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This study aims to develop an integrated approach for 3D lumbar vertebral biomodel design and analysis, specifically targeting unilevel disc degeneration and the replacement of lumbar artificial discs. Key objectives include improving existing design methods through 3D techniques, inverse modeling, and an engineering biomodel preparation protocol. Additionally, the study evaluates mechanical properties in the implantation area and between disc components to gauge the effectiveness of artificial discs in restoring functional movement within the studied biological model. The construction of a biological model representing the L3–L4 functional spinal unit was based on measurements from radiographic images and computed tomography data obtained from the study sample. The 3D finite element method in Ansys software (v. 19.2, ANSYS, Inc., Canonsburg, PA, USA) was used to monitor the distribution of equivalent stress values within the core of the two artificial discs and the behavior of vertebral bone components in the model. This approach enabled the creation of personalized digital models tailored to the specific implantation requirements of each patient. Stress analysis identified critical areas within the disc cores, suggesting potential design modifications to optimize artificial disc performance, such as selectively increasing core thickness in specific regions and considering adjustments during implantation. For example, preserving part of the lateral annulus fibrosus from the degenerative disc and maintaining the anterior and posterior longitudinal ligaments may play a crucial role in balancing the forces and moments experienced by the lumbar section. This study provides valuable insights into the development of patient-specific solutions for lumbar disc degeneration cases, with the potential for enhancing artificial disc design and implantation techniques for improved functional outcomes.

Purpose The purpose of this paper is to propose an integrated decision making model for Multi-Attributes Decision Making (MADM) problems in Additive Manufacturing process planning, as well as for related MADM problems in other research areas. Design/methodology/approach This research analyzed the drawbacks of former methods and then proposed two sub decision making models, ‘deviation model’ and ‘similarity model’. The former sub model aimed to measure the deviation extent of each alternative to the aspired goal based on analyzing Euclidean distance between them. While the latter sub model applying Grey Incidence analysis was used to measure the similarity between alternatives and the expected goal by investigating the curve shape of each alternative. Afterwards, an integrated model based on the aggregation of the two sub models was proposed and verified by numerical example and simple case studies. Findings The calculating results of the cited numerical example and the comparison to former related research showed that this proposed model is more practical and reasonable than former methods applied in MADM problems of AM. In addition, the proposed model can be also applied in other fields where exists MADM problems. Originality/value This proposed integrated model not only considers the deviation extent of alternatives to the aspired goal, but also investigated the similarity between alternatives and the expected goal. The similarity analysis compensates the drawbacks of traditional ‘distance-based’ models or methods that can’t distinguish alternatives which have the same distance-based index value.

Build orientation is one of the most important process planning tasks in additive manufacturing (AM) since it directly affects the part quality, build time, cost etc. Many researchers have investigated the orientation optimization problem and proposed numerous solutions. However, former researches only focused on how to find an optimal orientation for one part, but none of the solutions was provided to solve the orientation optimization problem of Multi-part production, where a group of parts in the same build vat or chamber should be optimally-orientated simultaneously. This paper introduces a two-step solution to solve the problem. At first, a feature based method is used to generate a set of finite optimal alternative orientations for each part within a given part group to guarantee each part’s individual build quality; then an improved genetic algorithm is applied to search for an optimal combination of part build orientations to minimize the total build time and cost at a global optimal level. A case study of orientating 16 parts simultaneously within a given build chamber is presented for demonstration.

Purpose For part models with complex shape features or freeform shapes, the existing build orientation determination methods may have issues, such as difficulty in defining features and costly computation. To deal with these issues, this paper aims to introduce a new statistical method to develop fast automatic decision support tools for additive manufacturing build orientation determination. Design/methodology/approach The proposed method applies a non-supervised machine learning method, K-Means Clustering with Davies–Bouldin Criterion cluster measuring, to rapidly decompose a surface model into facet clusters and efficiently generate a set of meaningful alternative build orientations. To evaluate alternative build orientations at a generic level, a statistical approach is defined. Findings A group of illustrative examples and comparative case studies are presented in the paper for method validation. The proposed method can help production engineers solve decision problems related to identifying an optimal build orientation for complex and freeform CAD models, especially models from the medical and aerospace application domains with much efficiency. Originality/value The proposed method avoids the limitations of traditional feature-based methods and pure computation-based methods. It provides engineers a new efficient decision-making tool to rapidly determine the optimal build orientation for complex and freeform CAD models.

