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

增材制造（AM），也称为3D打印，能够生产在尺寸和形状方面根据人体骨骼定制的医疗植入物。与标准尺寸的器械相比，这些个性化设计更适合患者，因此提供了更好的美观性并缩短了手术时间。虽然定制有很多好处，但挑战在于确保每个定制设备都具有相同的质量。这是困难的，因为植入物形状是完全独特的，并且可能非常复杂。目前，为了确保质量，研究人员使用各种制造环境制作了大量普通立方体测试样本，然后在决定使用哪种组合来制作真实的植入物之前比较其性能。这种试错方法花费大量时间，甚至可能无法产生非常可预测的装置，因为优化不是对代表真实的植入物的形状执行的。在本项目中，我们将制作各种医疗植入物常见的设计特征（例如曲面、螺孔），并在制造过程中和制造后收集关键性能数据。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些制造设置将为任何设计形状产生最佳质量。该工具将帮助企业快速准确地生产定制医疗设备，而不依赖于用户的知识。因此，我们将向更多的公司开放AM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，该项目旨在更好地了解增材制造设置和植入物特性之间的关系，这将有助于我们提高这些解剖学个性化设备的质量。除此之外，我们计划创建一种工具，以创建不仅根据患者骨骼的大小和形状定制的植入物，而且还具有两个关键功能：机械强度和细胞粘附。众所周知，如果植入物与周围的天然骨相比太强，这可能导致其失败。因此，开发一种方法来选择制造或设计参数，使机械匹配到病人的骨骼将有助于植入物持续更长的时间，并减少故障的数量。除了机械不匹配，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，坚持到AM设备的能力。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，导致一个定义的表面粗糙度，无论是启用或防止细胞附着。这种新的能力可以用来制造植入物，其表面可以阻止细菌细胞粘附，从而最大限度地降低感染风险。该工具也有可能通过推荐产生促进骨形成成骨细胞生长的表面形貌的制造设置来帮助改善植入物和天然组织之间的结合。总之，该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将AM场从试错中移开来实现。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持续时间更长，故障更少。因此，该项目将最终改善数百万接受骨植入物的人的生活，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.