Friction stir-welding (FSW) is widely recognized as a modern solid-state technology used to join dissimilar materials by solid-state mechanical mixing. Such mechanical mixing can be exploited to fabricate in situ composite structures through solid-state deformation mechanisms. The present investigation highlights the microstructural evolution and mechanical properties of an in situ composite structure fabricated by FSW of aluminum (Al) to titanium (Ti) incorporating a thin Nickel (Ni) interlayer. A 0.1 mm thick Ni foil was placed across the full butt interface between 4 mm thick Al and Ti plates before friction stir-welding. Properties of the composite were investigated in detail, and the results revealed that fragmented Ti and Ni particles of different sizes were consolidated in the weld nugget. Al, on the other hand, exhibited substantial microstructural refinement and developed an equiaxed microstructure with random grain orientation, mixed grain boundaries and low micro-strain accumulation in the weld nugget. At the processing temperature, Al reacted with both Ti and Ni to form multiple intermetallic compounds. Tensile testing indicated that the tensile properties of the weld were close to those of the base aluminum. This retention of mechanical properties in spite of recrystallization is attributed to the following mechanisms: (1) Ti and Ni undergo severe deformation, forming fine particles with varying sizes and shapes; (2) at particle interfaces, diffusion and chemical reactions produce interlayers and intermetallic compounds; (3) these particles are consolidated within dynamically recrystallized Al, imparting composite characteristics to the weld nugget; and (4) the particles containing intermetallic compounds act as dispersoids in the Al matrix. Quantitatively, the weld retained 98% (104.2 ± 3.3 MPa) UTS and 90% (17.1 ± 1.2) ductility of base aluminum, demonstrating the effectiveness of the Ni interlayer approach in controlling brittle intermetallic formation.
Ana Victoria Ibarra-Meneses, Laura Fernández, Juan Víctor San Martín
et al.
Abstract Background The whole blood stimulation assay (WBA) is a valuable tool for detecting asymptomatic Leishmania infection and monitoring the treatment of visceral leishmaniasis (VL). This study sought to identify specific recombinant proteins to replace the nonspecific soluble Leishmania antigen in this assay, which could be useful for developing a standardized diagnostic test that complies with good manufacturing practice. Methods Employing a cell lymphoproliferative assay, we here assessed the behaviour of 11 recombinant antigens in 61 subjects who had either been successfully treated for or had spontaneously recovered from Leishmania infantum infection. We then selected those antigens showing significant differences in immune cell stimulation indices and cytokine secretion between a responder and non-responder group, respectively, showing a cellular response to L. infantum or not. The three best candidate antigens, ΔCpB, NSC and ENSC, were then used in a WBA conducted on peripheral blood from 53 subjects stratified according to leishmaniasis status [cured VL, cured cutaneous leishmaniasis (CL), asymptomatic leishmaniasis (AS) and healthy controls]. Results ENSC was found to be the most effective antigen to detect cured VL by measuring specific IP-10 production (90% recognition) and TNF induced by ΔCpB to detect cured CL (71.4% recognition). Although the cytokines IL-2 and IP-10 elicited by NSC and ENSC were able to detect AS, this capacity was only moderate (60%). Conclusions We propose that, once validated in larger studies, these GLP Leishmania antigens might help improve the accuracy of treatment monitoring and diagnosing cure. Graphical Abstract
The Fourth Industrial Revolution has introduced “shared manufacturing” as a key concept that leverages digitalization, IoT, blockchain, and robotics to redefine the production and delivery of manufacturing services. This paper presents a novel approach to decentralized warehouse management integrating Large Language Models (LLMs) into the decision-making processes of autonomous agents, which serves as a proof of concept for shared manufacturing. A multi-layered system architecture consisting of physical, digital shadow, organizational, and protocol layers was developed to enable seamless interactions between parcel and warehouse agents. Shared Warehouse game simulations were conducted to evaluate the performance of LLM-driven agents in managing warehouse services, including direct and pooled offers, in a competitive environment. The simulation results show that the LLM-controlled agent clearly outperformed traditional random strategies in decentralized warehouse management. In particular, it achieved higher warehouse utilization rates, more efficient resource allocation, and improved profitability in various competitive scenarios. The LLM agent consistently ensured optimal warehouse allocation and strategically selected offers, reducing empty capacity and maximizing revenue. In addition, the integration of LLMs improves the robustness of decision-making under uncertainty by mitigating the impact of randomness in the environment and ensuring consistent, contextualized responses. This work represents a significant advance in the application of AI to decentralized systems. It provides insights into the complexity of shared manufacturing networks and paves the way for future research in distributed production systems.
