Lattice truss architectures fabricated from CoCrFeMnNi high-entropy alloys (HEAs) through the precision of Laser Powder Bed Fusion (L-PBF) technology were subsequently enhanced via vacuum impregnation with an aluminium alloy, resulting in the creation of a sophisticated bimetallic interpenetrating phase composite (IPC) architecture. Upon exposure to high-speed impact loading using a Split-Hopkinson Pressure Bar (SHPB), these IPCs exhibited exceptional comprehensive impact resistance, particularly notable for their superior energy absorption capabilities. This enhanced performance is attributed to the seamless integration of the distinctive physical properties of the HEA and the Al-based alloy, which together enable coordinated deformation of the bimetallic phases during impact. Detailed analysis of the metallurgical bonding microstructure at the bimetallic interfaces revealed the formation of robust bonding structures both during the impregnation process and after impact-induced failure, with the strengthening effect at the dissimilar material interfaces playing a crucial role in energy absorption by amplifying energy dissipation through crack propagation in the diffusion interface layer. Notably, under equivalent mass conditions, the impact resistance of these IPCs significantly surpasses that of the Al-based alloy alone, demonstrating the potential of this engineered, hierarchical structure as a lightweight, impact-resistant material.
Xiaofeng Xiao, Khawlah Alharbi, Pengyu Zhang
et al.
Causal inference has recently gained notable attention across various fields like biology, healthcare, and environmental science, especially within explainable artificial intelligence (xAI) systems, for uncovering the causal relationships among multiple variables and outcomes. Yet, it has not been fully recognized and deployed in the manufacturing systems. In this paper, we introduce an explainable, scalable, and flexible federated Bayesian learning framework, \texttt{xFBCI}, designed to explore causality through treatment effect estimation in distributed manufacturing systems. By leveraging federated Bayesian learning, we efficiently estimate posterior of local parameters to derive the propensity score for each client without accessing local private data. These scores are then used to estimate the treatment effect using propensity score matching (PSM). Through simulations on various datasets and a real-world Electrohydrodynamic (EHD) printing data, we demonstrate that our approach outperforms standard Bayesian causal inference methods and several state-of-the-art federated learning benchmarks.
Advancements in digitization have enabled two sided manufacturing-as-a-service (MaaS) marketplaces which has significantly reduced product development time for designers. These platforms provide designers with access to manufacturing resources through a network of suppliers and have instant order placement capabilities. Two key decision making levers are typically used to optimize the operations of these marketplaces: pricing and matching. The existing marketplaces operate in a centralized structure where they have complete control over decision making. However, a decentralized organization of the platform enables transparency of information across clients and suppliers. This dissertation focuses on developing tools for decision making enabling decentralization in MaaS marketplaces. In pricing mechanisms, a data driven method is introduced which enables small service providers to price services based on specific attributes of the services offered. A data mining method recommends a network based price to a supplier based on its attributes and the attributes of other suppliers on the platform. Three different approaches are considered for matching mechanisms. First, a reverse auction mechanism is introduced where designers bid for manufacturing services and the mechanism chooses a supplier which can match the bid requirements and stated price. The second approach uses mechanism design and mathematical programming to develop a stable matching mechanism for matching orders to suppliers based on their preferences. Empirical simulations are used to test the mechanisms in a simulated 3D printing marketplace and to evaluate the impact of stability on its performance. The third approach considers the matching problem in a dynamic and stochastic environment where demand (orders) and supply (supplier capacities) arrive over time and matching is performed online.
Visual servoing technology has been well developed and applied in many automated manufacturing tasks, especially in tools' pose alignment. To access a full global view of tools, most applications adopt eye-to-hand configuration or eye-to-hand/eye-in-hand cooperation configuration in an automated manufacturing environment. Most research papers mainly put efforts into developing control and observation architectures in various scenarios, but few of them have discussed the importance of the camera's location in eye-to-hand configuration. In a manufacturing environment, the quality of camera estimations may vary significantly from one observation location to another, as the combined effects of environmental conditions result in different noise levels of a single image shot at different locations. In this paper, we propose an algorithm for the camera's moving policy so that it explores the camera workspace and searches for the optimal location where the images' noise level is minimized. Also, this algorithm ensures the camera ends up at a suboptimal (if the optimal one is unreachable) location among the locations already searched, with limited energy available for moving the camera. Unlike a simple brute force approach, the algorithm enables the camera to explore space more efficiently by adapting the search policy from learning the environment. With the aid of an image averaging technique, this algorithm, in use of a solo camera, achieves the observation accuracy in eye-to-hand configurations to a desirable extent without filtering out high-frequency information in the original image. An automated manufacturing application has been simulated and the results show the success of this algorithm's improvement of observation precision with limited energy.
