Invisible Customisation - A Data Driven Approach to Predictive Additive Manufacture Enabling Functional Implant Personalisation

隐形定制——一种数据驱动的预测增材制造方法，实现功能性植入物个性化

• 批准号: EP/V003356/1
• 负责人: Sophie Cox
• 金额: $ 51.56万
• 依托单位: University of Birmingham
• 依托单位国家: 英国
• 项目类别: Research Grant
• 财政年份: 2020
• 资助国家: 英国
• 起止时间: 2020 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=EP%2FV003356%2F1
• 关键词: Invisible; Customisation; Data; Driven; Approach

Additive manufacturing (AM), otherwise known as 3D printing, is enabling the production of medical implants that are customised, in terms of size and shape, to a person's skeleton. Compared with devices of a standard size, these personalised designs fit the patient better and as such offer improved aesthetics and reduce surgery times. While customisation has many benefits, the challenge is to ensure each bespoke device is made to the same quality. This is difficult because the implant shape is completely unique and may be very complex. Currently in an effort to ensure quality, researchers make lots of plain cube test samples using various manufacturing settings and then compare properties before deciding what combination to use for the real implant. This trial and error approach takes a lot of time and may not even produce very predictable devices because the optimisation is not performed on shapes that are representative of real implants. In this project we will make various design features common to medical implants (e.g. curved surfaces, screw holes) and collect key performance data during and post manufacture. By using cutting edge mathematics, we will create a network that allows us to accurately predict which manufacturing settings will produce the best quality for any design shape. This tool will help businesses to standardise production of customised medical devices in a quick and accurate manner that is not dependent on the user's knowledge. Thereby we will open up the advantages of AM to more companies and help existing adopters to meet the standardisation requirements of the impending new Medical Device Regulations. Overall this project aims to better understand the relationships between additive manufacturing settings and implant properties, which will help us to improve the quality of these anatomically personalised devices. Beyond this we plan to create a tool to enable the creation of implants that are not only customised to the size and shape of the patient's skeleton but also two critical functionalities: mechanical strength and cell adhesion. It is known that if an implant is too strong compared with the surrounding native bone this can cause it to fail. As such, developing a way to select manufacturing or design parameters that enable mechanical matching to the patient's skeleton will help implants to last longer and reduce the number of failures. Besides mechanical mismatch, the other biggest threat to bone implants is infection. Our preliminary work has shown that surface roughness directly impacts the ability of cells, mammalian and bacterial, to stick onto AM devices. In this project we will exploit this knowledge to enable users to select manufacturing settings that result in a defined surface roughness that either enables or prevents cell attachment. This novel capability could be used, for example to create implants with a surface that stops bacterial cells from sticking and thus minimises infection risks. There is also potential that this tool could help to improve bonding between the implant and native tissue by recommending manufacturing settings that result in surface topographies that encourage growth of bone forming osteoblast cells. In summary, this project is focused on standardising the way we use 3D printing to ensure the properties of bespoke implants are predictable. This will be achieved by using mathematics to move the AM field away from trial and error. By understanding the relationships between manufacturing settings and key properties, we will create two tools that will enable us to make functionally personalised devices. The ability to predictively and selectively tailor mechanical properties and surface roughness will drive a new generation of implants that last longer and fail less often. Thereby, this project will ultimately improve the lives of millions of people who receive bone implants and help to reduce the associated healthcare costs.

