Tiago Santos, Luc Hennetier, Vítor A. F. Costa
et al.
Porcelain ware typically undergoes multiple firing stages, including decoration firing at temperatures that depend on the desired effect. Conventional decorative firing in gas tunnel kilns takes up to 90 min, whereas microwave heating offers a faster alternative, of around 50 min firings for both low- (~800 °C) and high-fire colors (~1200 °C). However, temperature assessment during microwave firing remains challenging. This study investigates the color changes in overglaze-decorated hard microwave and conventional porcelain firing. Decals with temperature-sensitive pigments (silver and blue) were applied to the porcelain pieces. Color coordinates (L*, a*, b*) were analyzed, comparing microwave and electrically fired samples with the gas-fired reference counterparts. Microwave-fired samples required lower temperatures to match the color of electrically fired samples. Electrically fired pieces at 900 °C are visually comparable to those processed in both a microwave furnace and a gas kiln at the same temperature of 800 °C. Color differences among different heating methods decrease as firing temperature increases. Microwave firing allows similar decorative results to be achieved as with conventional gas firing, while being faster and more energy efficient. Microwave porcelain firing is thus a viable and eco-friendly alternative for porcelain decoration, and the decals’ color changes can be used for temperature assessment during firing.
Marina A. Volosova, Sergei A. Kusmanov, Tatiana L. Mukhacheva
et al.
Nitriding is one of the technologies used to improve the performance properties of tool steels. In addition to traditional gas nitriding, technologies aimed at increasing the productivity and/or efficiency of processing have found application in industry. This paper presents the results of using vacuum and plasma electrolytic nitriding to improve the wear resistance of M2 high-speed steel, as well as prospects for further development of these technologies. XRD and SEM methods were used to study the structural and phase changes in the surface layers, showing the formation of layers of connections hardened to 1200–1300 HV. Tribological tests were carried out under dry friction conditions according to the shaft-shoe scheme with varying sliding speed. The mechanism of contact interaction during the friction of nitrided steel was established. It is shown that plastic deformations are the prevailing type of deformation. The mechanism of destruction of the surface layer is fatigue wear during dry friction without lubrication. This provides an increase in wear resistance by 6–29 times.
This study pioneers the innovative development and comprehensive characterisation of fusilli pasta enriched with protein hydrolysates derived from underutilised fish by-products (salmon, sea bass and tuna) materials typically discarded during seafood processing despite their significant nutritional potential. Fish hydrolysates were incorporated into durum wheat semolina at 1% and 3% concentrations to evaluate their impact on pasta quality and nutritional profile. Results demonstrated improvements in protein content and Omega-3 fatty acid profile across all enriched formulations compared to control samples. This incorporation significantly influenced technological properties, including optimal cooking time, weight gain, hydration capacity, cooking loss and colour parameters. Molecular weight fractionation revealed bioactive peptides with potential antioxidant properties, primarily in low molecular weight fractions. The microbiological assessment confirmed the absence of enterobacteria, moulds and yeasts in the final pasta products, although high viable microbial counts were detected in the raw hydrolysates. This research demonstrates the feasibility of valorising fish processing by-products to create innovative, nutrient-dense functional foods that align with contemporary consumer demands for sustainable production practices and enhanced nutritional profiles. Furthermore, it establishes a promising approach to circular economy principles within the by-product and pasta manufacturing sectors, potentially reducing waste whilst delivering products with improved health-promoting properties.
Nutrition. Foods and food supply, Food processing and manufacture
Murilo Zamboni Alvarenga, Marcelo Moll Brandão, Marcos Paulo Valadares de Oliveira
et al.