Most computational fluid dynamics (CFD) modelling of material extrusion additive manufacturing (MEX-AM) mainly relies on a moving boundary condition applied to the print bed and not the direct modelling of the motion of the nozzle. This paper presents step-by-step and detailed implementation of the nozzle motion as well as non-isothermal non-Newtonian flow during MEX-AM in Ansys Fluent. For nozzle motion the overset approach is used for the meshing which allows for the nozzle and the rest of the domain to be meshes separately, streamlining meshing and motion. The method is initially used to simulate a single strand, which was validated against experimental and numerical data. It was then applied to demonstrate out-of-plane nozzle motion in two case studies: three-layer printing and printing on a ramp. The model is further developed to simulate a single strand deposition with the well-known Cross–William–Landel–Ferry (Cross-WLF) behaviour for a thermal and shear-dependent material flow.
Concrete manufacturing projects are one of the most common ones for consulting agencies. Because of the highly non-linear dependency of input materials like ash, water, cement, superplastic, etc; with the resultant strength of concrete, it gets difficult for machine learning models to successfully capture this relation and perform cost optimizations. This paper highlights how PINNs (Physics Informed Neural Networks) can be useful in the given situation. This state-of-the-art model shall also get compared with traditional models like Linear Regression, Random Forest, Gradient Boosting, and Deep Neural Network. Results of the research highlights how well PINNs performed even with reduced dataset, thus resolving one of the biggest issues of limited data availability for ML models. On an average, PINN got the loss value reduced by 26.3% even with 40% lesser data compared to the Deep Neural Network. In addition to predicting strength of the concrete given the quantity of raw materials, the paper also highlights the use of heuristic optimization method like Particle Swarm Optimization (PSO) in predicting quantity of raw materials required to manufacture concrete of given strength with least cost.
Machine Learning (ML) has been demonstrated to improve productivity in many manufacturing applications. To host these ML applications, several software and Industrial Internet of Things (IIoT) systems have been proposed for manufacturing applications to deploy ML applications and provide real-time intelligence. Recently, an interactive causality enabled self-labeling method has been proposed to advance adaptive ML applications in cyber-physical systems, especially manufacturing, by automatically adapting and personalizing ML models after deployment to counter data distribution shifts. The unique features of the self-labeling method require a novel software system to support dynamism at various levels. This paper proposes the AdaptIoT system, comprised of an end-to-end data streaming pipeline, ML service integration, and an automated self-labeling service. The self-labeling service consists of causal knowledge bases and automated full-cycle self-labeling workflows to adapt multiple ML models simultaneously. AdaptIoT employs a containerized microservice architecture to deliver a scalable and portable solution for small and medium-sized manufacturers. A field demonstration of a self-labeling adaptive ML application is conducted with a makerspace and shows reliable performance.
We propose a zero-shot text-driven 3D shape deformation system that deforms an input 3D mesh of a manufactured object to fit an input text description. To do this, our system optimizes the parameters of a deformation model to maximize an objective function based on the widely used pre-trained vision language model CLIP. We find that CLIP-based objective functions exhibit many spurious local optima; to circumvent them, we parameterize deformations using a novel deformation model called BoxDefGraph which our system automatically computes from an input mesh, the BoxDefGraph is designed to capture the object aligned rectangular/circular geometry features of most manufactured objects. We then use the CMA-ES global optimization algorithm to maximize our objective, which we find to work better than popular gradient-based optimizers. We demonstrate that our approach produces appealing results and outperforms several baselines.
Ronald S. Polidan, Jack O. Burns, Alex Ignatiev
et al.
