While additive manufacturing has opened interesting avenues to reimagine manufacturing as a service (MaaS) platform, transmission of design files from client to manufacturer over networks opens up many cybersecurity challenges. Securing client's intellectual property (IP) especially from cyber-attacks emerges as a major challenge. Earlier works introduced streaming, instead of sharing process plan (G-code) files, as a possible solution. However, executing client's G-codes on manufacturer's machines exposes them to potential malicious G-codes. This paper proposes a viable approach when the client and manufacturer do not trust each other and both the client and manufacturer want to preserve their IP of designs and manufacturing process respectively. The proposed approach is based on segmenting and streaming design (STL) files and employing a novel machine-specific STL to G-code translator at the manufacturer's site in real-time for printing. This approach secures design and manufacturing process IPs as demonstrated in a real-world implementation.
Additive manufacturing (AM; 3D Printing), a process which creates a part layer-by-layer, has the potential to improve upon conventional lightweight mirror manufacturing techniques, including subtractive (milling), formative (casting) and fabricative (bonding) manufacturing. Increased mass reduction whilst maintaining mechanical performance can be achieved through the creation of intricate lattice geometries, which are impossible to manufacture conventionally. Further, part consolidation can be introduced to reduce the number of interfaces and thereby points of failure. AM design optimisation using computational tools has been extensively covered in existing literature. However, additional research, specifically evaluation of the optical surface, is required to qualify these results before these advantages can be realised. This paper outlines the development & metrology of an AM mirror for a CubeSat platform with a targeted mass reduction of 60% compared to an equivalent solid body. This project aims to incorporate recent developments in AM mirror design, with a focus on manufacture, testing & evaluation. This is achieved through a simplified design process of a Cassegrain t
In this study, we first reviewed the traditional astrolabe design methods and identified potential sources of manufacturing error. We then proposed an analytical approach using computer assistance to develop designs for the astrolabe components. This approach marks a pioneering step toward designing and producing a physical astrolabe model aided by computer technology. Our goal was to revive this significant heritage instrument while leveraging modern techniques and software to produce astrolabe models free from traditional manufacturing inaccuracies.
Spaced-based mirrors are a developing use-case for Additive Manufacturing (AM), the process that builds a part layer-by-layer. The increased geometric freedom results in novel and advantageous designs previously unachievable. Conventionally, mirror fabrication uses subtractive (milling & turning), formative (casting) and fabricative (bonding) manufacturing methods; however, an additive method can simplify an assembly by consolidating individual components into one, and incorporating lattice structures and function optimised geometries to reduce the mass of components, which are beneficial to space-based instrumentation as mass and volume are constrained. Attention must be given to the printability of the design - build orientation and powder/resin removal from lattices and internal cavities are challenges when designing for AM. This paper will describe the design, manufacture and metrology of mirror prototypes from the Active Deployable Optical Telescope (ADOT) 6U CubeSat project. The AM mirror is 52mm in diameter, 10mm deep, with a convex 100mm radius of curvature reflective surface and deploys telescopically on three booms. The objectives of the designs were to combine the bo
Edible robotics is an emerging research field with potential use in environmental, food, and medical scenarios. In this context, the design of edible control circuits could increase the behavioral complexity of edible robots and reduce their dependence on inedible components. Here we describe a method to design and manufacture edible control circuits based on microfluidic logic gates. We focus on the choice of materials and fabrication procedure to produce edible logic gates based on recently available soft microfluidic logic. We validate the proposed design with the production of a functional NOT gate and suggest further research avenues for scaling up the method to more complex circuits.
