<p>Advances in additive manufacturing technologies enable the rapid, high-throughput generation of mechanically soft microelectromechanical devices with tailored designs for many applications spanning from optical to biomedical applications. These devices can be softly interfaced with biological tissues and mechanically fragile systems, which enables to open up a whole new range of applications. However, the scalable production of these devices faces a significant challenge due to the complexity of the microfabrication process and the intolerable thermal, chemical, and mechanical conditions of their flexible polymeric substrates. To overcome these limitations, I have developed a set of advanced additive manufacturing technologies enabling (1) mechanics-driven manufacturing of quasi-three-dimensional (quasi-3D) nanoarchitectures with arbitrary substrate materials and structures; (2) repetitive replication of quasi-3D nanoarchitectures for infrared (IR) bandpass filtering; (3) electrochemical reaction-driven delamination of thin-film electronics over wafer-scale; (4) rapid custom printing of soft poroelastic materials for biomedical applications. </p> <p>First, I have developed a new mechanics-driven nanomanufacturing method enabling large-scale production of quasi-3D plasmonic nanoarchitectures that are capable of controlling light at nanoscale length. This method aims to eliminate the need for repetitive uses of conventional nanolithography techniques that are time- and cost-consuming. This approach is innovative and impactful because, unlike any of the conventional manufacturing methods, the entire process requires no chemical, thermal, and mechanical treatments, enabling a large extension of types of receiver substrate to nearly arbitrary materials and structures. Pilot deterministic assembly of quasi-3D plasmonic nanoarrays with imaging sensors yields the most important advances, leading to improvements in a broad range of imaging systems. Comprehensive experimental and computational studies were performed to understand the underlying mechanism of this new manufacturing technique and thereby provide a generalizable technical guideline to the manufacturing society. The constituent quasi-3D nanoarchitectures achieved by this manufacturing technology can broaden considerations further downscaled plasmonic metamaterials suggest directions for future research.</p> <p>Second, I have developed mechanics-driven nanomanufacturing that provides the capability to repetitively replicate quasi-3D plasmonic nanoarchitectures even with the presence of an extremely brittle infrared-transparent spacer, such as SU-8, thereby manipulating IR light (e.g., selectively transmitting a portion of the IR spectrum while rejecting all other wavelengths). Comprehensive experimental and computational studies were performed to understand the underlying nanomanufacturing mechanism of quasi-3D plasmonic nanoarchitectures. The spectral features such as the shape of the transmission spectrum, peak transmission and full width at half maximum (FWHM), etc. were studied to demonstrate the bandpass filtering effect of the assembled quasi-3D plasmonic nanoarchitecture.</p> <p>Third, I have developed an electrochemical reaction-driven transfer printing method enabling a one-step debonding of large-scale thin-film devices. Conventional transfer printing methods have critical limitations associated with an efficient and intact separation process for flexible 3D plasmonic nanoarchitectures or bio-integrated electronics at a large scale. The one-step electrochemical reaction-driven method provides rapid delamination of large-scale quasi-3D plasmonic nanoarchitectures or bio-integrated electronics within a few minutes without any physical contact, enabling transfer onto the target substrate without any defects and damages. This manufacturing technology enables the rapid construction of quasi-3D plasmonic nanoarchitectures and bio-integrated electronics at a large scale, providing a new generation of numerous state-of-art optical and electronic systems.</p> <p>Lastly, I have developed a new printing method enabling the direct ink writing (DIW) of multidimensional functional materials in an arbitrary shape and size to rapidly prototype stretchable biosensors with tailored designs to meet the requirement of adapting the geometric nonlinearity of a specific biological site in the human body. Herein, we report a new class of a poroelastic silicone composite that is exceptionally soft and insensitive to mechanical strain without generating significant hysteresis, which yields a robust integration with living tissues, thereby enabling both a high-fidelity recording of spatiotemporal electrophysiological activity and real-time ultrasound imaging for visual feedback. Comprehensive <i>in vitro</i>, <i>ex vivo</i>, and <i>in vivo</i> studies provide not only to understand the structure-property-performance relationships of the biosensor but also to evaluate infarct features in a murine acute myocardial infarction model. These features show a potential clinical utility in the simultaneous intraoperative recording and imaging on the epicardial surface, which may guide a definitive surgical treatment.</p>