FarView is an early-stage concept for a large, low-frequency radio observatory, manufactured in-situ on the lunar far side using metals extracted from the lunar regolith. It consists of 100,000 dipole antennas in compact subarrays distributed over a large area but with empty space between subarrays in a core-halo structure. FarView covers a total area of ~200 km2, has a dense core within the inner ~36 km2, and a ~power-law falloff of antenna density out to ~14 km from the center. With this design, it is relatively easy to identify multiple viable build sites on the lunar far side. The science case for FarView emphasizes the unique capabilities to probe the unexplored Cosmic Dark Ages - identified by the 2020 Astrophysics Decadal Survey as the discovery area for cosmology. FarView will deliver power spectra and tomographic maps tracing the evolution of the Universe from before the birth of the first stars to the beginning of Cosmic Dawn, and potentially provide unique insights into dark matter, early dark energy, neutrino masses, and the physics of inflation. What makes FarView feasible and affordable in the timeframe of the 2030s is that it is manufactured in-situ, utilizing space industrial technologies. This in-situ manufacturing architecture utilizes Earth-built equipment that is transported to the lunar surface to extract metals from the regolith and will use those metals to manufacture most of the array components: dipole antennas, power lines, and silicon solar cell power systems. This approach also enables a long functional lifetime, by permitting servicing and repair of the observatory. The full 100,000 dipole FarView observatory will take 4 - 8 years to build, depending on the realized performance of the manufacturing elements and the lunar delivery scenario.
The healthcare sector has gradually embraced effective project management for improved healthcare outcomes. This paper explores the application of the agile methodology for managing the healthcare sector. This study significantly contributes to the healthcare sector by providing insights into the application of Agile methodology, potentially enhancing healthcare management and patient outcomes. In this article, the author reviews ten peer-reviewed articles, analyses the findings, and generates three themes: Agile methodology development and implementation in the healthcare sector, the healthcare sector's readiness factors/levels for applying the agile methodology, and how agile methods improve healthcare outcomes. The findings across the three identified themes reiterate the crucial role of organizational leadership, flexible structures, and advanced technology in enhancing agility within the healthcare sector. Moreover, the studies highlight the potential of agile principles to enhance customer-centricity, customer satisfaction, and overall adaptability in healthcare organizations. The study recommends healthcare institutions priorities Agile competency development through training programs and cultivate a culture of adaptability to support Agile methodology adoption. Furthermore, it suggests quantitative research to validate readiness factors' influence, focusing on patient-centered outcomes and comparative studies between Agile and traditional healthcare approaches to bridge literature gaps.
Business, Production management. Operations management
Kwan Zhi Teh, Saikat Medya, Sugandhana Shanmuganathan
et al.
Metal additive manufacturing techniques have enabled the ability to construct complex internal channels, but they create rough surfaces of varying qualities. Surface texture is vital to engineering analysis and is usually emblematic of product quality. The problem, however, lies with the difficulty in measuring or assessing such internal surfaces. As they are concealed by nature, it is difficult to measure them non-destructively through conventional measurement methods. Non-destructive means are favored as they save materials and time, but a proper review of less-known available methods is first required to reveal and understand the proper means of evaluating internal surface non-destructively. This paper reviews the measurement methods of capacitance, vibration analysis, optical techniques, X-ray computed tomography, and replica methods critically with their working principles, pros and cons discussed. Their current applications in literature are evaluated to understand the appropriateness for internal surface applications. Endoscopic non-destructive testing (NDT), X-ray computed tomography, and replica methods are found to be rather suitable. Propositions are also given for enabling the less suitable methods. Amongst all these techniques, X-ray computed tomography stands out as a great method for such purposes and would appear to be the best path forward for development, provided that its resolution issues are improved through better reconstruction algorithms, novel scanning methodologies, or improved X-ray energy sources.
Jobish John, Md. Noor-A-Rahim, Aswathi Vijayan
et al.
