Hasil untuk "Manufactures"

Lei Deng, Wenhao Huang, Chao Yang et al.

Defect depth quantification in additively manufactured (AM) components remains a significant challenge for non-destructive testing (NDT). This study proposes a Pixel-wise Quantitative Thermography Neural Network (PQT-Net) to address this challenge for polylactic acid (PLA) parts. A key innovation is a novel data augmentation strategy that reconstructs thermal sequence data into two-dimensional stripe images, preserving the complete temporal evolution of heat diffusion for each pixel. The PQT-Net architecture incorporates a pre-trained EfficientNetV2-S backbone and a custom Residual Regression Head (RRH) with learnable parameters to refine outputs. Comparative experiments demonstrate the superiority of PQT-Net over other deep learning models, achieving a minimum Mean Absolute Error (MAE) of 0.0094 mm and a coefficient of determination (R) exceeding 99%. The high precision of PQT-Net underscores its potential for robust quantitative defect characterization in AM.

Saksham Kiroriwal, Julius Pfrommer, Jürgen Beyerer

Bayesian optimization (BO) is a powerful method for optimizing black-box manufacturing processes, but its performance is often limited when dealing with high-dimensional multi-stage systems, where we can observe intermediate outputs. Standard BO models the process as a black box and ignores the intermediate observations and the underlying process structure. Partially Observable Gaussian Process Networks (POGPN) model the process as a Directed Acyclic Graph (DAG). However, using intermediate observations is challenging when the observations are high-dimensional state-space time series. Process-expert knowledge can be used to extract low-dimensional latent features from the high-dimensional state-space data. We propose POGPN-JPSS, a framework that combines POGPN with Joint Parameter and State-Space (JPSS) modeling to use intermediate extracted information. We demonstrate the effectiveness of POGPN-JPSS on a challenging, high-dimensional simulation of a multi-stage bioethanol production process. Our results show that POGPN-JPSS significantly outperforms state-of-the-art methods by achieving the desired performance threshold twice as fast and with greater reliability. The fast optimization directly translates to substantial savings in time and resources. This highlights the importance of combining expert knowledge with structured probabilistic models for rapid process maturation.

Marios-Nektarios Stamatopoulos, Jakub Haluska, Elias Small et al.

A novel autonomous chunk-based aerial additive manufacturing framework is presented, supported with experimental demonstration advancing aerial 3D printing. An optimization-based decomposition algorithm transforms structures into sub-components, or chunks, treated as individual tasks coordinated via a dependency graph, ensuring sequential assignment to UAVs considering inter-dependencies and printability constraints for seamless execution. A specially designed hexacopter equipped with a pressurized canister for lightweight expandable foam extrusion is utilized to deposit the material in a controlled manner. To further enhance precise execution of the printing, an offset-free Model Predictive Control mechanism is considered compensating reactively for disturbances and ground effect during execution. Additionally, an interlocking mechanism is introduced in the chunking process to enhance structural cohesion and improve layer adhesion. Extensive experiments demonstrate the framework's effectiveness in constructing precise structures of various shapes while seamlessly adapting to practical challenges, proving its potential for a transformative leap in aerial robotic capability for autonomous construction.

Honglu He, Chen-lung Lu, Jinhan Ren et al.

Wire Arc Additive Manufacturing (WAAM) is a metal 3D printing technology that deposits molten metal wire on a substrate to form desired geometries. Articulated robot arms are commonly used in WAAM to produce complex geometric shapes. However, they mostly rely on proprietary robot and weld control software that limits process tuning and customization, incorporation of third-party sensors, implementation on robots and weld controllers from multiple vendors, and customizable user programming. This paper presents a general open-source software architecture for WAAM that addresses these limitations. The foundation of this architecture is Robot Raconteur, an open-source control and communication framework that serves as the middleware for integrating robots and sensors from different vendors. Based on this architecture, we developed an end-to-end robotic WAAM implementation that takes a CAD file to a printed WAAM part and evaluates the accuracy of the result. The major components in the architecture include part slicing, robot motion planning, part metrology, in-process sensing, and process tuning. The current implementation is based on Motoman robots and Fronius weld controller, but the approach is applicable to other industrial robots and weld controllers. The capability of the WAAM tested is demonstrated through the printing of parts of various geometries and acquisition of in-process sensor data for motion adjustment.

