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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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Modeling and optimizing the specific cutting energy of medium density fiberboard during the helical up-milling process

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WOOD MATERIAL SCIENCE & ENGINEERING 2023年第2期18卷 464-471页

作者： Li, Rongrong He, Chujun Xu, Wei Wang, Xiaodong (Alice) Nanjing Forestry Univ Coinnovat Ctr Efficient Proc & Utilizat Forest Re Nanjing 210037 Jiangsu Peoples R China Laval Univ Dept Wood & Forest Sci Quebec City PQ Canada

Due to the flexible motion characteristics, helical milling could achieve high surface quality and cutting stability. The effects of input parameters on specific cutting energy (SCE) during the medium density fiberboard (MDF) helical up-milling process were studied. Results of analysis of variance showed that the helical angle and depth of milling had extremely significant effects on SCE. SCE increased with increased helical angle, but decreased with increased milling depth. The impact of the rotation speed of the main shaft was non-significant. Due to the highest R-2 value, a quadratic model was selected to establish the relationship between input parameters and SCE. The relative errors between predicting results and confirmatory test results were minimal, which meant that the model had high predicting accuracy. Under the selected input parameters, the optimized parameters were 54 degrees, 5500 r/min, 1.5 mm for helical angle, the rotation speed of the main shaft, depth of milling, respectively. Although the arithmetic average of absolute roughness (Ra) and mean peak-to-valley height (Rz) increased about 58.3% and 46.2%, respectively, under the optimal milling parameters, the optimization was feasible at the initial rough machining stage. These results will be beneficial in guiding the selection of processing parameters to achieve reducing SCE.

关键词： Power consumption specific cutting energy helical up-milling medium density fiberboard response surface methodology process parameter optimization

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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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Advances and Challenges in Predictive Modeling for Additive Manufacturing of Dissimilar Metals and Complex Alloys

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MATERIALS 2023年第16期16卷 5680-5680页

作者： Adak, Debajyoti Sreeramagiri, Praveen Roy, Somnath Balasubramanian, Ganesh Indian Inst Technol Dept Mech Engn Kharagpur 721302 India Lehigh Univ Dept Mech Engn & Mech Bethlehem PA 18015 USA

We present a scrutiny on the state of the art and applicability of predictive methods for additive manufacturing (AM) of metals, alloys, and compositionally complex metallic materials, to provide insights from the computational models for AM process optimization. Our work emphasizes the importance of manufacturing parameters on the thermal profiles evinced during processing, and the fundamental insights offered by the models used to simulate metal AM mechanisms. We discuss the methods and assumptions necessary for an educated tradeoff between the efficacy and accuracy of the computational approaches that incorporate multi-physics required to mimic the associated fluid flow phenomena as well as the resulting microstructures. Finally, the current challenges in the existing approaches are summarized and future scopes identified.

关键词： additive manufacturing microstructure simulation thermal transport melt pool process parameter optimization

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Metal-Metal Bonding process Research Based on Xgboost Machine Learning Algorithm

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POLYMERS 2023年第20期15卷 4085-4085页

作者： Feng, Jingpeng Zhan, Lihua Ma, Bolin Zhou, Hao Xiong, Bang Guo, Jinzhan Xia, Yunni Hui, Shengmeng Cent South Univ State Key Lab Precis Mfg Extreme Serv Performance Changsha 410083 Peoples R China Cent South Univ Light Alloys Res Inst Changsha 410083 Peoples R China

Conventionally, the optimization of bonding process parameters requires multi-parameter repetitive experiments, the processing of data, and the characterization of complex relationships between process parameters, and performance must be achieved with the help of new technologies. This work focused on improving metal-metal bonding performance by applying SLJ experiments, finite element models (FEMs), and the Xgboost machine learning (ML) algorithm. The importance ranking of process parameters on tensile-shear strength (TSS) was evaluated with the interpretation toolkit SHAP (Shapley additive explanations) and it optimized reasonable bonding process parameters. The validity of the FEM was verified using SLJ experiments. The Xgboost models with 70 runs can achieve better prediction results. According to the degree of influence, the process parameters affecting the TSS ranked from high to low are roughness, adhesive layer thickness, and lap length, and the corresponding optimized values were 0.89 mu m, 0.1 mm, and 27 mm, respectively. The experimentally measured TSS values increased by 14% from the optimized process parameters via the Xgboost model. ML methods provide a more accurate and intuitive understanding of process parameters on TSS.

关键词： single-lap joints finite element models Xgboost machine learning algorithm interpretation toolkit SHAP process parameter optimization

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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 2025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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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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Performance optimization of Ti-Steel Laser-Welded Joints Based on Optimized Support Vector Machine and Multi-Objective Salp Swarm Algorithm

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SAE International Journal of Materials and Manufacturing 2024年第2期18卷

作者： Zhu, Yubin Meng, Xiangli Zhang, Xinran Jiangsu Normal University Engineering Training Center China Jiangsu Normal University China

The dissimilar welding of titanium to steel enables the integration of the advantageous properties of both metals, facilitating the design of lightweight, corrosion-resistant, and high-strength multifunctional composite structures. However, significant differences in their thermophysical properties pose substantial technical challenges in practical welding scenarios, necessitating careful selection of process parameters to enhance the quality and performance of the weld joint. This article establishes a support vector machine (SVM) model with laser power, welding speed, and laser spot diameter as independent variables, and the maximum residual stress and minimum yield strength of the weld joint as dependent variables. To improve prediction accuracy, the SVM model is optimized using the beluga whale optimization (BWO) algorithm. Taking the established model as the objective function, the multi-objective salp swarm algorithm (MSSA) is employed to optimize the laser welding process parameters for titanium-steel dissimilar metal welding. Simulation experiments validate the efficacy of this optimization approach. © 2025 SAE International.

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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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