Tae Hun Lee, Tim Klinkhammer, Daniel Zontar
et al.
Five-axis machining centers are essential for manufacturing complex, high-precision parts. However, their accuracy is significantly affected by thermally induced geometric errors, also known as thermo-elastic errors. This paper presents a comprehensive approach to thermal characterization and its potential application in predictive modeling on a five-axis machine tool demonstrator, showcasing the capabilities of a novel dynamic R-test measurement method. Based on a previously developed and validated dynamic R-test measurement method that enables the rapid, volumetric acquisition of machine deviations during continuous movement, detailed experimental investigations were conducted under various single- and combined-axis loading scenarios. The extensive dataset and detailed error information provided by the dynamic R-test method enabled thorough analysis and correlation of thermo-elastic errors, including translational and rotational errors, with temperature and control-internal axis data. A well-established phenomenological model based on PT1 transfer functions is used, detailing its input variables and parameter determination methods. The model’s predictive capability was rigorously validated against independent datasets, demonstrating significant reductions in primary errors (up to 70% in maximum residual and 80% in RMSE). This study identifies the most influential error types and their correlation with thermal loads. This confirms the feasibility of robustly predicting thermo-elastic behavior and enhancing the volumetric accuracy of five-axis machine tools, particularly by leveraging the detailed error insights enabled by the dynamic R-test.
In this paper, we study the zero-error capacity of channels with memory, which are represented by graphs. We provide a method to construct code for any graph with one edge, thereby determining a lower bound on its zero-error capacity. Moreover, this code can achieve zero-error capacity when the symbols in a vertex with degree one are the same. We further apply our method to the one-edge graphs representing the binary channels with two memories. There are 28 possible graphs, which can be organized into 11 categories based on their symmetries. The code constructed by our method is proved to achieve the zero-error capacity for all these graphs except for the two graphs in Case 11.
Ester Villanueva, Joseba Albizuri, Patricia Caballero
et al.
This work aims to introduce a new method to predict the wear rate accurately and quickly. Using techniques such as laser scanning confocal microscopy can take a long time to estimate the wear of the experimental alloys in situ. Developing a new method based on calculating the area under the early stages of the friction curve can be a useful and quick tool for estimating wear rate values and comparing wear between different alloys and conditions. The results validated the application of this new method with a regression coefficient of 0.98. This work also demonstrates that wear in the early stages accounts for the highest wear, indicating that the friction coefficient in the steady-state is not always a reliable indicator of the total wear rate. Hardness can be a more influencing parameter on wear rate than steady-state friction coefficient. Using the new method can help reduce time and predict wear more accurately of different alloys.
Carolina Machado Fraguas Soares, Chaylana Saldanha Santos, Francisco Santos Sabbadini
et al.