Fardin Jalil Piran, Prathyush P. Poduval, Hamza Errahmouni Barkam et al.

Machine Learning (ML) models integrated with in-situ sensing offer transformative solutions for defect detection in Additive Manufacturing (AM), but this integration brings critical challenges in safeguarding sensitive data, such as part designs and material compositions. Differential Privacy (DP), which introduces mathematically controlled noise, provides a balance between data utility and privacy. However, black-box Artificial Intelligence (AI) models often obscure how this noise impacts model accuracy, complicating the optimization of privacy-accuracy trade-offs. This study introduces the Differential Privacy-Hyperdimensional Computing (DP-HD) framework, a novel approach combining Explainable AI (XAI) and vector symbolic paradigms to quantify and predict noise effects on accuracy using a Signal-to-Noise Ratio (SNR) metric. DP-HD enables precise tuning of DP noise levels, ensuring an optimal balance between privacy and performance. The framework has been validated using real-world AM data, demonstrating its applicability to industrial environments. Experimental results demonstrate DP-HD's capability to achieve state-of-the-art accuracy (94.43%) with robust privacy protections in anomaly detection for AM, even under significant noise conditions. Beyond AM, DP-HD holds substantial promise for broader applications in privacy-sensitive domains such as healthcare, financial services, and government data management, where securing sensitive data while maintaining high ML performance is paramount.

Steve Yuwono, Dorothea Schwung, Andreas Schwung

This article describes a novel game structure for autonomously optimizing decentralized manufacturing systems with multi-objective optimization challenges, namely Distributed Stackelberg Strategies in State-Based Potential Games (DS2-SbPG). DS2-SbPG integrates potential games and Stackelberg games, which improves the cooperative trade-off capabilities of potential games and the multi-objective optimization handling by Stackelberg games. Notably, all training procedures remain conducted in a fully distributed manner. DS2-SbPG offers a promising solution to finding optimal trade-offs between objectives by eliminating the complexities of setting up combined objective optimization functions for individual players in self-learning domains, particularly in real-world industrial settings with diverse and numerous objectives between the sub-systems. We further prove that DS2-SbPG constitutes a dynamic potential game that results in corresponding converge guarantees. Experimental validation conducted on a laboratory-scale testbed highlights the efficacy of DS2-SbPG and its two variants, such as DS2-SbPG for single-leader-follower and Stack DS2-SbPG for multi-leader-follower. The results show significant reductions in power consumption and improvements in overall performance, which signals the potential of DS2-SbPG in real-world applications.

Yash Kulkarni, Susheela Sharma, Jordan P. Amadio et al.

Vital for spinal fracture treatment, pedicle screw fixation is the gold standard for spinal fixation procedures. Nevertheless, due to the screw pullout and loosening issues, this surgery often fails to be effective for patients suffering from osteoporosis (i.e., having low bone mineral density). These failures can be attributed to the rigidity of existing drilling instruments and pedicle screws forcing clinicians to place these implants into the osteoporotic regions of the vertebral body. To address this critical issue, we have developed a steerable drilling robotic system and evaluated its performance in drilling various J- and U-shape trajectories. Complementary to this robotic system, in this paper, we propose design, additive manufacturing, and biomechanical evaluation of a transformative flexible pedicle screw (FPS) that can be placed in pre-drilled straight and curved trajectories. To evaluate the performance of the proposed flexible implant, we designed and fabricated two different types of FPSs using the direct metal laser sintering (DMLS) process. Utilizing our unique experimental setup and ASTM standards, we then performed various pullout experiments on these FPSs to evaluate and analyze their biomechanical performance implanted in straight trajectories.

Juan P. Mendez, Xujiao Gao, Jeffrey Ivie et al.