The complexity of components fabricated in today's industry is ever increasing. This increase is partly due to market pressure but it is also a result from progress in fabrication technologies that opens up new possibilities. The increased use of additive manufacturing and multi-material systems, especially, has driven the complexity of parts to new heights. The new complex material systems brings benefits in many areas such as; mechanical properties, weight optimisation, and sustainability. However, the increased complexity also makes material integrity investigations and dimensional control more difficult. In additive manufacturing, for example, internal features can be fabricated which cannot be seen or measured with conventional tools. There is thus a need for non-destructive inspection methods that can measure these geometries. Such a method is X-ray computed tomography. Computed tomography utilizes the X-rays ability to penetrate material to create 3D digital volumes of components. Measurements and material investigations can be performed in these volumes without any damage to the investigated component. However, computed tomography in material science is still not a fully mature method and there are many uncertainties associated with the investigation technique. In the work presented in this thesis geometries fabricated by various additive manufacturing processes have been investigated using computed tomography. Also in this work, a dual-energy computed tomography tool has been developed with the aim to increase the measurement consistency of computed tomography when investigating complex geometries and material combinations. / MultiMatCT

Additive manufacturing (AM) allows building complex shapes with high accuracy. The X-ray Computed Tomography (XCT) is one of the promising non-destructive evaluation techniques for the evaluation of subsurface defects in an additively manufactured component. Automatic defect detection and segmentation methods can assist part inspection for quality control. However, automatic detection and segmentation of defects in XCT data of AM possess challenges due to contrast, size, and appearance of defects. In this research different deep learning techniques have been applied on publicly available XCT image datasets of additively manufactured cobalt chrome samples produced by the National Institute of Standards and Technology (NIST). To assist the data labeling image processing techniques were applied which are median filtering, auto local thresholding using Bernsen’s algorithm, and contour detection. A convolutional neural network (CNN) based state-of-art object algorithm YOLOv5 was applied for defect detection. Defect segmentation in XCT slices was successfully achieved applying U-Net, a CNN-based network originally developed for biomedical image segmentation. Three different variants of YOLOv5 which are YOLOv5s, YOLOv5m, and YOLOV5l were implemented in this study. YOLOv5s achieved defect detection mean average precision (mAP) of 88.45 % at an intersection over union (IoU) threshold of 0.5. And mAP of 57.78% at IoU threshold 0.5 to 0.95 using YOLOv5M was achieved. Additionally, defect detection recall of 87.65% was achieved using YOLOv5s, whereas a precision of 71.61 % was found using YOLOv5l. YOLOv5 and U-Net show promising results for defect detection and segmentation respectively. Thus, it is found that deep learning techniques can improve the automatic defect detection and segmentation in XCT data of AM.