Buscando solucionar a escassez de recursos naturais advindas das revoluções industriais e dos modelos de produções, a integração entre os conceitos e tecnologias habilitadoras com a economia circular, deverão cumprir novas otimizações dos recursos. Este estudo teve como objetivo realizar uma pesquisa bibliométrica, a partir das bases científicas Scopus e Web Of Science, e mapear os casos de aplicação conjunta de economia circular com a indústria 4.0 comparativamente a empresa MW. Através dessa pesquisa, demonstrou-se o crescimento do tema economia circular ou indústria 4.0 nos últimos anos, porém faltando estudos sobre o uso conjunto dos processos. Concluiu-se que as tecnologias habilitadoras existentes na Indústria 4.0 e a economia circular (EC), superam os desafios de operacionalização, suavizando a transição da cadeia de suprimentos de um modelo linear para circular. O uso combinado destas contribuem para a sustentabilidade da organização.
Production management. Operations management, Production capacity. Manufacturing capacity
Julian Senoner, Bernhard Kratzwald, Milan Kuzmanovic
et al.
To meet order fulfillment targets, manufacturers seek to optimize production schedules. Machine learning can support this objective by predicting throughput times on production lines given order specifications. However, this is challenging when manufacturers produce customized products because customization often leads to changes in the probability distribution of operational data -- so-called distributional shifts. Distributional shifts can harm the performance of predictive models when deployed to future customer orders with new specifications. The literature provides limited advice on how such distributional shifts can be addressed in operations management. Here, we propose a data-driven approach based on adversarial learning and job shop scheduling, which allows us to account for distributional shifts in manufacturing settings with high degrees of product customization. We empirically validate our proposed approach using real-world data from a job shop production that supplies large metal components to an oil platform construction yard. Across an extensive series of numerical experiments, we find that our adversarial learning approach outperforms common baselines. Overall, this paper shows how production managers can improve their decision-making under distributional shifts.
The modeling of multistage manufacturing systems (MMSs) has attracted increased attention from both academia and industry. Recent advancements in deep learning methods provide an opportunity to accomplish this task with reduced cost and expertise. This study introduces a stochastic deep Koopman (SDK) framework to model the complex behavior of MMSs. Specifically, we present a novel application of Koopman operators to propagate critical quality information extracted by variational autoencoders. Through this framework, we can effectively capture the general nonlinear evolution of product quality using a transferred linear representation, thus enhancing the interpretability of the data-driven model. To evaluate the performance of the SDK framework, we carried out a comparative study on an open-source dataset. The main findings of this paper are as follows. Our results indicate that SDK surpasses other popular data-driven models in accuracy when predicting stagewise product quality within the MMS. Furthermore, the unique linear propagation property in the stochastic latent space of SDK enables traceability for quality evolution throughout the process, thereby facilitating the design of root cause analysis schemes. Notably, the proposed framework requires minimal knowledge of the underlying physics of production lines. It serves as a virtual metrology tool that can be applied to various MMSs, contributing to the ultimate goal of Zero Defect Manufacturing.
Panel furniture uses an intelligent management system, combined with the production method of splitting orders by process, to achieve large quantities and large-scale manufacturing, but because of the insufficient and incomplete use of technology, capacity bottlenecks still exist. The problem of rework and replenishment is a long-term problem in furniture production. Under the constraints of existing production rules, the time difference of plates rework forces the original batch of plates to wait, which reduces the efficiency of warehousing. From the perspective of intelligent manufacturing for the optimization of the plates rework process, this study, through on-site observation records and data analysis of the production system, aimed to find short-term solutions and long-term solutions. In the short-term response, the time node for the completion of the replenishment is mainly according to the process regulations, and the plates are packaged into the warehouse after the replenishment is completed in batches. The long-term response strategy is mainly to achieve the interconnection of different production systems to achieve mutual information, and the paperless online operation of the plates rework process increases the subjective initiative of each process to improve the overall efficiency of the plates rework process.
Venukumar Sarila, Cheepu Murali Mohan, Kantumunchu Venkata Charan
et al.
