Ultra-precision grinding is widely used in fields such as precision instrumentation, military industry, and aerospace. Focusing on a grinding system based on hydrostatic support, this paper investigates the formation mechanism and variation patterns of roundness error during grinding. The dynamic equations are derived based on the structural characteristics of a vertical grinding system. The uncut chip thickness is formulated, enabling the prediction of grinding forces through mathematical expressions. The dynamic equations are solved using a fully discrete algorithm to obtain the surface profile of the workpiece after machining, and roundness error is extracted using the least squares method. As the speed ratio of the grinding wheel to the workpiece increases, the grinding accuracy improves, and the roundness error can be controlled within 0.2 μm. The farther the grinding force application point is from the center of the slider, the greater the roundness error. Under the condition of meeting the processing range, a shorter grinding wheel contact rod should be selected.
Archishna Bhattacharyya, Arthur Mehta, Yuming Zhao
An important distinction in our understanding of capacities of classical versus quantum channels is marked by the following question: is there an algorithm which can compute (or even efficiently compute) the capacity? While there is overwhelming evidence suggesting that quantum channel capacities may be uncomputable, a formal proof of any such statement is elusive. We initiate the study of the hardness of computing quantum channel capacities. We show that, for a general quantum channel, it is QMA-hard to compute its quantum capacity, and that the entanglement-assisted zero-error capacity under some restrictions is uncomputable; indicative of the fact that quantum channel capacities may generally be undecidable.
Maria Ramard, Romain Laniel, Mathieu Miroir
et al.
Laser cutting is an established, multi-physical process widely adopted by the metallurgical industry. However, this fast industrialisation has had a significant impact on quality control. Reviews from 2008 to 2022 primarily focus on single-criterion quality approaches, targeting defects like the Heat-Affected Zone, surface roughness, or kerf geometry, rather than adopting comprehensive methods. In addition, these studies show that cutting quality can be improved by selecting laser manufacturing parameters and part parameters such as thickness or material. However, the influence of part morphology remains underexplored. Following this observation, this study proposes a generic and complete method adapted from the Failure Modes, Effects and Criticality Analysis, allowing the evaluation of the criticality of all cutting defects in a part. It focuses on six laser cutting defects defined in an international standard and three types of morphology: arcs, angles and segments. The aim is to establish a holistic approach linking morphologies to all defect types. Industrial application reveals that thermal defects are highly influenced by morphology. Burrs and adherent slag are particularly critical in arcs and angles, while segments are less sensitive. This analysis establishes design limits and offers practical tools to improve industrial laser cutting through detailed quality assessments.
In the stamping process of automotive parts, springback is a major problem when using Advanced High-Strength Steel (AHSS). This phenomenon significantly impacts the shape accuracy of products and is difficult to control. This study aims to optimize process parameters such as blank holder force (BHF), die clearance, and blank width to minimize springback in the workpiece. Using optimal process parameters will enhance the efficiency of die compensation processes. The study uses the Finite Element Method (FEM) simulation to predict forming behavior. The case study, Reinforcement-CTR PLR, is made from AHSS grade JSC980Y with a thickness of 1 mm. Four material model combinations were evaluated against actual experiment results to select the most accurate springback prediction model. A full factorial design was used for experiments with varied process parameters. The optimization process used regression and various Artificial Neural Networks (ANNs). From the result, a Deep Neural Network (DNN) with two hidden layers performed with the highest accuracy compared to the other models. The optimal process parameters were identified as 27.62 tons BHF, 1 mm die clearance, and a 290 mm blank width. These optimal results achieved 98.05% of the part area within a displacement tolerance of −1 to 1 mm, closely matching FEM-based validation.
Luis W. Hernández, Yassmin Seid Ahmed, Dagnier A. Curra
et al.