The conventional manufacturing methods, such as milling and turning, are reliable methods which makes them commonly used. These methods are capable of producing high precision details with high surface quality. But there are drawbacks and limitations. Many of these methods are expensive and require high production volume to be profitable. The design constraints for conventional manufacturing methods are usually determined by the machining tools, which restrict shape and form.Additive manufacturing methods have gained high attraction in the last decades as an alternative to conventional methods. With greater design freedom, ease to use interface and minimal material waste, cost and time usage can be minimized. But changing the production process requires high knowledge of capability and limitations for these methods. This step is critical to assure the right detail and quality requirements. Additive manufacturing at present is limited to material selection, low precision and possibility of reduced mechanical properties. This report aims to investigate additive manufacturing and the possibility of replacing conventional manufacturing of tools and products. / De traditionella tillverkningsmetoderna (TT), exempelvis fräsning eller svarvning, är något många företag använder sig av för att bearbeta fram sina produkter. Dessa processer ger hög precision, snäva toleranser och en god ytfinish vid tillverkning. Men som för alla processer finns även nackdelar och begränsningar. Många av dessa processer är väldigt dyra och kräver större tillverkningsvolymer för att bli lönsamma. De har även begränsningar i frihet vid formgivning och kräver bland annat större kunskap hos operatören och leder till materialspill. På senare tid har additiva tillverkningsmetoder (AT) lockat många tillverkare för att byta ut de traditionella processerna. Med sin frihet i formgivning, enkelhet och diversitet ser man potential i att spara både tid och pengar. Men att byta ut nuvarande produktionsmetod kräver god förståelse av den nya metodens kapacitet och begränsningar. Detta för att kunna säkerställa kvalitetskrav för det tänkta arbetet. AT i dagens läge är begränsad till materialval, ger långt ifrån de snäva toleranser som kan åstadkommas med traditionella tillverkningsmetoderna, och kan påverka de mekaniska egenskaperna hos den tillverkade detaljen negativt. Vissa AT metoder kräver dessutom höga investeringskostnader och det visar sig inte alltid vara mer lönsamt mot traditionella tillverkningsmetoder. I detta arbete ska just detta undersökas och huruvida AT kan konkurrera med traditionella tillverkningsmetoder, samt hur AT kan säkerställa de krav som finns på befintlig produktion av detaljer. Detta arbete är en förstudie om additiv tillverkning och dess möjligheter att ersätta befintlig produktion av verktyg och produkter.

<p>  </p> <p>Sensing systems have evolved significantly in recent years as a result of several advances in a number of sensor manufacturing approaches. The proximity measuring of approaching objects is a challenging, costly, and critical operation that permits the detection of any impediments without coming into touch with them and causing an unfavorable occurrence. However, developing a flexible proximity sensor capable of operating throughout a wide range of object motion continues to be a difficulty. The current work describes a polymer-based sensor that makes use of a nanostructure composite as the sensing element. The sensor will be used in healthcare and automotive applications in the near future. Composites comprising Thermoplastic Polyurethane (TPU) and Carbon Nanotubes (CNTs) are capable of sensing the presence of an external item at a great distance. The sensor model's performance was then enhanced further by microfabricating an integrated model with a certain shape. The design and production techniques for the TPU/CNTs proximity sensor are basic, and the sensor's performance demonstrates repeatability, as well as high electrical sensitivity and mechanical flexibility. The sensing process is based on the comparison of stored charges at the composite film sensor to the sensor's base voltage. The sensor operates reliably across a detection range of 2-20 cm. Tunneling and fringing effects are used to explain substantial capacitance shifts as sensing mechanisms. The structure's fringing capacitance effect has been thoroughly examined using ANSYS Maxwell (Ansoft) FEA simulation, as the measurements perfectly confirm the simulation's sensitivity trend. A novel mathematical model of fringe capacitance and subsequent tests demonstrate that the distance between an item and the sensor may be determined. Additionally, the model argues that the change in capacitance is significantly influenced by sensor resistivity, with the starting capacitance varying between 0.045pF and 0.024pF in the range 103-105 mm. This analytical model would enable the sensor's sensitivity to be optimized.</p> <p>Additionally, a new generation of durable elastomeric materials is commercially accessible for 3D printing, allowing the development of an entirely new class of materials for wearable and industrial applications. By using functional grading and adjusting to diverse users, the mechanical reaction of soft 3D-printed objects may now be modified for increased safety and comfort. Additionally, electronics may be included into these 3D printed lattice and wearable structures to offer input on the movement of objects associated with healthcare devices as well as automotive components. Thus, in order to investigate the influence of additive manufacturing on the sensitivity of TPU/CNT sensors, samples with equal thickness and size but varied orientations are printed and compared to hot-press samples. Among the many 3D printed patterns, the [0,0] direction has the highest sensitivity, and may be used as an optimum method for increased sensitivity. In contrast to the hot-press samples, the 3D-printed TPU/CNT film features a crystalline network, which may aid in the passage of surface charges and hence increase capacitance changes.</p> <p>To have a better understanding which feature, and parameter can give us the most sensitivity we need to do an optimization. This will be accomplished by collecting experimental and computational results and using them as a basis for establishing a computationally and experimentally supported Genetic Algorithm Assisted Machine Learning (GAML) framework combined with artificial neural network (ANN) to develop TPU/CNT nanocomposite flexible sensors in which material characterizations will be coupled to strain, tactile, electronic and proximity characteristics to probe intermolecular interactions between CNTs and polymers. The proposed framework provides enhanced predictive capabilities by managing multiple sets of data gathered from physical testing (material characterization and sensor testing) and multi-fidelity numerical models spanning all lengths scales. The GAML-ANN framework will allow the concurrent optimization of processing parameters and structural features of TPU/CNT nanocomposites, enabling fabrication of high-performance, lightweight flexible sensor systems.</p> <p>Our suggested nanocomposite sensor establishes a new mainstream platform for ultrasensitive object perception, demonstrating a viable prototype for wearable proximity sensors for motion analysis and the automobile sector.</p>