This paper explores the role that 5G, WiFi-7, and Time-Sensitive Networking (TSN) can play in driving smart manufacturing as a fundamental part of the Industry 4.0 vision. The paper provides an in-depth analysis of each technology's application in industrial communications, with a focus on TSN and its key elements that enable reliable and secure communication in industrial networks. In addition, the paper includes a comparative study of these technologies, analyzing them based on a number of industrial use-cases, supported secondary applications, industry adoption, and current market trends. The paper concludes by highlighting the challenges and future directions for the adoption of these technologies in industrial networks and emphasizes their importance in realizing the Industry 4.0 vision within the context of smart manufacturing.
Machine learning (ML) has been extensively adopted for the online sensing-based monitoring in advanced manufacturing systems. However, the sensor data collected under abnormal states are usually insufficient, leading to significant data imbalanced issue for supervised machine learning. A common solution is to incorporate data augmentation techniques, i.e., augmenting the available abnormal states data (i.e., minority samples) via synthetic generation. To generate the high-quality minority samples, it is vital to learn the underlying distribution of the abnormal states data. In recent years, the generative adversarial network (GAN)-based approaches become popular to learn data distribution as well as perform data augmentation. However, in practice, the quality of generated samples from GAN-based data augmentation may vary drastically. In addition, the sensor signals are collected sequentially by time from the manufacturing systems, which means sequential information is also very important in data augmentation. To address these limitations, inspired by the multi-head attention mechanism, this paper proposed an attention-stacked GAN (AS-GAN) architecture for sensor data augmentation of online monitoring in manufacturing system. It incorporates a new attention-stacked framework to strengthen the generator in GAN with the capability of capturing sequential information, and thereby the developed attention-stacked framework greatly helps to improve the quality of the generated sensor signals. Afterwards, the generated high-quality sensor signals for abnormal states could be applied to train classifiers more accurately, further improving the online monitoring performance of manufacturing systems. The case study conducted in additive manufacturing also successfully validated the effectiveness of the proposed AS-GAN.
Rapid advances in artificial intelligence (AI) have the potential to significantly increase the productivity, quality, and profitability in future manufacturing systems. Traditional mass-production will give way to personalized production, with each item made to order, at the low cost and high-quality consumers have come to expect. Manufacturing systems will have the intelligence to be resilient to multiple disruptions, from small-scale machine breakdowns, to large-scale natural disasters. Products will be made with higher precision and lower variability. While gains have been made towards the development of these factories of the future, many challenges remain to fully realize this vision. To consider the challenges and opportunities associated with this topic, a panel of experts from Industry, Academia, and Government was invited to participate in an active discussion at the 2022 Modeling, Estimation and Control Conference (MECC) held in Jersey City, New Jersey from October 3- 5, 2022. The panel discussion focused on the challenges and opportunities to more fully integrate AI into manufacturing systems. Three overarching themes emerged from the panel discussion. First, to be successful, AI will need to work seamlessly, and in an integrated manner with humans (and vice versa). Second, significant gaps in the infrastructure needed to enable the full potential of AI into the manufacturing ecosystem, including sufficient data availability, storage, and analysis, must be addressed. And finally, improved coordination between universities, industry, and government agencies can facilitate greater opportunities to push the field forward. This article briefly summarizes these three themes, and concludes with a discussion of promising directions.
Wood fiber filled high density polyethylene composites (WPCs) were prepared using twin screw extruder and maleated polyethylene as a coupling agent. Bamboo fibers were initially treated with alkali (NaOH), boric acid - borax (Ba-Bx) and borax (Bx). The treated and untreated fibers were used in combination with ammonium polyphosphate (APP) to investigate their synergistic effects on thermal stability, flame retardancy and mechanical properties. Alkali pretreatment (5 % NaOH) of fibers showed significant improvement in performance of APP by increasing thermal stability in WPCs. The derivative thermogravimetric (DTG) results indicate significance of Ba-Bx in promoting char induction at lower temperatures (340 ºC) and thereby, improved the thermal stability in WPCs. Flammability decreased with addition of flame retardant additives. As compared to pure WPCs, composites containing APP 10 % / Ba-Bx 5 % exhibited maximum reduction in average heat release rate (HRR) by 69 %, peak heat release rate (PHRR) by 59 %, total heat released rate (THR) by 48 % and also increased time to ignition (TTI) by 62 %. However, no significant difference was found among the combinations i.e., APP with or without compounds towards reducing the flammability of WPCs. The strength properties also reduced significantly when boron compounds were added along with APP. In general, APP alone (15 %) is enough for imparting thermal stability and flame retardancy in WPCs.