Purpose The purpose of this paper is to present research work based on the authors’ conceptual framework reported in the VRAP Conference 2013. It is related with an efficient method to obtain an optimal part build orientation for additive manufacturing (AM) by using AM features with associated AM production knowledge and multi-attribute decision-making (MADM). The paper also emphasizes the importance of AM feature and the implied AM knowledge in AM process planning. Design/methodology/approach To solve the orientation problem in AM, two sub-tasks, the generation of a set of alternative orientations and the identification of an optimal one within the generated list, should be accomplished. In this paper, AM feature is defined and associated with AM production knowledge to be used for generating a set of alternative orientations. Key attributes for the decision-making of the orientation problem are then identified and used to represent those generated orientations. Finally, an integrated MADM model is adopted to find out the optimal orientation among the generated alternative orientations. Findings The proposed method to find out an optimal part build orientation for those parts with simple or medium complex geometric shapes is reasonable and efficient. It also has the potential to deal with more complex parts with cellular or porous structures in a short time by using high-performance computers. Research limitations/implications The proposed method is a proof-of-concept. There is a need to investigate AM feature types and the association with related AM production knowledge further so as to suite the context of orientating parts with more complex geometric features. There are also research opportunities for developing more advanced algorithms to recognize AM features and generate alternative orientations and refine alternative orientations. Originality/value AM feature is defined and introduced to the orientation problem in AM for generating the alternative orientations. It is also used as one of the key attributes for decision-making so as to help express production requirements on specific geometric features of a desired part.

Restoring damaged components is a very promising and high-value project, which enable to save a lot of production time and cost, and thus has already attracted wide attention, especially in the aviation industry. In the past few years, cold spray (CS) had been widely adopted in restoration and repair applications due to its unique advantages, such as no thermal influence, high efficiency, flexibility, etc. Nowadays, speeding up the product lifecycle as well as improving the accuracy and reliability of CS based repairing require an advance strategy with higher efficiency and more agility. To respond to this need, in this article, a concept on the development of a knowledge based intelligent CS repairing framework is presented. The framework includes a 3D scanning system for providing the information needed on the partially damaged part to repair, a dynamic defect repairing knowledge base for providing related standard defect geometry repairing strategy, including matching the standardized defect geometry, machining pre-treatment, CS toolpath, CS parameter setting, etc., and a CS additive repair system involving robotic repair trajectory programming, simulation and material deposition. Based on the proposed framework, the design of intelligent CS additive repair system and its flow are explained. The novel repair strategy and method proposed in this article can become a model for the metal repair industry in the future.

Purpose The manufacturability of extremely fine porous structures in the SLM process has rarely been investigated, leading to unpredicted manufacturing results and preventing steady medical or industrial application. The research objective is to find out the process limitation and key processing parameters for printing fine porous structures so as to give reference for design and manufacturing planning. Design/methodology/approach In metallic AM processes, the difficulty of geometric modeling and manufacturing of structures with pore sizes less than 350 μm exists. The manufacturability of porous structures in selective laser melting (SLM) has rarely been investigated, leading to unpredicted manufacturing results and preventing steady medical or industrial application. To solve this problem, a comprehensive experimental study was conducted to benchmark the manufacturability of the SLM process for extremely fine porous structures (less than 350 um and near a limitation of 100 um) and propose a manufacturing result evaluation method. Numerous porous structure samples were printed to help collect critical datasets for manufacturability analysis. Findings The results show that the SLM process can achieve an extreme fine feature with a diameter of 90 μm in stable process control, and the process parameters with their control strategies as well as the printing process planning have an important impact on the printing results. A statistical analysis reveals the implicit complex relations between the porous structure geometries and the SLM process parameter settings. Originality/value It is the first time to investigate the manufacturability of extremely fine porous structures of SLM. The method for manufacturability analysis and printing parameter control of fine porous structure are discussed.