In industrial design, additive manufacturing technology is one of the key technologies that have changed the way of producing metal component parts on short demand. Because of competitiveness among industries and the requirement to keep up with thegrowth of thesmart factory technology, the industries are pushed to step up and take further steps towards industry 4.0. Today the AM technology is used at prototype scale, but previous literature says that for the technology to reach the full capacity, it needs to be scaled up. Previous literature shows that improvements in the supply chain are necessary in order to scale up the industrial production and achieve high-scale adoption of the technology. As there are few sourcesin the literature about AM scalability or finding critical improvements in terms of lead times, costs and material consumptions, this study will fill that gap. The main objective of this research is to study small-scale 3D printing in the AM industries with two main industrial objectives in mind: 1 –Understanding the problem of optimization of a small-scale 3D printing operation in the industry and 2 –projecting a scenario regarding the scaling up of such facilities to reach full industrial production capacity. The method used for finding improvements in the additive manufacturing supply chain was optimization. I have developed the Overall Material Flow Effectiveness model (OMFE), which is an optimization model that takes into consideration the relevantparameters of the AM material flow regarding lead times, costs and material consumption. A literature review was conducted to determine the research design and what has and not been investigated. A sensitivity analysis was performed, which provided information aboutissues of scale, size and significance of optimizing a prototyping model,andalso aboutanalyzing the optimization model development in terms of evaluating the prototyping, making it better and scaling up to high-level production. The optimal material flow of the AM industry is a scaled-up production with implemented improvements regarding transport and cost. By comparing it with the current prototype production, it is possible to identifythat all of the OMFE related factors have higher percentages. The top losses within the current AM industry are related to non-human processes. The most significant optimization loss is the loss of transport, where the time from supplier to goods reception have a significant influence. The second largestlossis cost,generated bylabour management.

Selective laser melting (SLM) is a method of additive manufacturing that has become increasingly popular in recent years for fabricating complex components, especially in the medical and aerospace industries. By fabricating components in a layerwise fashion, SLM provides users the freedom to design components based on their desired functionality rather than their manufacturability. The current state-of-the-art for SLM is limited though, as defects induced by the SLM process have proven to greatly alter the material properties of fabricated parts. In addition, traditional post-process nondestructive inspection methods have experienced significant difficulty in accurately detecting these process-induced defects. Therefore, the objective of this study is to investigate methods of processing and analysis for optical in-situ monitoring data recorded during SLM fabrication of six test samples. Four of the samples were designed with seeded (i.e., intentional) defects located at their center to serve as a reference defect signatures in the resulting in-situ data. An off-axis optical tomography (OT) sensor was used to capture near-infrared (NIR) melt pool emissions during the fabrication of each layer. Image analysis was subsequently performed using a custom squared difference (SD) operator to enhance defect signatures in the OT data. Results from the SD operator were then used to perform k-means clustering to partition the data into k relevant clusters, where the optimal number of k clusters for each image is employed as metric for detecting the onset of defects in the samples. By employing OT image data from samples containing seeded intentional defects, the k-means clustering approach was investigated as a method of defect detection for the in-situ OT images. Results showed that the SD operator is capable of elucidating anomalous signatures in the in-situ data. However, variations within the SD distributions ultimately limited detection capabilities as the output from k-means clustering was unable to accurately distinguish the seeded defects from the fused regions of material.