This study investigates multi-objective optimization of end-milling parameters for AISI D2 cold-worked tool steel using GC1130-coated carbide inserts under wet machining, focusing on cutting speed and feed rate per tooth values beyond manufacturer recommendations. The objective was to identify parameter settings that minimize surface roughness while maximizing cutting tool life—two performance criteria that often conflict in practice. A full-factorial design of experiments was implemented, varying the cutting speed (220–310 m/min) and feed rate (0.06–0.25 mm/tooth). Response Surface Methodology (RSM) was used to develop predictive models, and a desirability function approach (DFA) was applied to perform multi-response optimization under three weighting schemes. The statistical models showed strong reliability, with R<sup>2</sup> values of 81.09% for surface roughness and 95.02% for tool life. The optimal settings—220 m/min cutting speed and 0.25 mm/tooth feed—resulted in a tool life of 11.03 min and surface roughness of 0.587 µm. This yielded the highest desirability index (D = 0.8706) under tool-life-prioritized weighting, outperforming other cases by up to 10.69%. These findings offer a practical balance between quality and durability, especially for applications where tool wear is a limiting factor.
Mohamad El Mehtedi, Pasquale Buonadonna, Rayane El Mohtadi
et al.
Fused deposition modeling (FDM) is widely applied in various fields due to its affordability and ease of use. However, it faces challenges such as achieving high surface quality, precise dimensional tolerance, and overcoming anisotropic mechanical properties. This review analyzes and compares the machinability of 3D-printed PLA, PETG, and carbon-fiber-reinforced PETG, focusing on surface roughness and burr formation. A Design of Experiments (DoE) with a full-factorial design was used, considering three factors: rotation speed, feed rate, and depth of cut. Each factor had different levels: rotational speed at 3000, 5500, and 8000 rpm; feed rate at 400, 600, and 800 mm/min; and depth of cut at 0.2, 0.4, 0.6, and 0.8 mm. Machinability was evaluated by roughness and burr height using a profilometer for all the materials under the same milling conditions. To evaluate the statistical significance of the influence of various processing parameters on surface roughness and burr formation in 3D-printed components made of three different materials—PLA, PETG, and carbon-fiber-reinforced PETG—an analysis of variance (ANOVA) test was conducted. This analysis investigated whether variations in rotational speed, feed rate, and depth of cut resulted in measurable and significant differences in machinability results. Results showed that milling parameters significantly affect roughness and burr formation, with optimal conditions for minimizing any misalignment highlighting the trade-offs in parameter selection. These results provide insights into the post-processing of FDM-printed materials with milling, indicating the need for a balanced approach to parameter selection based on application-specific requirements.
Marina Tenório, Rui Ferreira, Victor Belafonte
et al.
Modular timber construction embodies a pioneering and eco-friendly methodology within the building sector. With the notable progress made in manufacturing technologies and the advent of engineered wood products, timber has evolved into a promising substitute for conventional materials such as concrete, masonry, and steel. Beyond its structural attributes, timber brings environmental advantages, including its inherent capacity for carbon sequestration and a reduced carbon footprint compared to conventional materials. Timber’s lightweight nature, coupled with its versatility and efficiency in factory-based production, accelerates modular construction processes, providing a sustainable solution to the growing demands of the building industry. This work thoroughly explores contemporary modular construction using wood as the primary material. The investigation spans various aspects, from the fundamentals of modularity and the classification of modular timber solutions to considerations of layout design, structural systems, and stability at both the building and module levels. Moreover, inter-module joining techniques, MEP (mechanical, electrical, and plumbing) integration, and designs for disassembly are scrutinized. The investigation led to the conclusion that timber modular construction, drawing inspiration from the steel modular concept, consistently utilizes a structural approach based on linear members (timber frame, post-and-beam, etc.), incorporating stability configurations and diverse joint techniques. Despite the emphasis on modularization and prefabrication for adaptability, a significant portion of solutions still concentrate on the on-site linear assembly process of those linear members. Regarding modularity trends, the initial prevalence of 2D and 3D systems has given way to a recent surge in the utilization of post-and-beam structures, congruent with the ascending verticality of buildings. In contrast to avant-garde and bold trends, timber structures typically manifest as rectilinear, symmetric plans, characterized by regular and repetitive extrusions, demonstrating a proclivity for centrally located cores. This work aims to offer valuable insights into the current utilization of modular timber construction while identifying pivotal gaps for exploration. The delineation of these unexplored areas seeks to enable the advancement of modular timber projects and systems, fully leveraging the benefits provided by prefabrication and modularity.
