimental results, high-quality machined surfaces can be obtained and the "fish scale" phenomenon can be avoided when the feeding speed is 0.015 m/s. Compared with the traditional trajectory, the improved Hilbert trajectory is easier to achieve uniform surface polishing. The introduction of ultrasonic amplitude (UA) can increase the material removal rate (MRR) by more than 21.6 % compared with the conventional polishing method. The maximum removal depth (MRD) increased by more than 27.76 % and the surface roughness (SR) is reduced by more than 76.45 %. The research results have an important application value for realizing efficient and high-quality polishing of SiCp/Al ceramics.

关键词： Trajectory optimization process parameter optimization SiCp/al ceramics Ultrasonic vibration-assisted polishing Micro cutting

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Effect of annealing on physio-mechanical properties and laser parameter selection of additively manufactured Ti6Al4V orthopedic implants

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RAPID PROTOTYPING JOURNAL 2023年第5期29卷 959-970页

作者： Gaur, Bhanupratap Sagar, Samrat Suryawanshi, Chetana M. Tikekar, Nishant Ghyar, Rupesh Bhallamudi, Ravi Indian Inst Technol Biomed Engn & Technol Incubat Ctr BETIC Lab Mech Engn Dept Mumbai India Indian Inst Technol Mech Engn Dept Microstruct Mech & Microforming Lab MMMF Lab Mumbai India Indian Inst Technol DSSE Mumbai India

PurposeTi6Al4V alloy patient-customized implants (PCI) are often fabricated using laser powder bed fusion (LPBF) and annealed to enhance the microstructural, physical and mechanical properties. This study aims to demonstrate the effects of annealing on the physio-mechanical properties to select optimal process parameters. Design/methodology/approachTest samples were fabricated using the Taguchi L9 approach by varying parameters such as laser power (LP), laser velocity (LV) and hatch distance (HD) to three levels. Physical and mechanical test results were used to optimize the parameters for fabricating as-built and annealed implants separately using Grey relational analysis. An optimized parameter set was used for fabricating biological test samples, followed by animal testing to validate the qualified parameters. FindingsTwo optimized sets of process parameters (LP = 100 W, LV = 500 mm/s and HD = 0.08 mm;and LP = 300 W, LV = 1,350 mm/s and HD = 0.08 mm) are suggested suitable for implant fabrication regardless of the inclusion of annealing in the manufacturing process. The absence of any necrosis or reaction on the local tissues after nine weeks validated the suitability of the parameter set for implants. Practical implicationsTo help PCI manufacturers in parameter selection and to exclude annealing from the manufacturing process for faster implant delivery. Originality/valueTo the best of the authors' knowledge, this is probably a first attempt that suggests LPBF parameters that are independent of inclusion of annealing in implant fabrication process.

关键词： Patient-customized orthopedic implants Laser powder bed fusion Direct metal laser sintering Annealing process parameter optimization

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Spring-Back Prediction and process optimization of Rocket Frame Ring

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INTERNATIONAL JOURNAL OF AERONAUTICAL AND SPACE SCIENCES 2025年 1-20页

作者： Zhang, Zaifang Chen, Jiayi Ostrosi, Egon Cheng, Hui Zhou, Liang Meng, Wei Zhang, Zhichao Shanghai Univ Sch Mechatron Engn & Automat Shanghai 200444 Peoples R China Univ Bourgogne Franche Comte UTBM Pole Ind 4 0 Pole ERgon & COncept Syst ERCOS ELLIADD EA4661 F-25000 Belfort France Shanghai Aerosp Equipments Manufacturer Co Ltd Shanghai 200245 Peoples R China

Profile stretch-bending forming is a complex process influenced by multiple factors, including processing technology, geometric parameters, and material properties. These factors make it challenging to predict and optimize the forming process, especially controlling springback, which significantly affects the final quality. This study investigates the impact of the stretch-bending process on the quality of rocket frame rings by combining both simulation and production data. A Co-Kriging model is developed to approximate the frame ring forming process, and a multi-objective optimization model is applied to minimize springback, deformation, and residual stress in the rings. The optimization is performed using the non-dominated sorting genetic algorithm with a projected target area (NSGA-II/A) to identify the optimal process parameters. The results show that the optimized parameters significantly improve the forming quality and help achieve higher precision in the production of rocket frame rings. This work provides valuable insights for enhancing the stretch-bending forming process and guiding the production of high-quality components.