Binder jetting (BJ) is one of the major metal additive manufacturing (AM) technology used for the production of intricate metal components using a layer-by-layer approach. It belongs to the more general family of processes known as powder bed fusion procedures, in which a bed of metal powder is first selectively fused together with the help of a binder and then sintered in order to produce the final metal component. Binder Jetting is the sole non-fusion-based powder bed additive manufacturing technology; this means that, unlike laser-based AM procedures, the resulting parts are completely free of residual stresses. Small to medium batch production can be cost-effective due to lower tooling and setup expenses. This analysis focuses on the capacity of some of the most important engineering materials, including titanium, Inconel and stainless steel, to produce intricate geometries with a high degree of precision and accuracy. These materials find extensive use across many applications, including defence, industry, biomedical, aerospace, and other fields.
Roland Lachmayer, Bernd-Arno Behrens, Tobias Ehlers
et al.
The deep-drawability of a sheet metal blank is strongly influenced by the tribological conditions prevailing in a deep-drawing process. Therefore, new methods to influence the tribology represent an important research topic. In this work, the application of a process-integrated lubrication in a deep-drawing process is investigated. Most promising geometries of the lubrication channels and outlet openings are first identified by means of numerical simulation at the example of a demonstrator process. Cylindrical test specimens with the specified channel geometries are additively manufactured and installed in a strip drawing test stand. Additive manufacturing enables the possibility of manufacturing complex channel geometries which cannot be manufactured by conventional methods. A hydraulic metering device for conveying lubricant is connected to the cylindrical test specimens. Thus, hydraulically lubricated strip drawing tests are performed. The tests are evaluated according to the force curves and the fluid mechanical buildup of pressure cushion. The performance of process-integrated lubrication is thus analyzed and evaluated. By means of a coupled forming and SPH simulation, the lubrication channels could be optimally designed. From the practical tests, it could be achieved that the drawing force decreases up to 27% with pressure cushion build up. In this research, a hydraulic lubrication in the area of highest contact normal stresses is the most optimal process parameter regarding friction reduction.
Tandan kosong kelapa sawit (TKKS) merupakan limbah padat industri, Tandan Kosong Kelapa Sawit yang dewasa ini hanya dibuang atau dibakar sehingga menimbulkan pencemaran lingkungan. Salah satu usaha dalam mengatasi hal tersebut adalah memanfaatkannya untuk pembuatan material baru.Pada penelitian ini Variasi arah serat yang digunakan yaitu 0,30,45 60dan 90 dengan variasi massa serat yang digunakan dalam penelitian ini yaitu 5%, 10% dan 15% serat Tandan Kosong Kelapa Sawit. Dari hasil pengolahan data material komposit serat TKKS Harga impak (HI) terbesar terdapat pada spesimen arah 0 dengan persentase serat TKKS 15% sebesar 0,330 J/mm2 dan Harga impak (HI) terkecil terdapat pada spesimen arah 90° dengan persentase serat TKKS 5% sebesar 0,075 J/mm2 dengan bentuk patahan Getas.
Abstract Flexible flow-shop scheduling with dynamic transport waiting times (FFSPDW) is a typical flow shop scheduling problem in smart manufacturing system. In this problem, jobs need to be transported by transporters like AGV (Automated Guided Vehicle) to next stage after processing. During transportation, waiting time dynamically occurs for both jobs and transporters, and finally contributes to makespan. The waiting times are conditioned by buffer capacity, machine allocations and production sequence. In order to minimize makespan with consideration of dynamic waiting times, we proposed a waiting time calculation approach to evaluate waiting time and makespan. This approach considers two situations: infinite buffer capacity and zero buffer capacity. Further, we developed a memetic algorithm integrated with waiting time calculation approach to solve FFSPDW. Finally, we verified the algorithm parameters via analysis of variance. Computational results show that the proposed memetic algorithm is able to reach high quality solutions with short computation time. Consequently, the proposed approach is suitable for solving industrial FFSPDW problems.