O presente estudo aborda o impacto da orientação analítica nas cadeias de suprimentos, um tema que ainda foi pouco explorado em profundidade. O objetivo principal deste trabalho é ampliar o conhecimento sobre como a orientação analítica afeta a resiliência nas cadeias de suprimentos. Para alcançar esse objetivo, foi realizada uma pesquisa com 143 profissionais-chave de indústrias de transformação, e os dados foram analisados por meio de modelagem de equações estruturais. Os resultados destacaram a importância dos mecanismos testados, em conjunto, para explicar a relação entre orientação analítica e resiliência. Assim, a principal contribuição teórica deste artigo é a identificação da prevenção e da adaptação como mediadoras da relação entre orientação analítica e resiliência nas cadeias de suprimentos. Reconhecendo a importância da prevenção e adaptação como mediadores, gestores podem na prática implementar estratégias para fortalecer esses aspectos e aumentar a resiliência das cadeias diante de desafios. Essa abordagem reduz vulnerabilidades, melhora a capacidade de resposta e mantém a eficiência operacional, gerando vantagem competitiva no mercado.
Production management. Operations management, Production capacity. Manufacturing capacity
Brian Parrott, Angelica Coronado Preciado, Eric Feron
The trade-off between resolution and speed represents a significant challenge when extrusion-based additive manufacturing (AM) is used for large-format additive manufacturing (LFAM). This paper presents an analysis of a new material extrusion process, named selective sheet extrusion (SSE), that aims to decouple these parameters. Unlike traditional single-nozzle material extrusion processes, SSE utilizes a single, very wide nozzle through which extrusion is controlled by an array of dynamically actuated teeth at the nozzle outlet. This allows the system to deposit a selectively structured sheet of material with each pass, potentially enabling the deposition of an entire layer of a part in a single pass. An analysis of the theoretical performance of the SSE technology, in terms of speed and material efficiency in comparison with single-nozzle extrusion systems, predicted speed increases of 2–3 times for the geometries that were explored. The analysis was then validated through experimental work that indicated a normalized improvement in print speed of between 2.3 and 2.5 times using a proof-of-concept SSE prototype. The SSE concept expands the opportunity frontier of LFAM technologies by enabling enhanced print speeds, while maintaining higher resolutions at scale. This enhancement in speed and/or resolution could have significant benefits, especially in large-scale prints that benefit from enhanced internal resolution.
In 3D printing, the layered structure often results in artifacts. This effect becomes stronger for surfaces with a lower ramp angle. This effect can be mitigated by manufacturing parts with non-planar layers that fit the parts’ surface geometry. Using the open-source slicing software PrusaSlicer. an algorithm was developed to modify the slicer’s input and output data in a way that fits parts with low ramp angle surfaces. To achieve consistent part quality, all layers were modified to be printed in a non-planar way. The test results indicate that the proposed methods can significantly reduce surface roughness. Although the algorithm works well for parts with a flat base and vertical walls, it would need to be highly adapted to work for different part geometries. Additionally, compared to other algorithms used in Curved-Layer Fused Deposition Modeling (CLFDM), the changed layer structure introduces a changed visual appearance of parts.
Mahrukh Sadaf, Mario Bragaglia, Lidija Slemenik Perše
et al.
Additive manufacturing (AM) has attracted huge attention for manufacturing metals, ceramics, highly filled composites, or virgin polymers. Of all the AM methods, material extrusion (MEX) stands out as one of the most widely employed AM methods on a global scale, specifically when dealing with thermoplastic polymers and composites, as this technique requires a very low initial investment and usage simplicity. This review extensively addresses the latest advancements in the field of MEX of feedstock made of polymers highly filled with metal particles. After developing a 3D model, the polymeric binder is removed from the 3D-printed component in a process called debinding. Furthermore, sintering is conducted at a temperature below the melting temperature of the metallic powder to obtain the fully densified solid component. The stages of MEX-based processing, which comprise the choice of powder, development of binder system, compounding, 3D printing, and post-treatment, i.e., debinding and sintering, are discussed. It is shown that both 3D printing and post-processing parameters are interconnected and interdependent factors, concurring in determining the resulting mechanical properties of the sintered metal. In particular, the polymeric binder, along with its removal, results to be one of the most critical factors in the success of the entire process. The mechanical properties of sintered components produced through MEX are generally inferior, compared with traditional techniques, as final MEX products are more porous.