关键词： Rocket frame ring Stretch-bending forming process parameter optimization Co-Kriging model NSGA-II/A

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Optimizing the Industrial Human-Machine Interface: Holographic Surfaces and Their Role in Anomaly Detection 26th

Optimizing the Industrial Human-Machine Interface: Holograph...

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26th International Conference on Human-Computer Interaction (HCII)

作者： Schloer, Nicholas Boos, Sabine Harst, Felix Lanquillon, Carsten Ohrnberger, Morris Schoch, Fabian Stache, Nicolaj C. Wittenberg, Carsten Heilbronn Univ Ctr Ind Artificial Intelligence IAI Max Planck Str 39 D-74081 Heilbronn Germany

ISBN: (纸本)9783031621093;9783031621109

This abstract explores the integration of holographic surfaces into industrial human-machine interfaces (HMIs) to optimize anomaly detection processes. By utilizing this cutting-edge technology, industrial environments can achieve more efficient and effective anomaly detection, ultimately leading to increased safety, productivity, and operational integrity. Holographic user interfaces provide a novel approach by visualizing complex data in an immersive, intuitive, and interactive way. By projecting three-dimensional representations of equipment, processes, and real-time data onto user interfaces, operators can quickly recognize anomalies and deviations from normal operating conditions. The holographic interfaces provide contextual awareness, improving operators' understanding of the industrial environment and enabling them to make informed decisions with confidence. Holographic interfaces have the potential to revolutionize anomaly detection in industrial HMIs, resulting in improved efficiency and safety in industrial operations.

关键词： Artificial Intelligence Predictive Maintenance Predictive Quality Anomaly detection process parameter optimization Explainable AI Human-Computer Interaction Mixed Reality

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Multi-objective optimization of material extrusion additively manufactured parts from PETG feedstock material using grey wolf algorithm

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PROGRESS IN ADDITIVE MANUFACTURING 2024年第5期10卷 3807-3825页

作者： Trivedi, Achyut Patel, Mihir Ganpat Univ Ctr Excellence Addit Mfg Mehsana 384012 Gujarat India Ganpat Univ Maritime Studied Mehsana 384012 Gujarat India

The present study examines the multi-objective optimization of material extrusion additively manufactured (MEAM) parts utilizing the Grey Wolf Algorithm (GWO) with a focus on polyethylene terephthalate glycol (PETG) material. The process parameters are involved like nozzle temperature, layer thickness, and part orientation, while the response parameters have effects on tensile strength, flexural strength, and surface roughness. The primary aim of this optimization aspires to maximize tensile strength and flexural strength, and second, to minimize surface roughness. For achieving the optimization process, a central composite design (FCCCD) methodology linked with analysis of variance (ANOVA) is used in the present study. The experimental results establish the connection between process parameters and response characteristics. The present study findings underline the importance of certain settings i.e., higher nozzle temperature and layer thickness, and lower part orientation. Improving the mechanical properties such as tensile strength and flexural strength is the most important for ensuring the structural integrity and functional performance of MEAM parts. The optimization framework is guided by the GWO algorithm, strategically adjusts the process parameters to achieve these objectives. As an initial objective, experimental outcomes show a notable improvement in mechanical properties, particularly when operating at higher nozzle temperatures. This suggests that thermal energy plays an important role in enhancing the bonding strength of PETG-based MEAM parts. In addition to that, the second objective focuses on reducing issues related to surface roughness which is critical for achieving high-quality surface finishes. The optimization process shows that reducing part orientation, with adjustments in nozzle temperature and layer thickness, contributes significantly to minimizing surface roughness. This underscores the importance of present multi-objective optimizing pr