We investigate the different transport mechanisms that can occur in pn junction devices made using atomic precision advanced manufacturing (APAM) at temperatures ranging from cryogenic to room temperature. We first elucidate the potential cause of the anomalous behavior observed in the forward-bias response of these devices in recent cryogenic temperature measurements, which deviates from the theoretical response of a silicon Esaki diode. These anomalous behaviors include current suppression at low voltages in the forward-bias response and a much lower valley voltage at cryogenic temperatures than theoretically expected for a silicon diode. To investigate the potential causes of these anomalies, we studied the effects of a few possible transport mechanisms, including band-to-band tunneling, band gap narrowing, potential impact of non-Ohmic contacts, band quantization, impact of leakage, and inelastic trap-assisted tunneling, through semi-classical simulations. We find that a combination of two sets of band-to-band tunneling (BTBT) parameters can qualitatively approximate the shape of the tunneling current at low bias. This can arise from band quantization and realignment due to the strong potential confinement in $δ$-layers. We also find that the lower-than-theoretically-expected valley voltage can be attributed to modifications in the electronic band structure within the $δ$-layer regions, leading to a significant band-gap narrowing induced by the high density of dopants. Finally, we extend our analyses to room temperature operation and predict that trap-assisted tunneling (TAT) facilitated by phonon interactions may become significant, leading to a complex superposition of BTBT and TAT transport mechanisms in the electrical measurements.

Abhinav Chandraker, Sampad Barik, Nichenametla Jai Sai et al.

Traditionally, yield strength prediction relies on detailed and resource-intensive microstructural characterization combined with empirical equations. However, quantifying microstructural feature length scales for novel processes like additive manufacturing, which involves inhomogeneous hierarchical features, poses a challenge. The lack of accurate material constants for broader composition ranges further limits empirical predictions. This study proposes an alternative machine learning (ML) approach for predicting the yield strength of additively manufactured (AM) multi-principal element alloys (MPEAs) from the Co-Cr-Fe-Mn-Ni system by correlating composition, printing parameters, and testing conditions. The best-performing ML model achieved an R2 of 0.84, comparable to that achieved using microstructural detail-driven empirical strengthening contributions. The validity of the ML approach was further confirmed by printing and testing two compositions (one novel and one from the dataset). This data-driven approach directly relates yield strength to initial printing parameters, highlighting their significance and individual effects, such as scan velocity's direct impact and laser power's inverse impact on yield strength. This demonstrates ML's potential to guide AM processes, reducing the need for iterative experiments and enabling rapid exploration of compositional and printing spaces to achieve desired properties.

Atah Nuh Mih, Hung Cao, Joshua Pickard et al.

Quality assurance is crucial in the smart manufacturing industry as it identifies the presence of defects in finished products before they are shipped out. Modern machine learning techniques can be leveraged to provide rapid and accurate detection of these imperfections. We, therefore, propose a transfer learning approach, namely TransferD2, to correctly identify defects on a dataset of source objects and extend its application to new unseen target objects. We present a data enhancement technique to generate a large dataset from the small source dataset for building a classifier. We then integrate three different pre-trained models (Xception, ResNet101V2, and InceptionResNetV2) into the classifier network and compare their performance on source and target data. We use the classifier to detect the presence of imperfections on the unseen target data using pseudo-bounding boxes. Our results show that ResNet101V2 performs best on the source data with an accuracy of 95.72%. Xception performs best on the target data with an accuracy of 91.00% and also provides a more accurate prediction of the defects on the target images. Throughout the experiment, the results also indicate that the choice of a pre-trained model is not dependent on the depth of the network. Our proposed approach can be applied in defect detection applications where insufficient data is available for training a model and can be extended to identify imperfections in new unseen data.

Patrick M. Praegla, Thomas Mair, Andreas Wimmer et al.

Particle dampers represent a simple yet effective means to reduce unwanted oscillations when attached to structural components. Powder bed fusion additive manufacturing of metals allows to integrate particle inclusions of arbitrary shape, size and spatial distribution directly into bulk material, giving rise to novel metamaterials with controllable dissipation without the need for additional external damping devices. At present, however, it is not well understood how the degree of dissipation is influenced by the properties of the enclosed powder packing. In the present work, a two-way coupled discrete element - finite element model is proposed allowing for the first time to consistently describe the interaction between oscillating deformable structures and enclosed powder packings. As fundamental test case, the free oscillations of a hollow cantilever beam filled with various powder packings differing in packing density, particle size, and surface properties are considered to systematically study these factors of influence. Critically, it is found that the damping characteristics strongly depend on the packing density of the enclosed powder and that an optimal packing density exists at which the dissipation is maximized. Moreover, it is found that the influence of (absolute) particle size on dissipation is rather small. First-order analytical models for different deformation modes of such powder cavities are derived to shed light on this observation.

