Browsing by Author "Partanen, Jouni, Prof., Aalto University, Finland"

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Bioactive Patient-Specific Implants for Regeneration of Critical Size Bone Defects
(2023) Dienel, Kasper
School of Chemical Technology | Doctoral dissertation (article-based)
Bone possesses the ability to spontaneously heal itself. Traumatic injury or tumor resection can lead to a bone defect, a lack of bone where it should normally exist. If the deficit is larger than a diagnostic limit, the defect is said to be of critical size, therefore requiring clinical intervention. In such cases autologous bone, or a bioactive synthetic ceramic resembling the mineral component of bone, is used to fill the defect. Additive manufacturing (AM) of bone tissue engineering scaffolds presents an adaptable method for fabrication of patient-specific implants for the same clinical reconstruction. In this thesis polymer/tricalcium phosphate (TCP) composites for bone regeneration scaffolds were studied with the ultimate goal of manufacturing large implants for craniomaxillofacial reconstruction. Such a materials should possess physico-chemical properties optimal for inducing bone growth while being suitable for AM. Within the work two very different methods of AM and therefore also two unique polymer groups were investigated. Poly(trimethylene carbonate) (PTMC) was synthetized for preparing resins for vat photopolymerization. PTMC/TCP composite scaffolds with varying ceramic ratio were characterized to evaluate their performance. The encouraging results showed that large amounts of a TCP could be incorporated into the scaffolds, therefore reinforcing the biocompatible scaffold and turning it bioactive. The AM method allows full control over scaffold design for optimal bone regeneration enabling fine pore architectures and a bioactive surface of TCP with a microscale topographical surface roughness. The process was subsequently upscaled and augmented for consistent manufacturing of large patient-specific implants. Following successful initial screening the composite scaffolds were tested in vivo in two animal models including cranial and tibia defect in rabbits and proof-of-concept pre-clinical study in the mandible of minipigs. Results in the small animal model showed promising results showing that scaffolds provide a conductive surface that induces bone formation. The minipig study confirmed these findings, but PTMC/TCP scaffolds were associated with elevated incidence of infection likely due to high local concentrations of TCP. Therefore, the results point out an intricate balance between biocompatibility and bioactivity. As an alternative method, well-established and commercially readily available medical grade poly(L-lactide-co-D,L-lactide) and poly(L-lactide-co-glycolide) were evaluated in composites with TCP for fused filament fabrication. Comparable scaffolds could successfully be manufactured and the general properties were promising. However, based on further evaluation of existing clinical data and considering the specific clinical application, some challenges remain and potential risks need to be recognized.
Implications of additive manufacturing processes and design complexity on long-term material behavior
(2023) Puttonen, Tuomas
School of Engineering | Doctoral dissertation (article-based)
Additive manufacturing (AM) technologies have developed rapidly from the 1980s prototyping applications and are becoming an established manufacturing method of today. In contrast to traditional manufacturing, AM enables producing parts with unprecedented levels of geometrical complexity. The increased freedom of design translates into functional, operational, and economic benefits. However, the AM principle of material addition and solidification in layers creates material anisotropies. Fast cooling rates and complex thermal histories result in unique microstructures, in-process defects, and residual stresses. Furthermore, the advantage of geometrical complexity is not without consequences. The size and shape of part features inversely influence the manufacturing process. Fine features in geometrically complex parts can induce local microstructure variation and lead to unexpected failure modes. Extending the application space and part optimization potential of AM to safety-critical components requires a better understanding of how complex geometries and process-induced effects may influence the long-term environmental durability of end-use parts. The dissertation contributes to the cause with exploratory studies where the environmental durability of AM materials is assessed in the context of plastic material weathering and metal corrosion. In addition, a large part of the contribution is linked to method development for microcomputed X-ray tomography. The work focuses on powder bed fusion, a technique most prevalently used across industries. The AM process and part orientation influence on plastic degradation is studied with accelerated weathering, tensile testing, and fractography on simple geometries. In the study of corrosion, the evaluation is extended to geometrical complexity. Lattice structures, with identical units repeating in three dimensions, are employed to reveal any relations between part size or shape with corrosion susceptibility. The weathering durability of AM plastics was not found to alter significantly due to a varying build orientation. In contrast, the corrosion experiments suggested a higher susceptibility to localized corrosion for the finest wall-thickness lattice structures, although, noise sources in the novel experimental setup hindered numerical validation. In this regard, the method requires further development. Nevertheless, the time-lapse microcomputed X-ray tomography workflow enables localization, indexing, and characterization of minute corrosion-induced changes. Furthermore, the same method is applicable to other research fields.
Switchover to additive manufacturing: Dynamic decision-making for accurate, personalized and smart end-use parts
(2022) Akmal, Jan Sher
School of Engineering | Doctoral dissertation (article-based)
Additive manufacturing (AM) is rapidly developing into a general-purpose technology akin to electric drives and computers serving a plethora of applications. The advent and proliferation of the additive process triggering Industry 4.0 is encouraging academics and practitioners to establish new practices, designs, and modes of creating and supplying end-use parts. Contributing to this emerging stream of research on AM technologies, the overarching objective of this doctoral dissertation is to discover situations and ways in which companies can benefit from implementing AM in conjunction with conventional manufacturing technologies. This is addressed and limited by three sub-objectives. First sub-objective establishes a new operational practice—dynamic supplier selection using the build-to-model mode of manufacturing—for the provision of idiosyncratic spare parts to improve the after-sales operations of a case company. Second sub-objective estimates the combined uncertainty and the worst-case error in creating an end-use part, particularly a personalized implant made by radiologic images, thresholding, digital design, and AM. Third sub-objective develops process interruption-based embedding and creates prototypes of smart parts, in particular intelligent implants using four AM technologies. The work uses a multi-methods approach combining three case studies, experiments, and research methodologies to achieve the aim of theoretical insights, practical relevance, and innovation. The empirical evidence confirms that AM can radically shift the performance frontier for problematic parts in conventional supply. The dynamic supplier selection practice allows operations managers to choose a supplier or multiple suppliers for idiosyncratic parts both existing and new. The selection can be based on cost reduction, lead-time reduction, and trade-offs in cost and lead-time according to customer requirements without significant transaction costs. The generative mechanism of successful outcome is triggered by the simplicity in AM process instructions. Encapsulating the design and production-process instructions reduces mundane transaction costs and enables highly interactive model-based supplier relationships for decentralized manufacturing. The accuracy of AM technologies is predominant for establishing and substantiating appropriate practices. The process interruption-based embedding opens a direction for creating smart parts facilitating condition monitoring, machine learning, and preventive maintenance for Industry 4.0. This doctoral dissertation aids researchers and practitioners in switching parts over to AM technologies from large spare part repositories with a dynamic response as opposed to a static choice with conventional manufacturing involving increasing minimum order quantities, costs, and lead-times. It can allow a dynamic response for accurate, personalized, and smart end-use parts.