The fishing capacity and capacity utilization for Chinese inshore fleets over the latest 13 years were measured using the DEA method. Relevant models were then established to analyze the relationships between capacity output, capacity utilization, and income, and the function of collecting taxes to control fishing capacity was quantitatively simulated. It was pointed out that the tax system would be effective for curtailing fishing capacity and improving the efficiency of the entire fishing industry in China, provided that the tax rate is not too low. Finally, it was suggested that collecting taxes at a proper rate be implemented for Chinese inshore fishing fleets.
AbstractSince the COVID-19 pandemic, the automotive industry which is regarded as a best practice considering its supply chain has experienced new threats which render its supply chain vulnerable. For instance, the many lockdowns, associated with collapsing of global distribution channels sunk vehicle sales dramatically. The purpose of this study is to identify capabilities to strengthen the resilience of automotive supply chains to pandemics. By using the supply chain resilience framework developed by Sytch et al. [4] we analyze the resilience of the automotive supply chain with evidence from the literature to a pandemic crisis with vulnerability factors similar to those of COVID-19.We do find evidence that seven out of ten capabilities we looked for are present in the automotive supply chain. Capabilities to improve are i.e., multiple sources for tier 1 suppliers, improving risk pooling/sharing, and defining means of production postponement.With the evidence on resilience factors for pandemics, we provide managers with a set of factors to focus on in pandemics. Thus, our study helps managers to better prepare their supply chain to resist global crises such as the COVID-19 pandemic. We used a methodology that can be applied with more secondary and also primary sources and therefore is interesting for researchers.
Large cylindrical worm gear set of ISO type I are manufactured using endmill tools on universal CNC machine tools. This manufacturing method requires neither special gear-generating machines nor special tools. The tooth flank forms of ISO type I cylindrical worm gears are involute helicoids as a standard. The targeted theoretical tooth flanks of the worm and the mating worm wheel are determined based on a tooth contact analysis (TCA) of such worm gear set. The cutting conditions of the worm are determined after the offset distance between the worm axis, and the central axis of the endmill tool is calculated. Afterward, the worm is manufactured by controlling only two axes on machine tools using a swarf milling method by use of the side of the endmill tool under the determined conditions. Meanwhile, the targeted theoretical tooth flanks of the mating worm wheel are modeled in 3-dimensional computer-aided design software, and the worm wheel is manufactured by a swarf milling method in a computer-aided manufacturing process. The comparison of experimental and analytical tooth contact patterns indicates almost no difference between the two tooth contact patterns.
Jorge Luiz Dias Agia, Ernesto Michelangelo Giglio, Oduvaldo Vendrametto
O trabalho investiga como ocorre a moderação da governança colaborativa (G.C.) para a organização das redes (O.R.) do Programa Nacional de Alimentação Escolar nos municípios de Cubatão-SP e Itanhaém-SP. O PNAE é uma rede complexa, com tarefas de especialidades entre vários atores, com importância social e econômica para os municípios. Para investigar o tema foi realizada uma pesquisa qualitativa, descritiva, explicativa e comparativa. Coletaram-se dados de fontes secundárias e entrevistas com roteiro estruturado, construído a partir dos indicadores de G.C. e O.R. Os dados permitem afirmar o benefício do avanço no conhecimento sobre a moderação da G.C. na O.R., já que surgiram exemplos de ajustes de regras e normas na rede. O benefício metodológico é a oferta de uma matriz de indicadores que se mostrou operacional e confiável.
Production management. Operations management, Production capacity. Manufacturing capacity
Baldur Steingrimsson, Ankur Agrawal, Xuesong Fan
et al.
