Sodiq Arogundade, Olayinka Bandele, Vanessa Nakayange
et al.
Southern Africa is one of the major suppliers of raw materials necessary for the automotive sector’s supply chain. However, the region remains far behind in integrating into the global supply chain, with the exception of South Africa. Hence, building supply chain resilience is one of the region’s major goals, including the Sustainable Development Goals, Agenda 2063, and the regional supply chain under the African Continental Free Trade Agreement (AfCFTA). This paper presents a regional supply chain mapping of the automotive industry. The article also empirically analyzed the impact of productive capacity on intra-Southern African trade along the automotive value chain. The analysis of the regional value chain (RVC) mapping suggests that South Africa is the most integrated country in the Southern African region across the tiered automotive component value chain, demonstrating a strong position in intra-regional trade for all levels of component production. The empirical analysis of the gravity models (PPML and IV-PPML) underscores the crucial role of productive capacity in enhancing intra-Southern African exports of automotive components, with particular emphasis on Tier 1 components. As the most advanced and high-value-added segment within the automotive value chain, Tier 1 components require a robust foundation of manufacturing capacity, skilled labor and efficient processes to meet the stringent standards of both regional and global markets. The analysis indicates that countries with higher levels of productive capacity are better positioned to serve as reliable suppliers of these essential components, facilitating stronger trade ties within the Southern African region.
Abstract This paper develops a stochastic network-control framework for analyzing structural growth transitions in directed production economies subject to congestion constraints. The economy is modeled as a dynamically evolving weighted production network in which inter-firm linkages generate amplification effects, while infrastructure capacity imposes nonlinear attenuation through congestion. The interaction between network propagation and capacity geometry determines an effective amplification index that governs transitional stability. We show that sustained structural upgrading requires keeping amplification below a critical stability threshold. When propagation dominates attenuation, stochastic shocks spread through the network and throughput stagnates. When capacity expansion anticipates network deepening, attenuation dominates propagation, producing a smooth phase transition toward a high-productivity regime. This shift can be summarized by a macroeconomic order parameter capturing the move from amplification-dominated to attenuation-dominated dynamics. Formulated as a finite-horizon stochastic control problem with endogenous network evolution, Monte Carlo simulations confirm the existence of a stabilization threshold and quantify the volatility-reducing role of anticipatory infrastructure expansion.
Mostafa Meraj Pasha, Md Shakil Arman, Zhijian Pei
et al.
Binder jetting additive manufacturing (BJAM) of silicon carbide (SiC) has been reported in the literature. In the reported studies, the effects of the compaction thickness on the properties of SiC green parts printed by BJAM have largely been unexamined. This study aims to fill this gap in the literature by investigating the effects of the compaction thickness on the density, integrity, and microstructure of SiC green parts printed by BJAM. In this study, experiments were conducted using four levels of compaction thickness at two levels of layer thickness. The results indicate that increasing the compaction thickness enhances the green part density, reaching 1.85 g/cm<sup>3</sup> at a layer thickness of 45 µm and 1.87 g/cm<sup>3</sup> at a layer thickness of 60 µm, respectively. However, a higher compaction thickness might also introduce defects in green parts, such as cracks. Scanning electron microscopy (SEM) analysis confirmed the improved particle packing and reduced porosity with the increased compaction thickness. These findings underscore a trade-off between density and defect formation, providing critical insights for optimizing BJAM process variables for fabricating SiC parts.
Tiago Oliveira Gonçalves Teixeira, Reza Beygi, Ricardo João Camilo Carbas
et al.
This study presents a simple and innovative design to join a 2 mm thick steel sheet to a 5 mm thick aluminium sheet in a butt configuration. Thickness differences were addressed using support plates, while an aluminium run-on plate was employed to prevent the FSW tool from plunging into the steel. The process produced a unique S-shaped Al/St interface, the formation mechanism of which is analysed in this study. Scanning electron microscopy (SEM) observations revealed a gradient in the thickness of intermetallic compounds (IMCs) along the joint interface, decreasing from the top to the bottom. This S-shaped interface led to a 150% increase in the ultimate tensile strength (UTS) of the joint. The mechanism underlying this enhancement, attributed to the curved geometry of the interface and its alignment with the loading direction, is discussed in detail. These findings highlight the potential of this approach for improving the performance of dissimilar material joints in lightweight structural applications.
Maximilian Egger, Rawad Bitar, Antonia Wachter-Zeh
et al.
The problem of identifying the channel with the highest capacity among several discrete memoryless channels (DMCs) is considered. The problem is cast as a pure-exploration multi-armed bandit problem, which follows the practical use of training sequences to sense the communication channel statistics. A capacity estimator is proposed and tight confidence bounds on the estimator error are derived. Based on this capacity estimator, a gap-elimination algorithm termed BestChanID is proposed, which is oblivious to the capacity-achieving input distribution and is guaranteed to output the DMC with the largest capacity, with a desired confidence. Furthermore, two additional algorithms NaiveChanSel and MedianChanEl, that output with certain confidence a DMC with capacity close to the maximal, are introduced. Each of those algorithms is beneficial in a different regime and can be used as a subroutine in BestChanID. The sample complexity of all algorithms is analyzed as a function of the desired confidence parameter, the number of channels, and the channels' input and output alphabet sizes. The cost of best channel identification is shown to scale quadratically with the alphabet size, and a fundamental lower bound for the required number of channel senses to identify the best channel with a certain confidence is derived.