In this paper we study properties of a variant of the $1/2$-caloric capacity, called $1/2$-symmetric caloric capacity. The latter is associated simultaneously with the $1/2$-fractional heat equation and its conjugate. We establish its semi-additivity in $\mathbb{R}^{n+1}$ and, moreover, we compute explicitly the $1/2$-symmetric caloric capacity of rectangles, which illustrates its anisotropic behavior.
Large pretrained self-attention neural networks, or transformers, have been very successful in various tasks recently. The performance of a model on a given task depends on its ability to memorize and generalize the training data. Large transformer models, which may have billions of parameters, in theory have a huge capacity to memorize content. However, the current algorithms for the optimization fall short of the theoretical capacity, and the capacity is also highly dependent on the content. In this paper, we focus on the memory capacity of these models obtained using common training algorithms and synthetic training data. Based on the results, we derive an empirical capacity model (ECM) for a generic transformer. The ECM can be used to design task-specific transformer models with an optimal number of parameters in cases where the target memorization capability of the task can be defined.
Determining the capacities of quantum channels is one of the fundamental problems of quantum information theory. This problem is extremely challenging and technically difficult, allowing only lower and upper bounds to be calculated for certain types of channels. In this paper, we prove that every quantum channel $Λ$ in arbitrary dimension, when contaminated by white noise in the form $Λ_x(ρ)=(1-x)Λ(ρ)+x\tr(ρ) \frac{I}{d}$, completely loses its capacity of transmitting quantum states when $x\geq \frac{1}{2}$, no matter what type of encoding and decoding is used. In other words, the quantum capacity of the channel vanishes in this region. To show this, we find a channel ${\cal N}_x$, which anti-degrades the depolarizing channel when $x\geq \frac{1}{2}$. We also find the quantum capacity of the complement of the depolarizing channel in closed form. Besides the erasure channel, this is the only example of a parameteric channel in arbitrary dimension for which the quantum capacity has been calculated in closed form.
This paper addresses the capacitated periodic review inventory control problem, focusing on a retailer managing multiple products with limited shared resources, such as storage or inbound labor at a facility. Specifically, this paper is motivated by the questions of (1) what does it mean to backtest a capacity control mechanism, (2) can we devise and backtest a capacity control mechanism that is compatible with recent advances in deep reinforcement learning for inventory management? First, because we only have a single historic sample path of Amazon's capacity limits, we propose a method that samples from a distribution of possible constraint paths covering a space of real-world scenarios. This novel approach allows for more robust and realistic testing of inventory management strategies. Second, we extend the exo-IDP (Exogenous Decision Process) formulation of Madeka et al. 2022 to capacitated periodic review inventory control problems and show that certain capacitated control problems are no harder than supervised learning. Third, we introduce a `neural coordinator', designed to produce forecasts of capacity prices, guiding the system to adhere to target constraints in place of a traditional model predictive controller. Finally, we apply a modified DirectBackprop algorithm for learning a deep RL buying policy and a training the neural coordinator. Our methodology is evaluated through large-scale backtests, demonstrating RL buying policies with a neural coordinator outperforms classic baselines both in terms of cumulative discounted reward and capacity adherence (we see improvements of up to 50% in some cases).
Samir Khrais, Hadeel Al Hmoud, Ahmad Abdel Al
et al.
