Personalised therapeutic drugs are the future of the medical drug sector. For manufacturers, this will require the flexibility to produce many different unique batches within a given facility. This research paper aims to demonstrate the potential of mobile collaborative robots for improving current manufacturing practices in personalised therapeutics. The benefits and challenges of introducing robots in biologics are explored, including current practices, limitations, likely future practices, and the market outlook. Experiments demonstrating the application of a mobile collaborative robot to perform three different routine tasks is presented. These experiments include the transport of centrifugal tubes, manipulation of infusion bags, and scanning of Petri dishes for environmental monitoring. The investigations highlight the potential of collaborative mobile robotic platforms for automating the routine tasks carried out within the biomanufacturing sector.

This work investigates the deposition of polytetrafluoroethylene (PTFE) onto a superelastic NiTi wire using an ambient temperature-coating technique known as CoBlast. The process utilises a stream of abrasive (Al₂O₃) and a coating medium (PTFE) sprayed simultaneously at the surface of the substrate. Superelastic NiTi wire is used in guidewire applications, and PTFE coatings are commonly applied to reduce damage to vessel walls during insertion and removal, and to aid in accurate positioning by minimising the force required to advance, retract or rotate the wire. The CoBlast coated wires were compared to wire treated with PTFE only. The coated samples were examined using variety of techniques: X-ray diffraction (XRD), microscopy, surface roughness, wear testing and flexural tests. The CoBlast coated samples had an adherent coating with a significant resistance to wear compared to the samples coated with PTFE only. The XRD revealed that the process gave rise to a stress-induced martensite phase in the NiTi which may enhance mechanical properties. The study indicates that the CoBlast process can be used to deposit thin adherent coatings of PTFE onto the surface of superelastic NiTi.

Additive manufacturing workflows generate large amounts of data in each phase, which can be very useful for monitoring process performance and predicting the quality of the finished part if used correctly. In this paper, a framework is presented that utilises machine learning methods to predict porosity defects in printed parts. Data from process settings, in-process sensor readings, and post-process computed tomography scans are first aligned and discretised using a voxelisation approach to create a training dataset. A multi-step classification system is then proposed to classify the presence and type of porosity in a voxel, which can then be utilised to find the distribution of porosity within the build volume. Titanium parts were printed using a laser powder bed fusion system. Two discretisation techniques based on voxelisation were utilised: a defect-centric and a uniform discretisation method. Different machine learning models, feature sets, and other parameters were also tested. Promising results were achieved in identifying porous voxels; however, the accuracy of the classification requires improvement before being applied industrially. The potential of the voxelisation-based framework for this application and its ability to incorporate data from different stages of the additive manufacturing workflow as well as different machine learning models was clearly demonstrated.