The customized multiple arrays are becoming vastly used in microarray experiments for varies purposes, mainly for its ability to handle a large quantity of data and output high quality results. However, experimenters who use customized multiple arrays still face many problems, such as the cost and time to manufacture the masks, and the cost for production of the multiple arrays by costly machines. Although there is some research on the multiple arrays, there is little concern on the manufacture time and cost, which is actually important to experimenters. In this paper, we have proposed methods to reduce the time and cost for the manufacture of the customized multiple arrays. We have first introduced a heuristic algorithm for the mask decomposition problem for multiple arrays. Then a streamline method is proposed for the integration of different steps of manufacture on a higher level. Experiments show that our methods are very effective in reduction of the time and cost of manufacture of multiple arrays.
Continuous developments in Additive Manufacturing (AM) technologies are opening opportunities in novel machining, and improving design alternatives for modern particle accelerator components. One of the most critical, complex, and delicate accelerator elements to manufacture and assemble is the Radio Frequency Quadrupole (RFQ) linear accelerator, used as an injector for all large modern proton and ion accelerator systems. For this reason, the RFQ has been selected by a wide European collaboration participating in the AM developments of the I.FAST (Innovation Fostering in Accelerator Science and Technology) Horizon 2020 project. RFQ is as an excellent candidate to show how sophisticated pure-copper accelerator components can be manufactured by AM and how their functionalities can be boosted by this evolving technology. To show the feasibility of the AM process, a prototype RFQ section has been designed, corresponding to one-quarter of a 750 MHz 4-vane RFQ, which was optimised for production with state-of-art Laser Powder Bed Fusion (L-PBF) technology, and then manufactured in pure copper. To the best knowledge of the authors, this is the first RFQ section manufactured in the world b
A hierarchical approach to the construction of compound distributions for process-induced faults in IC manufacture is proposed. Within this framework, the negative binomial distribution is treated as level-1 models. The hierarchical approach to fault distribution offers an integrated picture of how fault density varies from region to region within a wafer, from wafer to wafer within a batch, and so on. A theory of compound-distribution hierarchies is developed by means of generating functions. A study of correlations, which naturally appears in microelectronics due to the batch character of IC manufacture, is proposed. Taking these correlations into account is of significant importance for developing procedures for statistical quality control in IC manufacture. With respect to applications, hierarchies of yield means and yield probability-density functions are considered.
Code verification is a necessary step towards establishing credibility in computational physics simulations. It is used to assess the correctness of the implementation of the numerical methods within the code, and it is a continuous part of code development. Code verification is typically performed using exact and manufactured solutions. However, exact solutions are often limited, and manufactured solutions generally require the invasive introduction of an artificial forcing term within the source code, such that the code solves a modified problem for which the solution is known. The equations for some physics phenomena, such as non-decomposing ablation, yield infinite analytic solutions, but the boundary conditions may eliminate these possibilities. For such phenomena, however, we can manufacture the terms that comprise the boundary conditions to obtain exact solutions. In this paper, we present a nonintrusive method for manufacturing solutions for non-decomposing ablation in two dimensions, which does not require the addition of a source term.
Geometric optimization is played an important to manufacture and design materials in many implementations. Therefore the choice of optimization method is of considerable significance to easily solve the problems. In this work, the ground state geometries for bisphenol A and phosgene that manufacture polycarbonate have been optimized using the Hartree-Fock method with different basis sets. The optimization results for bisphenol A and phosgene are compared with theoretical and experimental data. The obtained optimizations results have been shown that our data are in agreement with the literature and experimental data.
An innovative way of producing large-scale unalloyed tantalum parts, based on the Wire + Arc Additive Manufacturing process, has been developed in this study. Two different unalloyed tantalum wires have been used to deposit 200-mm-long structures in tantalum. The effect of the wire chemistry on microstructure, hardness, porosity, mechanical properties and strain localisation has been investigated. The deposits showed high integrity and excellent mechanical properties, with yield strength, ultimate tensile strength and elongation as high as 234 MPa, 261 MPa, and 36 %, respectively. Indeed, yield strength was higher than commercially available tantalum, even though, in this study, the grains were large and had a high aspect ratio. Wire + Arc Additive Manufacture has clearly shown the potential to produce tantalum components with relatively low cost and reduced lead time, thus offering a new robust and viable manufacturing route.