关键词： Multi-objective optimization Material extrusion additive manufacturing (MEAM) Grey wolf algorithm (GWO) PETG process parameter optimization

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Hot cracking during the fabrication of Inconel 625/stainless steel 308 L functionally graded material by dual-wire arc additive manufacturing

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JOURNAL OF MANUFACTURING processES 2022年 82卷 461-473页

作者： Li, Tianxu Wang, Zhijiang Hu, Shengsun Yang, Zhenwen Wang, Ying Tianjin Univ Sch Mat Sci & Engn Tianjin Key Lab Adv Joining Technol Tianjin 300354 Peoples R China Tianjin Univ Sch Mat Sci & Engn Tianjin 300354 Peoples R China

In the present work, dual-wire arc additive manufacturing was used to fabricate the functionally graded material (FGM) with a smooth composition transition from 100 wt% Inconel 625 (IN625) to 100 wt% stainless steel 308 L, whose composition transition was along the torch-travelling direction. With such a composition transition, po-sition with the weakest mechanical properties could be clearly and easily located in the composition gradient path, allowing the issue to be fully exposed. During the fabrication, cracks were found near the position with IN625 of 20 wt%. By analyzing the fracture morphology and crack location, they were judged as liquation cracks, which belong to hot cracks. The segregation and aggregation of carbides at the grain boundary formed the low -melting-temperature eutectic phase, which is liquefied into the liquid film by the heating of subsequent layers and is then pulled apart by residual stress. According to the experimental results and the calculations of phase diagrams, the cracks always appeared near the position with IN625 of 20 wt%, where the FGM has the highest crack sensitivity. Therefore, it was determined as the weakest position of the FGM and its weakening mechanism could be clarified easier owing to the composition transition, which provides a high possibility to get better mechanical properties if a more targeted optimization method is applied. process parameter optimization re-duces the heat input per unit height to thin dendrites and to alleviate cracking, but there is still a cracking tendency in the weak composition range. The composition gradient path optimization, by removal of the cor-responding composition range, can solve the cracking issue more thoroughly, and the tensile strength and elongation increased by 39.5 % and 221.7 %, respectively, which greatly improved the mechanical properties of the FGM.

关键词： Hot cracking Composition gradient path optimization process parameter optimization Dual-wire arc additive manufacturing Functionally graded material

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optimization of magnetic composite fluid polishing process based on response surface method

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JOURNAL OF THE CHINESE INSTITUTE OF ENGINEERS 2022年第1期45卷 35-41页

作者： Jian, Yin Ri, Pan Fan, Jinwei Wang, Zhenzhong Zhang, Dongxu Ji, Shuting Beijing Univ Technol Beijing Key Lab Adv Mfg Technol Beijing 100124 Peoples R China Xiamen Univ Dept Mech & Elect Engn Xiamen Peoples R China Xiamen Univ Shenzhen Res Inst Shenzhen Peoples R China

Currently, research on the optimization of key process parameters in the polishing process usually uses orthogonal test method, which needs a lot of experiments. In order to solve this problem, this paper proposes a parameters optimization of magnetic composite fluid polishing process based on response surface method. Firstly, roughness of the polycarbonate microfluidic chip surface polished with magnetic composite fluid was used as the optimization object, while the machining gap, spindle speed and machining time were defined as the key parameters, and experiments were designed and conducted based on response surface method. Subsequently, using the above experimental data, a prediction model for microfluidic chip surface roughness after polishing that depends on key parameters is established. After that, by the application of the prediction model, the key parameters were optimized and verified by experiments. The results indicated that by using the optimized process parameters, the minimum surface roughness of the microfluidic chip reduced from 0.5 mu m to 0.0384 mu m, while the maximum and minimum errors between measured and predicted results are 0.0052 mu m and 0.0007 mu m, respectively. The above result has proved the accuracy of the established model and the effectiveness of the proposed parameters optimization method.