Ankle braces typically restrict the functional range of motion. Braces should preferably allow a free functional range of motion during sport while protecting the foot at high-risk positions beyond that range. This could be achieved with 3D printed metamaterial structures that could have varying properties throughout an individual’s ankle range of motion. This paper aims to illustrate an exploratory methodology of using an affordable Fused Deposition Modelling 3D printing technology to develop an ankle brace using metamaterial structures. It also showcases the design, manufacturing processes and testing of 3D printed customised ankle brace prototype designs that incorporated metamaterial structures. Initial tests showed that as designed, the prototype braces maintained the full range of motion for plantar flexion angles. Results also showed that the prototypes required one of the lowest moments during functional range of motion while achieving almost twice to thrice the moment required beyond the functional range of motion.
David Stüve, Robert van der Meer, Mouhamad Shaker Ali Agha
et al.
Advanced Planning Systems (APS) can contribute to improved decision-making and enhanced efficiency along complex food supply chains. This paper presents a systematic literature review of three increasingly important supply chain planning (SCP) tasks supported by APS, namely Supply Chain Network Design (SCND), Sales & Operations Planning (S&OP) and Production Planning & Scheduling (PP&S). Furthermore, academic literature on the implementation of software tools for SCP practices is investigated. The literature review reveals that multiple models for SCP practices have been developed. Empirical literature including case studies on the implementation of APS is sparse. The findings suggest that developed models for the examined planning tasks are implemented to a limited extent in practice. The study can help practitioners in the food industry to get insights regarding the opportunities by the areas of SCP examined in this paper. A theoretical framework providing research propositions to enhance the understanding of APS implementation is introduced.
Muhammad Imran, Jinsheng Sun, Fatima Sehar Zaidi
et al.
Control charts, one of the main tools in Statistical Process Control (SPC), have been widely adopted in manufacturing sectors as an effective strategy for malfunction detection throughout the previous decades. Measurement errors (M.E's) are involved in the quality characteristic of interest. The authors explored the impact of a linear covariate error model on the multivariate cumulative sum (CUSUM) control charts for a specific kind of data known as compositional data(CoDa). The average run length ARL is used to assess the performance of the proposed chart. The results indicate that M.E's significantly affects the multivariate CUSUM-CoDa control charts. The authors have used the Markov chain method to study the impact of different involved parameters using four different cases for the variance-covariance matrix (i.e. uncorrelated with equal variances, negatively correlated with equal variances, uncorrelated with unequal variances, positively correlated with unequal variances). The authors concluded that the ARL of the multivariate CUSUM-CoDa chart increase with an increase in the value of error variance-covariance matrix, while the ARL decreases with an increase in the subgroup size m or the constant powering b. For the implementation of the proposal, two illustrated examples have been reported for multivariate CUSUM-CoDa control charts in the presence of M.E's. One deals with the manufacturing process of uncoated aspirin tablets, and the other is based on monitoring machines in the muesli manufacturing process.
With the rapid growth of display devices, quality inspection via machine vision technology has become increasingly important for flat-panel displays (FPD) industry. This paper discloses a novel visual inspection system for liquid crystal display (LCD), which is currently a dominant type in the FPD industry. The system is based on two cornerstones: robust/high-performance defect recognition model and cognitive visual inspection service architecture. A hybrid application of conventional computer vision technique and the latest deep convolutional neural network (DCNN) leads to an integrated defect detection, classfication and impact evaluation model that can be economically trained with only image-level class annotations to achieve a high inspection accuracy. In addition, the properly trained model is robust to the variation of the image qulity, significantly alleviating the dependency between the model prediction performance and the image aquisition environment. This in turn justifies the decoupling of the defect recognition functions from the front-end device to the back-end serivce, motivating the design and realization of the cognitive visual inspection service architecture. Empirical case study is performed on a large-scale real-world LCD dataset from a manufacturing line with different layers and products, which shows the promising utility of our system, which has been deployed in a real-world LCD manufacturing line from a major player in the world.