This study investigates the impact of gas metal arc welding (GMAW) parameters on the bead geometry and material distortion of AISI 316L. Three parameters—arc current in ampere (A), filler feed rate (m/min), and gas composition—were modified at varying levels in order to examine their effects. This study sheds new light on MAG welding lines’ physical properties and behavior and highlights the influence of quaternary shielding gas compositions. Taguchi analysis, which includes signal-to-noise (S/N) ratio and analysis of variance (ANOVA), was utilized to analyze and optimize the welding parameters. This study found that arc current significantly impacts bead geometry, while the shielding gas composition has the most significant effect on angular distortion and transverse shrinkage. The optimal welding parameters for achieving the best bead height and width are 160 A, 3.5 m/min, G1, with a bead height of 4.89 mm, and 120 A, 3 m/min, G2, with a bead width of 6.69 mm. Moreover, the optimal welding parameters for minimizing both angular distortion and transverse shrinkage are 120 A, 4 m/min, G2, resulting in an angular distortion value of 0.0042° and a transverse shrinkage value of 0.0254 mm. This research has practical implications for improving welding performance and can contribute to the advancement of MAG and MIG welding in manufacturing applications.
Mehmet Tahsin Şahin, Liudmyla Niemets, Mutlu Yilmaz
et al.
Formulation of the problem. The study examines knowledge-intensive business services (KIBS) firms' capability to access, process, and transform information into innovation. KIBS are defined as facilitators, carriers of knowledge, and sources of innovation for other sectors. KIBS play an important role in the production, use, and transfer of knowledge to the manufacturing sector. KIBS activities do not demonstrate a uniform structure within themselves, so a dual classification as professional services (P-KIBS) and technological services (T-KIBS) based on functioning and input has been developed. KIBS activities are concentrated in large cities. Major cities or capitals have well-developed infrastructure, public administration centers, advanced social activities, and numerous research institutes and universities. All of them attract a highly skilled population. Regarding metropolitan city economic growth, KIBS stand out because of their high added value, high income, high innovation returns, and high financial capacity, and they contribute to development.
The purpose. The present study aims to reveal the innovative capacities and dynamics of P-KIBS and T-KIBS firms operating in the metropolitan area of Ankara, Turkey's capital city. To reach this aim, Turkey's capital city Ankara is analyzed by using the results of a questionnaire applied to 410 small and medium-sized (SMEs) KIBS firms, 146 of which are P-KIBS firms and other 264 are T-KIBS firms.
Methods. In the course of the research and preparation of the article, the authors used several scientific methods, both philosophical and general scientific, as well as specific scientific methods. In particular, the methods of analysis and synthesis, induction and hypothetical-deductive method, mathematical-statistical and spatial analysis, methods of grouping and classification, questionnaires and surveys, etc., were used.
The results. There are notable differences between the P-KIBS and T-KIBS firms, considering their spatial distribution patterns and the dynamics of their innovation processes. The spatial distribution patterns of the KIBS firms were revealed. While T-KIBS activities demonstrate a spatial clustering tendency independent of the CBD, the P-KIBS firms prefer to locate within the CBD or its vicinity. KIBS sectors generally prefer to be situated by high-income residents, new settlements, secure and prestigious areas close to large public institutions such as ministries and general directorates. It was revealed that the T-KIBS firms are most densely located in the CBD and newly developing business districts of Balgat and Söğütözü, and in the Technology Development Zones of Ankara. P-KIBS firms, on the other hand, are more widely spread in the Çankaya and Yenimahalle districts. It was found that advances in information and communication technologies have a varied impact on the location selection preferences of P-KIBS and T-KIBS enterprises. According to the research results, the factors influencing the clustering of P-KIBS companies and T-KIBS firms were identified and determined. There is a significant relationship between the innovative P-KIBS and T-KIBS firms and their collaboration with other institutions. As a result of the the study, it has been determined that there is a significant relationship between collaboration, R&D, intrafirm and extrafirm social relationships on the innovation of KIBS firms and also contrasting innovation dynamics related to different classes of KIBS in metropolitan areas.
Franz Reuther, Sven Winter, Sebastian Fritsch
et al.