关键词： Magnetic composite fluid polishing response surface method process parameter optimization surface roughness

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Machine learning-based optimization of process parameters in selective laser melting for biomedical applications

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JOURNAL OF INTELLIGENT MANUFACTURING 2022年第6期33卷 1843-1858页

作者： Park, Hong Seok Nguyen, Dinh Son Le-Hong, Thai Van Tran, Xuan Univ Ulsan Dept Mech Engn 93 Daehak Ro Ulsan 44610 South Korea Thu Dau Mot Univ Inst Strategies Dev Binh Duong Vietnam

Titanium-based alloy products manufactured by Selective Laser Melting (SLM) have been widely used in biomedical applications, owing to their high biocompatibility, significantly good mechanical properties. In order to improve the Ti-6Al-4V SLM-fabricated part quality and help the manufacturing engineers choose optimal process parameters, an optimization methodology based on an artificial neural network was developed to relate four key process parameters (laser power, laser scanning speed, layer thickness, and hatch distance) and two target properties of a part fabricated by the SLM technique (density ratio and surface roughness). A supervised learning deep neural network based on the backpropagation algorithm was applied to optimize input parameters for a given set of quality part outputs. Several methods were utilized to solve undesired problems occurring during neural network training to increase the model accuracy. The model's performance was proven with a value of R-2 of 99% for both density ratio and surface roughness. A selection system was then built, allowing users to choose the optimal process parameters for fabricated products whose properties meet a specific user requirement. Experiments performed with the optimal process parameters recommended by the optimization system strongly confirmed its reliability by providing the ultimate part qualities nearly identical to those defined by the user with only about 0.9-4.4% of errors at the maximum. Finally, a graphical user interface was developed to facilitate the choice of the optimum process parameters for the desired density ratio and surface roughness.

关键词： Selective laser melting Titanium-based alloys process parameter optimization Artificial neural network Additive manufacturing Machine learning

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optimization of EDM process parameters based on variable-fidelity surrogate model

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INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY 2022年第3-4期122卷 2031-2041页

作者： Ma, Jun Yin, Chunyang Li, Xiaoke Han, Xinyu Ming, Wuyi Chen, Shiyou Cao, Yang Liu, Kun Zhengzhou Univ Light Ind Sch Mech & Elect Engn Henan Key Lab Mech Equipment Intelligent Mfg Zhengzhou 450002 Peoples R China China Railway Engn Equipment Grp Co Ltd Zhengzhou 450002 Peoples R China

To balance the cost and accuracy in the optimization of EDM process parameters, the variable-fidelity surrogate model (VFM) was introduced in this paper to replace the implicit relationship between the EDM process parameters (peak current, duty cycle, pulse period) and performance functions (material remove rate, MRR, and surface roughness, Ra). Low-fidelity (LF) samples were obtained by using the low-cost EDM heat flow simulation, and high-fidelity (HF) samples were obtained by using the EDM machining experiments. By fusing the LF and HF samples, it is possible to establish an accurate expression of the implicit relationship between the EDM process parameters and performance functions in the variable-fidelity framework. Finally, the EDM process parameter model was established, and sequential quadratic programming (SQP) was used to obtain the optimal solution by calling the VFM. Through the verification experiments, the results showed that the parameter combination obtained by VFM is 15.1% higher than the MRR of the low-fidelity model (LFM) and 13.0% higher than the high-fidelity model (HFM), which reaches 88.285 mg/min, while the Ra has slightly decreased and is within the constraint range. From the above research, it can be concluded that the proposed technology has significant advantages and application potential in the field of EDM machining optimization.

关键词： EDM process parameter optimization Variable-fidelity surrogate model Co-Kriging

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