Magdalena Kapelko, Lidia Ortiz

This paper computes composite indicators of corporate social responsibility (CSR) from an efficiency perspective for food and beverage manufacturing firms in various world regions over the period from 2011 to 2018. From a methodological perspective, we extend the measurement of composite indicators within data envelopment analysis, allowing for the non-convexities of the production set and for the appropriate comparison of indicators between groups of firms. From an empirical point of view, we contribute by comparing the efficiency in CSR practices of food and beverage companies across regions of Europe, the United States and Canada, and Asia Pacific. The study reveals differences in CSR efficiency between food and beverage firms in the regions considered, with USA and Canadian firms tending to perform best, followed by European firms, and Asian Pacific firms achieving the worst efficiency results. The study also shows that regional catching up in performance occurred over the analyzed period.

Priscilla Delgado, C. Alessandra Luna, Anjana Dissanayaka et al.

Learning miniaturization science remains challenging due to the non-intuitive behavior of microscale objects and complex assembly approaches. Traditional approaches for creating microsystems require expensive equipment, facilities, and trained staff. To improve as well as democratize microdevice education, we created a new educational activity that enables students to build and test advanced microfluidics by leveraging sticker microfluidics, composed of double-sided dry film adhesive layers. Along with T-mixers and bubble generators, our activity is the first to enable students to build a valve and F-mixer in the classroom setting. This helps emphasize less intuitive aspects of device manufacturing such as the creation of complex 3-dimenstional shapes with layers and layer alignment. In addition, this paper provides the first reported quantitative data on significant improvements in student knowledge and confidence from building and testing several common devices. All 11 students had substantial improvements in conceptual mastery and confidence after the activity. Student responses to a guided reflection highlight how the activity supports a variety of learning needs and preferences. Given the impact on student learning, our low-cost activity helps reduce global barriers to miniaturization science education.

Hridyesh Tewani, Megan Hinaus, Mayukh Talukdar et al.

Polymer syntactic foams are transforming materials that will shape the future of next-generation aerospace and marine structures. When manufactured using traditional processes, like compression molding, syntactic foams consist of a solid continuous polymer matrix reinforced with stiff hollow particles. However, polymer matrix segregation can be achieved during the selective laser sintering process with thermoplastic polyurethane (TPU). It is uncertain what role hollow particles play in forming this matrix segregation and its impact on the corresponding mechanical properties of syntactic foams. We show that the size of the hollow particles controls the internal microscale morphology of matrix segregation, leading to counter-intuitive macroscale mechanical responses. Particles with diameters greater than the gaps between the cell walls of the segregated matrix get lodged between and in the walls, bridging the gaps in the segregated matrix and increasing the stiffness of syntactic foams. In contrast, particles with smaller diameters with higher particle crushing strength get lodged only inside the cell walls of the segregated matrix, resulting in higher densification stresses (energy absorption). We show that stiffness and densification can be tuned while enabling lightweight syntactic foams. These novel discoveries will aid in facilitating functional and lightweight syntactic foams for cores in sandwich structures.

H. H. Zhun, V. V. Starikov, V. P. Koverya

One of the important stages in the creation of the scientific and technical foundations for the calculation, design and manufacturing technology of the lowest heat-conductivity thermal protection from screen-vacuum thermal insulation (SVTI) is the development of a process for achieving the optimal vacuum in the SVTI layers, since at this pressure, thermal conductivity through the SVTI is carried out only due to the radiant and contact-conductive components. It is proposed to obtain such a pressure in thermal insulation by using cushioning material in it, which was previously degassed in a separate vacuum chamber at 370-380 K for 12 hours in order to remove water molecules from its structure and then replace them with dry nitrogen molecules. These molecules have 3-4 times less heat of adsorption; therefore they are pumped out faster. As a result, it becomes possible to accelerate (by 20 hours) to achieve optimal vacuum in thermal insulation, as well as 11% lower effective thermal conductivity. The analysis carried out (according to the developed methodology) showed that the achieved optimal effective thermal conductivity of thermal insulation in a cryocontainers is determined by 33% of radiant thermal conductivity and 67% of the contact-conductive component.

Jiannan Wang, Liangjing Ge, Anlin Deng et al.