The authors present a generic framework for the parameter optimization of additive manufacturing (AM) processes, one tailored to a high-throughput experimental methodology (HTEM). Given the large number of parameters, which impact the quality of AM-metallic components, the authors advocate for partitioning the AM parameter set into stages (tiers), based on their relative importance, modeling one tier at a time until successful, and then systematically expanding the framework. The authors demonstrate how the construction of multi-dimensional functions, based on neural networks (NN), can be applied to successfully model relative densities and Rockwell hardness obtained from HTEM testing of the Inconel 718 superalloy fabricated, using a powder-bed approach. The authors analyze the input data set, assess its suitability for predictions, and show how to optimize the framework for the multi-dimensional functional construction, such as to obtain the highest degree of fit with the input data set. The authors also compare and contrast the NN-based multi-dimensional functional construction to multi-variate linear regression, to polynomial regression, and to Gaussian process regression (GPR), highlight similarity between the NN-based multi-dimensional functional construction and the GPR, and offer insights into the suitability of each of these methods for the data set and the application at hand. In terms of the coefficient of determination, $R^2$, a relatively simple, single-layer NN with 5 or 10 nodes outperforms multi-variate linear regression, 2nd-order polynomial regression, and GPR for the primary Inconel 718 HTEM data set studied. The novelty of the research work entails the versatile and scalable NN framework presented, suitable for use in conjunction with HTEM, for the AM parameter optimization of superalloys, and beyond.
AbstractIn many production applications, plants that produce multiple products with random demands share the required items among suppliers. The decision of how to allocate requests between suppliers to achieve the desired level of customer service is relevant to the efficiency of the production network. The literature highlighted how the long chain has the same level of performance as the full flexible network. This research proposes a decision model based on the game theory model to improve the performance of the production network. The model uses the Gale-Shapley algorithm with low computational complexity to share the demand among the suppliers. A simulation environment allows the evaluation of the proposed model in different conditions, and the model is compared to the dedicated, full flexibility, and long chain models. The numerical results show how the proposed model improves the efficiency of the production environment by keeping the number of connections with the supplier closer to the long chain model.
Mulla Ahmet Pekok, Rossitza Setchi, Michael Ryan
et al.
Research in metal matrix composites (MMCs) indicates that superior mechanical properties may be achieved by embedding reinforcement materials. However, the development of new composite powder for additive manufacturing requires an in-depth understanding of its key characteristics prior to its use in the fabrication process. This paper focuses on the low-energy ball milling (LEBM) of aluminium 2024 alloy (AA2024) reinforced with graphene nanoplatelets (GNPs). The main aim is to investigate the effect of the milling time (from 0.5 to 16 h) on the morphology and flowability of the powder. The study shows that, while short milling times (under 2 h) could not break the Van der WaRals forces between nanoparticles, GNPs were well separated and sufficiently covered the powder surface after 4 h of milling, thanks to the continuously applied impact energy. Longer milling time provides increasingly similar flowability results, confirmed by both the experimental work and discrete element model (DEM) simulations. Moreover, the ball milling process decreases the crystallite size of the milled powder by 24%, leading to a 3% higher microhardness. Lastly, the surface energy of the powder was determined as 1.4 mJ/m<sup>2</sup> by DEM, using the angle of repose of the as-received powder from experimental work.
The products manufacturing companies aim to increase their production capacity as well as the quality of their products in order to achieve their marketing plans and increase their competitiveness with other companies. In order to achieve this, companies strive to move forward in the processes of facilitating production processes and reduce the cost of production through multiple locations of implementation of product parts and components according to the nature of Each part and raw materials used in its production, as well as the number of labor required to manufacture it and the cost of that labor.In addition to the tendency of some companies to assign the implementation of some of these components and parts to multiple countries according to what they are distinguished geographically and logistically ... etc., which resulted in the distribution of some industrial institutions to produce components of their products to become in multiple regions around the world.Although the productive institutions have achieved that desired goal of reducing the cost of production, but they are a front for new problems in the process of communication and exchange of information between multiple production points spread around the world, especially with the multiplicity of languages, cultures, time differences ... etc., and this resulted in wasting more time in Facilitating the communication process and transferring the information necessary to complete the manufacturing and assembly operations in the optimum way and with the required quality, which must be carried out quickly within the life cycle of the product during its production, from design and production planning to manufacturing and assembly.Perhaps the stage of assembling the components and parts of the product is the most important articulated stage in the production process because of its high accuracy and good communication between both (the design and production stages) and (the production stages of each other). With pictures and diagrams, which imposes on the workers the sequence between reading these instructions and realizing them correctly, then implementing the assembly process in practice, which results in a high probability of distracting and slowing the production process, which results in wasting time and thus reduces the efficiency of the production line.From the above, the importance of adopting augmented reality emerges as one of the effective solutions in achieving communication and information transfer between parts and components designers and those who manufacture and assemble them and carry out maintenance operations for them.