One way to solve the challenge of small and medium-sized enterprise (SME) manufacturers of acquiring sufficient orders is by joining digital Manufacturing-as-a-Service (MaaS) platforms for on-demand manufacturing. However, joining such platforms brings about new challenges such as efficient quoting handling in the face of potentially low success rates and the need for high production quality for low lot sizes. Automating the complete interaction between manufacturers and MaaS platforms, from registering the manufacturer and its capabilities to handling incoming requests and managing offers, orders, and production quality reporting, helps to overcome these challenges. Thus, the increased number of requests can be handled efficiently, and the production quality can be maintained at a high level even for low lot sizes. This paper presents an architecture for automating the interaction and functional building blocks between manufacturers and MaaS platforms, along with a prototype implementation and evaluation of its effectiveness in addressing the challenges SME manufacturers are faced with.
Applying micro-patterns to surfaces has been shown to impart useful physical properties such as drag reduction and hydrophobicity. However, current manufacturing techniques cannot produce micro-patterned surfaces at scale due to high-cost machinery and inefficient coverage techniques such as raster-scanning. In this work, we use multiple robots, each equipped with a patterning tool, to manufacture these surfaces. To allow these robots to coordinate during the patterning task, we use the ergodic control algorithm, which specifies coverage objectives using distributions. We demonstrate that robots can divide complicated coverage objectives by communicating compressed representations of their trajectory history both in simulations and experimental trials. Further, we show that robot-produced patterning can lower the coefficient of friction of metallic surfaces. This work demonstrates that distributed multi-robot systems can coordinate to manufacture products that were previously unrealizable at scale.
Micro-milling spindles require high rotational speeds where conventional rolling element bearings face limitations such as friction and thermal expansion. Active magnetic bearings (AMBs) address these challenges by providing non-contact and lubrication-free operation at ultra-high speeds with the ability to actively regulate spindle dynamics. The existing literature on AMB spindles has mainly reported specific prototype realizations or control system implementations for specific spindle dynamics. Consequently, design knowledge remains fragmented across isolated successful studies. This paper addresses this gap by presenting a systematic and iterative framework to design and manufacture a micro-milling AMB spindle. The process involves a multidisciplinary design flow with a focus on critical practical aspects of manufacturing. The realized spindle is reported as a case study.
Advanced manufacturing with new bio-derived materials can be achieved faster and more economically with first-principle-based artificial intelligence and machine learning (AI/ML)-derived models and process optimization. Not only is this motivated by increased industry profitability, but it can also be optimized to reduce waste generation, energy consumption, and gas emissions through additive manufacturing (AM) and AI/ML-directed self-driving laboratory (SDL) process optimization. From this perspective, the benefits of using 3D printing technology to manufacture durable, sustainable materials will enable high-value reuse and promote a better circular economy. Using AI/ML workflows at different levels, it is possible to optimize the synthesis and adaptation of new bio-derived materials with self-correcting 3D printing methods, and in-situ characterization. Working with training data and hypotheses derived from Large Language Models (LLMs) and algorithms, including ML-optimized simulation, it is possible to demonstrate more field convergence. The combination of SDL and AI/ML Workflows can be the norm for improved use of biobased and renewable materials towards advanced manufacturing.