We used the binding energy-bond-charge model to study the atomic bonding and electrical properties of the two-dimensional graphene/BN van der Waals heterostructure. We manipulated its atomic bonding and electrical properties by manufacturing defects. We discovered that this process yielded a band structure with a flat band, i.e., a horizontal band structure without dispersion at the Fermi level. Thus, our research is significant because it is the first report on this flat band of defect graphene/BN van der Waals heterostructures.

Athar Kharal

With the advent of Industry 4.0, Data Science and Explainable Artificial Intelligence (XAI) has received considerable intrest in recent literature. However, the entry threshold into XAI, in terms of computer coding and the requisite mathematical apparatus, is really high. For fault diagnosis of steel plates, this work reports on a methodology of incorporating XAI based insights into the Data Science process of development of high precision classifier. Using Synthetic Minority Oversampling Technique (SMOTE) and notion of medoids, insights from XAI tools viz. Ceteris Peribus profiles, Partial Dependence and Breakdown profiles have been harvested. Additionally, insights in the form of IF-THEN rules have also been extracted from an optimized Random Forest and Association Rule Mining. Incorporating all the insights into a single ensemble classifier, a 10 fold cross validated performance of 94% has been achieved. In sum total, this work makes three main contributions viz.: methodology based upon utilization of medoids and SMOTE, of gleaning insights and incorporating into model development process. Secondly the insights themselves are contribution, as they benefit the human experts of steel manufacturing industry, and thirdly a high precision fault diagnosis classifier has been developed.

Zhenglei He, Kim Phuc Tran, Sebastien Thomassey et al.

Multi-objective optimization of the textile manufacturing process is an increasing challenge because of the growing complexity involved in the development of the textile industry. The use of intelligent techniques has been often discussed in this domain, although a significant improvement from certain successful applications has been reported, the traditional methods failed to work with high-as well as human intervention. Upon which, this paper proposed a multi-agent reinforcement learning (MARL) framework to transform the optimization process into a stochastic game and introduced the deep Q-networks algorithm to train the multiple agents. A utilitarian selection mechanism was employed in the stochastic game, which (-greedy policy) in each state to avoid the interruption of multiple equilibria and achieve the correlated equilibrium optimal solutions of the optimizing process. The case study result reflects that the proposed MARL system is possible to achieve the optimal solutions for the textile ozonation process and it performs better than the traditional approaches.

Xifan Wang, Franziska Schmidt, Dorian Hanaor et al.

Here we introduce a versatile stereolithographic route to produce three different kinds of Si-containing thermosets that yield high performance ceramics upon thermal treatment. Our approach is based on a fast and inexpensive thiol-ene free radical addition that can be applied for different classes of preceramic polymers with carbon-carbon double bonds. Due to the rapidity and efficiency of the thiol-ene click reactions, this additive manufacturing process can be effectively carried out using conventional light sources on benchtop printers. Through light initiated cross-linking, the liquid preceramic polymers transform into stable infusible thermosets that preserve their shape during the polymer-to-ceramic transformation. Through pyrolysis the thermosets transform into glassy ceramics with uniform shrinkage and high density. The obtained ceramic structures are nearly fully dense, have smooth surfaces, and are free from macroscopic voids and defects. A fabricated SiOC honeycomb was shown to exhibit a significantly higher compressive strength to weight ratio in comparison to other porous ceramics.

Christoph Meier, Ryan W. Penny, Yu Zou et al.

Among the many additive manufacturing (AM) processes for metallic materials, selective laser melting (SLM) is arguably the most versatile in terms of its potential to realize complex geometries along with tailored microstructure. However, the complexity of the SLM process, and the need for predictive relation of powder and process parameters to the part properties, demands further development of computational and experimental methods. This review addresses the fundamental physical phenomena of SLM, with a special emphasis on the associated thermal behavior. Simulation and experimental methods are discussed according to three primary categories. First, macroscopic approaches aim to answer questions at the component level and consider for example the determination of residual stresses or dimensional distortion effects prevalent in SLM. Second, mesoscopic approaches focus on the detection of defects such as excessive surface roughness, residual porosity or inclusions that occur at the mesoscopic length scale of individual powder particles. Third, microscopic approaches investigate the metallurgical microstructure evolution resulting from the high temperature gradients and extreme heating and cooling rates induced by the SLM process. Consideration of physical phenomena on all of these three length scales is mandatory to establish the understanding needed to realize high part quality in many applications, and to fully exploit the potential of SLM and related metal AM processes.