Purpose This paper aims to introduce a new nesting scheme to better describe and solve the single-layer-part packing problem in additive manufacturing (AM). Design/methodology/approach Parallel nesting scheme using two-dimensional (2D) changeable projection profiles is developed. At first, a feature-based orientation optimization method is used to identify a set of practical alternative build orientations for each part to ensure the part quality. Then, 2D polygons are used to represent each part’s projection profiles under its alternative build orientations. Finally, a parallel layout searching algorithm is developed to identify the optimal part layout by using 2D changeable projection profiles. Findings The proposed nesting scheme can both guarantee the production quality for each part and search the optimal part layout with larger probability but less computational time. Originality/value With the use of changeable 2D projection profiles, this method conducts 2D computation to solve the single-layer-part packing problem with five degrees of freedom, which saves much computation cost and, at the same time, guarantees the production quality of each part. By adding specific nesting objectives or constraints and heuristic searching knowledge to the proposed nesting scheme, practical nesting software can be developed to meet the specific nesting or packing requirements for industrial AM machines.

Purpose The purpose of this study is to test the concept of a relatively low cost but biocompatible customized surgical guide printing method using a new composite material for the FDM process to support accurate virtual model reconstruction in CT. Design/methodology/approach Current additive manufacturing printed surgical guides have problems of scanning artifacts or low computed tomography (CT) values for virtual model reconstruction in CT-assisted surgical operations. These tools always face difficulties in precise positioning due to the effect of human soft tissues and manually made unstable landmarks. To solve this problem, this paper proposes a modified material, polyetheretherketone powder mixed with barium sulfate powder, for printing customized surgical guides with relatively low cost to support a synchronized scanning strategy, for the accurate reconstruction of human tissues and in vitro models. Findings A set of benchmarking experiments and clinical simulation cases were conducted. The results showed that the proposed solution can be used to print surgical guides to form stable and clear CT graphs for three-dimensional digital model reconstruction. Human tissues and in vitro models can be accurately reconstructed using clear CT graphs without any scanning artifacts or difficulties in image segmentation for virtual model reconstruction, thus facilitating accurate operation guidance and positioning. Originality/value This method has wide application potential for printing modular or customized surgical guides with low cost and reusability, especially for surgical operations using CT-assisted navigation systems in underdeveloped regions where medical device costs are a critical issue.

Purpose The use of continuous fiber-reinforced filaments improves the mechanical properties obtained with the fused filament fabrication (FFF) process. Yet, there is a lack of simulation tailored tools to assist in the design for additive manufacturing of continuous fiber composites. To build such models, a precise elastic model is required. As the porosity caused by interbead voids remains an important flaw of the process, this paper aims to build an elastic model integrating this aspect. Design/methodology/approach To study the amount of porosity, which could be a failure initiator, this study proposes a two step periodic homogenization method. The first step concerns the microscopic scale with a unit cell made of fiber and matrix. The second step is at the mesoscopic scale and combines the elastic material of the first step with the interbead voids. The void content has been set as a parameter of the model. The material models predicted with the periodic homogenization were compared with experimental results. Findings The comparison between periodic homogenization results and tensile test results shows a fair agreement between the experimental results and that of the numerical simulation, whatever the fibers’ orientations are. Moreover, a void content reduction has been observed by increasing the crossing angle from one layer to another. An empiric law giving the porosity according to this crossing angle was created. The model and the law can be further used for design evaluation and optimization of continuous fiber-reinforced FFF. Originality/value A new elastic model considering interbead voids and its variation with the crossing angle of the fibers has been built. It can be used in simulation tools to design high performance fused filament fabricated composite parts.