ABSTRACT COVID-19 has had a worldwide impact. The consensus is that the sudden pause of global production and the shrinking international trade will contract the global economy. This study explores the short-term impact of the COVID-19 shock on global value chains (GVC), especially considering China’s production-capacity damage. Findings suggest that downstream countries and sectors suffer more from China’s production disruption than upstream ones. The Most impacted countries are the United States, South Korea, Japan, and Germany; while the most-affected sectors include electronic and optical equipment, textiles, machinery, manufacturing, and wholesale trade. It is found that China is too important in GVC to be substituted for in the current world economy.
This paper considers an arbitrarily-varying fading channel consisting of one transmitter, one receiver and an arbitrarily varying adversary. The channel is assumed to have additive Gaussian noise and fast fading of the gain from the legitimate user to the receiver. We study four variants of the problem depending on whether the transmitter and/or adversary have access to the fading gains; we assume the receiver always knows the fading gains. In two variants the adversary does not have access to the gains, we show that the capacity corresponds to the capacity of a standard point-to-point fading channel with increased noise variance. The capacity of the other two cases, in which the adversary has knowledge of the channel gains, are determined by the worst-case noise variance as a function of the channel gain subject to the jammer's power constraint; if the jammer has enough power, then it can imitate the legitimate user's channel, causing the capacity to drop to zero. We also show that having the channel gains causally or non-causally at the encoder and/or the adversary does not change the capacity, except for the case where all parties know the channel gains. In this case, if the transmitter knows the gains non-causally, while the adversary knows the gains causally, then it is possible for the legitimate users to keep a secret from the adversary. We show that in this case the capacity is always positive.
Andreas Wirtz, Mohamed Abdulgader, Michael P. Milz
et al.
Structural elements of offshore facilities, e.g., offshore wind turbines, are subject to static and dynamic mechanical and environmental loads, for example, from wind, waves, and corrosive media. Protective coatings such as thermal sprayed ZnAl coatings are often used for protection, mainly against corrosive stresses. The Machine Hammer Peening (MHP) process is an innovative and promising technique for the post-treatment of ZnAl coating systems that helps reducing roughness and porosity and inducing compressive residual stresses. This should lead to an enhancement of the corrosion fatigue behavior. In this paper, the effect of a thermally assisted MHP process was investigated. The softening of the coating materials will have a direct effect on the densification, residual porosity and the distribution of cracks. The investigation results showed the influence of thermally assisted MHP on the surface properties, porosity, residual stresses, and hardness of the post-treated coatings. The best densification of the coating, i.e., the lowest porosity and roughness and the highest compressive residual stresses, were achieved at a process temperature of 300 °C. A further increase in temperature on the other hand caused a higher porosity and, in some cases, locally restricted melting of the coating and consequently poorer coating properties.
Society is on the cusp of harnessing recent advances in synthetic biology to discover new bio-based products and routes to their affordable and sustainable manufacture. This is no more evident than in the discovery and manufacture of Synthetic Biological Materials, where synthetic biology has the capacity to usher in a new Materials from Biology era that will revolutionise the discovery and manufacture of innovative synthetic biological materials. These will encompass novel, smart, functionalised and hybrid materials for diverse applications whose discovery and routes to bio-production will be stimulated by the fusion of new technologies positioned across physical, digital and biological spheres. This article, which developed from an international workshop held in Manchester, United Kingdom, in 2017 [1], sets out to identify opportunities in the new materials from biology era. It considers requirements, early understanding and foresight of the challenges faced in delivering a Discovery to Manufacturing Pipeline for synthetic biological materials using synthetic biology approaches. This challenge spans the complete production cycle from intelligent and predictive design, fabrication, evaluation and production of synthetic biological materials to new ways of bringing these products to market. Pathway opportunities are identified that will help foster expertise sharing and infrastructure development to accelerate the delivery of a new generation of synthetic biological materials and the leveraging of existing investments in synthetic biology and advanced materials research to achieve this goal.
Capacity is an important quantity in potential theory and in the study of Markov processes. We give equivalent conditions between the capacity, the mean exit time, and the Green function for non-reversible diffusions.