Design for manufacturing plays a critical role in advanced aeroengine development, where complex components necessitate careful consideration of manufacturability. However, current practices in pipe routing remain largely decoupled from down-stream manufacturing, leading to labor-intensive, trial-and-error iterations to achieve manufacturable designs. To address this problem, this study proposes the Frenet-based pipe routing optimization (FPRO) framework, a manufacturability knowledge-integrated reinforcement learning approach for free-form pipe design in aeroengines. FPRO formulates the routing problem as a boundary value problem in the Frenet frame. In this framework, the pipe path is represented by curvature and torsion profiles, which are generated using cubic Hermite interpolation. To integrate design and manufacturing, domain-specific manufacturing knowledge is embedded as constraints on the permissible ranges of curvature and torsion. The path optimization is performed using the proximal policy optimization algorithm with stochastic exploration and a stage-guided reward mechanism. A unified mapping formulation then translates the optimized path into motion trajectories for t
Manufacturability assessment is a critical step in bridging the persistent gap between design and production. While artificial intelligence (AI) has been widely applied to this task, most existing frameworks rely on geometry-driven methods that require extensive preprocessing, suffer from information loss, and offer limited interpretability. This study proposes a methodology that evaluates manufacturability directly from parametric design features, enabling explicit incorporation of dimensional tolerances without requiring computer-aided design (CAD) processing. The approach employs Kolmogorov-Arnold Networks (KANs) to learn functional relationships between design parameters, tolerances, and manufacturability outcomes. A synthetic dataset of 300,000 labeled designs is generated to evaluate performance across three representative scenarios: hole drilling, pocket milling, and combined drilling-milling, while accounting for machining constraints and design-for-manufacturing (DFM) rules. Benchmarking against fourteen machine learning (ML) and deep learning (DL) models shows that KAN achieves the highest performance in all scenarios, with AUC values of 0.9919 for drilling, 0.9841 for mi
Lithium-ion batteries (LIBs) have an important role in the shift required to achieve a global net-zero carbon target of 2050. Electrode manufacture is amongst the most expensive steps of the LIB manufacturing process and, despite its apparent maturity, optimised manufacturing conditions are arrived at by largely trial and error. Currently, LIB manufacturing plants are controlled to follow the fixed "recipe" obtained by trial and error, which may nonetheless be suboptimal. Moreover, regulating the process as a whole to conform to the set conditions is not widespread. Inspired by control approaches used in other film and sheet processes, we discuss opportunities for implementing real-time process control of electrode-related products, which has the potential to reduce the electrode manufacturing cost, CO2 emissions, usage of resources by increases in process yield, and throughput. We highlight the challenges and significant opportunities of implementing real-time process control in LIB electrode production lines.
Powder bed fusion is a widely used additive manufacturing (AM) process for producing complex, small-batch parts that are impractical to manufacture using conventional methods. However, its broader adoption is hindered by process-induced defects. The challenge in AM stems from inherent material and process uncertainties. Therefore, it is critical to account for these uncertainties in the design optimization and control of powder bed fusion AM processes. In this work, we formulate and solve a design optimization problem under uncertainty for a powder bed fusion metal AM process. Our objective is to minimize energy consumption while enforcing a risk-based constraint formulated with a buffered probability of failure on residual stress, along with a constraint on melting temperature to ensure a successful build. We use surrogate models for the residual stress and temperature snapshots to accelerate optimization; we train these models using data from high-fidelity finite element simulations. We validate the optimization results through additional high-fidelity simulations. The validated results demonstrate that the proposed optimization reduces energy consumption, enhances process reliab
Fabrication today relies on disparate, large machines spread across industrial facilities. These are operated by domain experts to construct and assemble artefacts in sequential steps from large numbers of parts. This traditional, centralized mass manufacturing paradigm is characterized by large capital costs and inflexibility to changing needs, complex global supply chains hinged on economic and political stability, and waste and over-manufacturing of uniform artefacts that fail to meet the technical and personal needs of today's diverse individuals and use cases. Today, these challenges are particularly severe at points of need, such as the space environment. The space environment is remote and unpredictable, and the ability to manufacture in situ offers unique opportunities to address new challenges as they arise. However, the challenges faced in space are often mirrored on Earth. In hospitals, disaster zones, low resource environments and laboratories, the ability to manufacture customized artefacts at points of need can significantly enhance our ability to respond rapidly to unforeseen events. In this thesis, I introduce digital fabrication platforms with co-developed hardware