At present, there are no experimental methods that allow for the complete direction-dependent mechanical characterization of tubes. This considerably limits the parameterization of complex, anisotropic material models. The present study introduces a new approach to overcome these limitations: tube sections are first flattened into a planar geometry; then, samples for uniaxial testing are taken out of the flattened tube section and used for parameter identification. In this paper, special emphasis is placed on the intermediate step of flattening, which is investigated in detail both numerically and experimentally. Flattening by pressing is identified as the most advantageous of several options, and the procedure is optimized by numerical simulations that address springback compensation. Experimental validation is performed on tubes (steel E235) with a diameter of 60 mm and an average wall thickness of 1.524 mm. Tube sections are successfully flattened in a custom-built tool with only small remaining out-of-plane displacements after flattening. The numerically predicted pressing force curves agree very well with the experimental data.
Predrag Mitić, Suzana Petrović Savić, Aleksandar Djordjevic
et al.
This research focuses on small- and medium-sized businesses that provide machining or other process services but do not produce their own products. Their daily manufacturing schedule varies according to client needs. Small- and medium-sized businesses strive to operate in these circumstances by extending their customer base and creating adequate production planning targets. Their resources are limited, including the technical and technological components of their equipment, tools, people resources, time, and capacities. As a result, planning operations with the present resources of small- and medium-sized businesses in the midst of the global economic crisis is a widespread issue that must be addressed. This study seeks to offer a novel mathematical optimization model based on a genetic algorithm to address job shop scheduling and capacity planning difficulties in small- and medium-sized businesses, therefore improving performance management and production planning procedures. On the basis of the created optimization model, an appropriate software solution, and quantitative data concerning the job shop scheduling and capacity planning challenges of manufacturing operations in small- and medium-sized businesses, the study findings will be obtained. The practical implications include the establishment and development of a decision support system based on the genetic algorithm optimization method, which may improve the effectiveness of the flexible job shop scheduling problem and capacity planning in the production planning process. The given model and the application of the differential precedence preservative crossover operator within genetic algorithms are what constitute the novelty of this study.
Johnathon B. Hunt, Brian A. Mazzeo, Carl D. Sorensen
et al.
Friction stir welding (FSW) is an advantageous solid-state joining process that is suitable for many materials in multiple industries. In an industrial setting, manufacturers are actively seeking faster welding speeds to increase throughput. Increasing welding speed limits the size of defect-free parameter windows, which may increase the frequency of defects. The push for faster welding speeds emphasizes the need for economical non-destructive evaluation (NDE) for FSW, like any other type of welding. This work introduces a generalized defect detection method that recognizes the stochastic nature of the FSW process, and that can be generally applied to FSW of a material across a dynamic range of process parameters and welding conditions. When applied to aluminum friction stir-welded blanks at speeds ranging from 1500 to 3000 mm/min with varying ranges of tool tilts, the methodology proved 100% effective at positive detection when defects were present with zero scrap rate. Furthermore, additional development demonstrated the proposed stochastic approach can be used to detect the spatial location of a defect within a weld with 94% detection accuracy and a 4.2% scrap rate.
Lean manufacturing is a production method focused on reducing production times, eliminating waste, and synchronizing production with fluctuating demand. A standard lean manufacturing methodology is the product wheel, a repeating sequence of production of various items. If this product wheel sequence is short, it is easier to interrupt or alter production to adjust for failures or fluctuations in demand, so the manufacturing process is leaner. However, a sequence that is too short results in frequent changeover from the production of one item to the next, yielding higher costs. This study formulates the product wheel methodology as an optimization problem and proposes two approaches to solving this problem: one via a relaxation to integer linear programming, and another via the probabilistic optimization technique of simulated annealing. We assess the performance of these two approaches through simulations and analyze the relationships between production leanness and costs.
Throughput capacity of large ad hoc networks has been shown to scale adversely with the size of network $n$. However the need for the nodes to find or repair routes has not been analyzed in this context. In this paper, we explicitly take route discovery into account and obtain the scaling law for the throughput capacity under general assumptions on the network environment, node behavior, and the quality of route discovery algorithms. We also discuss a number of possible scenarios and show that the need for route discovery may change the scaling for the throughput capacity dramatically.