添加制造(AM)，也被称为3D打印，使医疗植入物的生产成为可能，这些植入物的大小和形状都是根据人的骨骼定制的。与标准尺寸的设备相比，这些个性化的设计更适合患者，因此提供了更好的美感，并减少了手术时间。虽然定制有很多好处，但挑战在于确保每一款定制设备的质量都相同。这是困难的，因为植入物的形状是完全独特的，可能非常复杂。目前，为了确保质量，研究人员使用不同的制造环境制作了大量的普通立方体测试样品，然后比较性能，然后决定使用哪种组合来制作真正的植入物。这种反复试验的方法需要大量的时间，甚至可能不会产生非常可预测的设备，因为优化不是对代表真实植入物的形状进行的。在这个项目中，我们将使医疗植入物的各种设计特征具有共性(例如曲面、螺纹孔)，并收集制造过程中和制造后的关键性能数据。通过使用尖端数学，我们将创建一个网络，使我们能够准确预测哪些制造设置将为任何设计形状产生最好的质量。该工具将帮助企业以快速和准确的方式标准化定制医疗设备的生产，而不依赖于用户的知识。因此，我们将把AM的优势开放给更多的公司，并帮助现有的采用者满足即将出台的新的医疗器械法规的标准化要求。总体而言，该项目旨在更好地了解添加剂制造设置和植入物特性之间的关系，这将帮助我们提高这些解剖个性化设备的质量。除此之外，我们计划创造一种工具，不仅可以根据患者骨骼的大小和形状定制植入物，还可以根据两项关键功能进行定制：机械强度和细胞粘附性。众所周知，如果种植体与周围的天然骨相比太坚固，就会导致种植失败。因此，开发一种方法来选择制造或设计参数，使其能够与患者的骨骼进行机械匹配，将有助于植入物使用更长时间并减少失败次数。除了机械不匹配，植入物的另一个最大威胁是感染。我们的初步工作表明，表面粗糙度直接影响细胞、哺乳动物和细菌附着在AM设备上的能力。在这个项目中，我们将利用这一知识，使用户能够选择制造设置，以产生定义的表面粗糙度，从而启用或阻止电池附着。例如，这种新的能力可以用来制造具有阻止细菌细胞粘连的表面的植入物，从而将感染风险降至最低。该工具还有可能通过推荐制造环境来帮助改善植入物与天然组织之间的结合，从而产生鼓励成骨细胞生长的表面形貌。总而言之，这个项目的重点是标准化我们使用3D打印的方式，以确保定制植入物的性能是可预测的。这将通过使用数学将AM领域从试验和错误中移走来实现。通过了解制造设置和关键属性之间的关系，我们将创建两个工具，使我们能够制造功能个性化的设备。预测性和选择性地定制机械性能和表面粗糙度的能力将推动新一代植入物寿命更长、失败次数更少。因此，该项目最终将改善数百万接受骨移植的人的生活，并有助于降低相关的医疗成本。

项目成果

Stakeholder Perspectives on the Current and Future of Additive Manufacturing in Healthcare.

• DOI: 10.18063/ijb.v8i3.586
• 发表时间: 2022
• 期刊: INTERNATIONAL JOURNAL OF BIOPRINTING
• 影响因子: 8.4
• 作者: Villapun, Victor M.;Carter, Luke N.;Avery, Steven;Gonzalez-Alvarez, Alba;Andrews, James W.;Cox, Sophie
• 通讯作者: Cox, Sophie

Surface Free Energy Dominates the Biological Interactions of Postprocessed Additively Manufactured Ti-6Al-4V.

• DOI: 10.1021/acsbiomaterials.2c00298
• 发表时间: 2022-10-10
• 期刊: ACS BIOMATERIALS SCIENCE & ENGINEERING
• 影响因子: 5.8
• 作者: Puzas, Victor Manuel Villapun;Carter, Luke N.;Schroder, Christian;Colavita, Paula E.;Hoey, David A.;Webber, Mark A.;Addison, Owen;Shepherd, Duncan E. T.;Attallah, Moataz M.;Grover, Liam M.;Cox, Sophie C.
• 通讯作者: Cox, Sophie C.

Exploring the duality of powder adhesion and underlying surface roughness in laser powder bed fusion processed Ti-6Al-4V

• DOI: 10.1016/j.jmapro.2022.06.057
• 发表时间: 2022-09-01
• 期刊: JOURNAL OF MANUFACTURING PROCESSES
• 影响因子: 6.2
• 作者: Carter, Luke N.;Villapun, Victor M.;Cox, Sophie C.
• 通讯作者: Cox, Sophie C.