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

增材制造（AM），也被称为3D打印，使医疗植入物的生产能够根据人的骨骼在尺寸和形状方面进行定制。与标准尺寸的设备相比，这些个性化设计更适合患者，因此提供了更好的美学和减少手术时间。虽然定制有很多好处，但挑战在于确保每个定制设备都达到相同的质量。这很困难，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设置制作了许多普通立方体测试样品，然后在决定使用哪种组合来制作真正的植入物之前比较其性能。这种反复试验的方法需要花费大量时间，甚至可能无法产生非常可预测的设备，因为优化不是在代表真实植入物的形状上进行的。在这个项目中，我们将制作医疗植入物常见的各种设计特征（如曲面、螺钉孔），并收集制造过程中和制造后的关键性能数据。通过使用尖端的数学，我们将创建一个网络，使我们能够准确地预测哪种制造设置将为任何设计形状产生最佳质量。该工具将帮助企业以快速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，我们将向更多的公司开放增材制造的优势，并帮助现有的采用者满足即将出台的新医疗器械法规的标准化要求。总的来说，这个项目旨在更好地理解增材制造设置和植入物属性之间的关系，这将有助于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来创造植入物，不仅可以根据患者骨骼的大小和形状定制，而且还具有两个关键功能：机械强度和细胞粘附性。众所周知，如果植入物与周围的原生骨相比过于坚固，就会导致其失效。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机械匹配，将有助于植入物的使用寿命更长，并减少失败的次数。除了机械不匹配，对骨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、哺乳动物和细菌粘附在增材制造设备上的能力。在这个项目中，我们将利用这些知识，使用户能够选择制造设置，从而确定表面粗糙度，从而启用或阻止细胞附着。例如，这种新能力可以用于制造具有表面的植入物，该表面可以阻止细菌细胞粘附，从而将感染风险降至最低。该工具也有可能通过推荐制造环境来促进成骨细胞生长的表面形貌，从而帮助改善植入物与天然组织之间的结合。总之，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特性是可预测的。这将通过使用数学来移动AM领域远离试验和错误来实现。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。预测和选择性定制机械性能和表面粗糙度的能力将推动新一代植入物的使用寿命更长，故障更少。因此，该项目最终将改善数百万接受骨植入的人的生活，并有助于降低相关的医疗成本。

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.