This paper combines the digital twin system modeling method to conduct an in-depth study and analysis of graph-theoretic combinatorial optimization. This paper provides new ideas and approaches for optimal numerical analysis work by studying the digital twin modeling method that integrates digital modeling and graph theory combination, provides theoretical support for safe, stable, and economic operation of the system, proposes a solution for digital twin model based on big data platform, focuses on the nearest neighbor propagation (AP) and graph theory combination, solves the digital twin real-time monitoring data asynchronous, incomplete problem, and applies the algorithm to the digital twin model based on the big data platform for data preprocessing to achieve better results. This paper also presents a web-based digital twin system based on intelligent practical needs, analysis, and comparison of existing models, combined with digital twin technology, detailing the differences and connections between the various levels of numerical analysis and the implementation of this data in various fields, such as user management, equipment health management, product quality management, and workshop 3D navigation and detailed modeling of the digital twin system based on this numerical analysis to realize remote online monitoring, analysis, and management. In this paper, for the numerical analysis process, firstly, the key technologies of modeling and simulation operation control of production line based on digital twin are studied, and the rapid response manufacturing system based on a digital twin is designed and validated. Secondly, a scheduling technology framework for capacity simulation evaluation and optimization is established, and batching optimization, outsourcing decision, and rolling scheduling techniques are thus proposed to form a batching optimization algorithm based on priority rules, which realizes batching processing, outsourcing decision, and rolling scheduling of production orders to optimize equipment utilization and capacity. Finally, digital twin-based modeling is designed, and the validation results demonstrate the system’s superior performance in achieving information interaction between physical and virtual production lines, optimization of numerical analysis, and display of results.
Volker Böß, Berend Denkena, Marc-André Dittrich
et al.
Additive manufacturing is typically a flexible alternative to conventional manufacturing processes. However, manufacturing costs increase due to the effort required to experimentally determine optimum process parameters for customized products or small batches. Therefore, simulation models are needed in order to reduce the amount of effort necessary for experimental testing. For this purpose, a novel technological simulation method for directed energy deposition additive manufacturing is presented here. The Dexel-based simulation allows modeling of additive manufacturing of varying geometric shapes by considering multi-axis machine tool kinematics and local process conditions. The simulation approach can be combined with the simulation of subtractive processes, which enables integrated digital process chains.
We compute the capacity of entanglement in the bipartite random pure state model using the replica method. We find the exact expression of the capacity of entanglement which is valid for a finite dimension of the Hilbert space. We argue that in the gravitational path integral, the capacity of entanglement receives contributions only from the sub-leading saddle points corresponding to the partially connected geometries.
Aline M. Barreiro, Geneviève K. Pinheiro, Bruno N. Wesling
et al.
Inkjet printing presents a high potential for cost reduction of electronic devices manufacturing due to the capacity to deposit materials with high precision, less material waste, and large-scale production through the roll-to-roll printing processes. In this work, a nanostructured TiO2 ink was developed using TiO2 aerogel and an alkaline aqueous solution, which resulted in a very stable suspension. A high-intensity ultrasonic mixer was used to fragment and disperse TiO2 aerogels producing suspensions with particles smaller than 200 nm, which are suitable for the inkjet printing process. For the development of the ink, the viscosity and surface tension were adjusted by using glycerol and a surfactant (Triton X-100). The influence of those components on the properties of the ink was evaluated for different concentrations. After formulation of the inks, the printing parameters were adjusted to optimize the process. Films with high surface area and less than 100 nm grain size were successfully produced. Electrical measurements revealed a resistive-like behavior with the sheet resistance increasing with number of printed layers.
Materials of engineering and construction. Mechanics of materials