This paper explores the adoption of additive manufacturing technology in the process industries and tries to provide a holistic view about the preference and scope of this technology in the process industry sector. There has been vast literature about use of this technology in the automobile, aerospace, and medical sector. This study will help us to understand how Additive Manufacturing technology is shaping the other process industries and explore if it has same significance. To address the research questions qualitative research method is used in this study with semi-structured interviews with the respondents in process industries and Additive Manufacturing suppliers. All respondents are selected using purposive sampling and remote interviews were conducted with them.The first finding of this study was that additive manufacturing can neither be stated directly as radical or disruptive innovation because this technology has shown both radical as well as disruptive changes in the process industry. Secondly, this technology is adopted in the process industry based on the three innovation attributes: relative advantage, trialability, and observability. Following this, there is discussion on important barriers and how companies are taking efforts to overcome this barrier and adopt this technology easily. Further, this study implies that there is still an immense scope to explore this technology to reap its full benefits. This study gives understanding to AM suppliers that small-scale firms in process industry could be a possible direction to explore for more business opportunities apart from automobile and aerospace industry. For potential researchers in additive manufacturing, this study stands to give understanding for adoption pattern and innovation attributes for which it is valued.

Additive manufacturing (AM), also called 3-dimensional printing (3DP), emerged as a disruptive technology affecting multiple organizations' business models and supply chains and endangering incumbents' financial health, or even rendering them obsolete. The world market for products created by AM has increased more than 25% year over year. Using Christensen's theory of disruptive innovation as a conceptual framework, the purpose of this multiple case study was to explore the successful strategies that 4 individual managers, 1 at each of 4 different light and high-tech manufacturing companies in the Netherlands, used to adopt AM technology into their business models. Participant firms originated from 3 provinces and included a value-added logistics service provider and 3 machine shops serving various industries, including the automotive and medical sectors. Data were collected through semistructured interviews, member checking, and analysis of company documents that provided information about the adoption of 3DP into business models. Using Yin's 5-step data analysis approach, data were compiled, disassembled, reassembled, interpreted, and concluded until 3 major themes emerged: identify business opportunities for AM technology, experiment with AM technology, and embed AM technology. Because of the design freedom the use of AM enables, in combination with its environmental efficiency, the implications for positive social change include possibilities for increasing local employment, improving the environment, and enhancing healthcare for the prosperity of local and global citizens by providing potential solutions that managers could use to deploy AM technology.

This research explores the use of two additive manufacturing processes for the fabrication of multi-material structures. Ultrasonic consolidation (UC) and laser- engineered net shaping (LENS) processes were used for parallel systematic investigations of the process parameters and methodologies for the development of multi-material structures. The UC process uses ultrasonic energy at low temperature to bond metallic foils. A wide range of metallic materials including nickel; titanium; copper; molybdenum; tantalum; MetPreg®; silver; stainless steel; and aluminum alloys 1100, 3003, and 6061 were bonded in different combinations. Material domains are inherently discrete in ultrasonically consolidated structures. The mechanical properties of some of the bonded structures were characterized to lay the groundwork for their real-life applications. LENS uses a laser beam to deposit metallic powder materials for the fabrication of fully dense structures. Mechanical testing was used to characterize the flexural and tensile properties of dual-material structures made of Ti6Al4V/10wt%TiC composite and Ti6Al4V materials. Experimental results show that the strength of transition joints in multi-material structures significantly depends on the joint design. Dual-material minimum weight structures, representing geometrically and materially complex structures, were fabricated using the results of the process parameters and fabrication methodologies developed in this work. The structures performed well under loading test conditions. It shows that function-specific multi-material structures ultrasonically consolidated and LENS fabricated can perform well in real-life applications.