In this work, we investigate the capacity of multi-antenna fading channels with 1-bit quantized output per receive antenna. Specifically, leveraging Bayesian statistical tools, we analyze the asymptotic regime with a large number of receive antennas. In the coherent case, where the channel state information (CSI) is known at the receiver's side, we completely characterize the asymptotic capacity and provide the exact scaling in the extreme regimes of signal-to-noise ratio (SNR) and the number of transmit antennas. In the non-coherent case, where the CSI is unknown but remains constant during T symbol periods, we first obtain the exact asymptotic capacity for T<=3. Then, we propose a scheme involving uniform signaling in the covariance space and derive a non-asymptotic lower bound on the capacity for an arbitrary block size T. Furthermore, we propose a genie-aided upper bound where the channel is revealed to the receiver. We show that the upper and lower bounds coincide when T is large. In the low SNR regime, we derive the asymptotic capacity up to a vanishing term, which, remarkably, matches our capacity lower bound.
Salil Gokhale, Shivika Narang, Samarth Singla
et al.
We study the problem of capacity modification in the many-to-one stable matching of workers and firms. Our goal is to systematically study how the set of stable matchings changes when some seats are added to or removed from the firms. We make three main contributions: First, we examine whether firms and workers can improve or worsen upon changing the capacities under worker-proposing and firm-proposing deferred acceptance algorithms. Second, we study the computational problem of adding or removing seats to either match a fixed worker-firm pair in some stable matching or make a fixed matching stable with respect to the modified problem. We develop polynomial-time algorithms for these problems when only the overall change in the firms' capacities is restricted, and show NP-hardness when there are additional constraints for individual firms. Lastly, we compare capacity modification with the classical model of preference manipulation by firms and identify scenarios under which one mode of manipulation outperforms the other. We find that a threshold on a given firm's capacity, which we call its peak, crucially determines the effectiveness of different manipulation actions.
To satisfy the requirements of five-axis processing quality, this article improves and optimizes the machine tool structure design to produce improved dynamic characteristics. This study focuses on the investigation of five-axis machine tools’ static and dynamic stiffness as well as structural integrity. We also include performance optimization and experimental verification. We use the finite element approach as a structural analysis tool to evaluate and compare the individual parts of the machine created in this study, primarily the saddle, slide table, column, spindle head, and worktable. We discuss the precision of the machine tool model and relative space distortion at each location. To meet the requirements of the actual machine, we optimize the structure of the five-axis machine tool based on the parameters and boundary conditions of each component. The machine’s weight was 15% less than in the original design model, the material it was subjected to was not strained, and the area of the structure where the force was considerably deformed was strengthened. We evaluate the AM machine’s impact resistance to compare the vibrational deformation observed in real time with the analytical findings. During modal analysis, all the order of frequencies were determined to be 97.5, 110.4, 115.6, and 129.6 Hz. The modal test yielded the following orders of frequencies: 104, 118, 125, and 133 Hz. Based on the analytical results, the top three order error percentages are +6.6%, +6.8%, +8.1%, and +2.6%. In ME’scope, the findings of the modal test were compared with the modal assurance criteria (MAC) analysis. According to the static stiffness analysis’s findings, the main shaft and screw have quite substantial major deformations, with a maximum deformation of 33.2 µm. Force flow explore provides the relative deformation amount of 26.98 µm from the rotating base (C) to the tool base, when a force of 1000 N is applied in the <i>X</i>-axis direction, which is more than other relative deformation amounts. We also performed cutting transient analysis, cutting spectrum analysis, steady-state thermal analysis, and analysis of different locations of the machine tool. All of these improvements may effectively increase the stiffness of the machine structure as well improve the machine’s dynamic characteristics and increases its machining accuracy. The topology optimization method checks how the saddle affects the machine’s stability and accuracy. This research will boost smart manufacturing in the machine tool sector, leading to notable advantages and technical innovations.
Peter Kozový, Miroslav Matuš, Vladimír Bechný
et al.
High-feed milling (HFM) represents a progressive manufacturing technology that has recently found widespread application across various industries. HFM is characterized by high machining speed, reduced cycle times, increased overall productivity, and increased tool life. Due to its versatility, HFM is a suitable technology for the application of various materials. The study deals with experimental analysis of cutting forces, machined surface integrity, and statistical evaluation in high-feed machining. In the present study, nickel-copper-based alloy (Monel) was chosen as the machined material, employing HFM with a monolithic ceramic milling cutter. The Monel material is characterized by its excellent mechanical properties and chemical resistance in harsh environments. During machining, cutting forces were recorded in three mutually perpendicular directions. This paper delves into the analysis of the impact of the depth of cut (a<sub>p</sub>), width of cut (a<sub>e</sub>), and lead-in angle (ε). The chosen evaluation characteristics encompass the tool load, primary profile, and the attained roughness of the machined surface. It is noteworthy that the technology under consideration predominantly aligns with the roughing phase of the manufacturing process. Additionally, the investigation incorporates a statistical analysis of the response surface pertaining to the cutting force components, namely Fx, Fy, Fz, and the resultant cutting force F.
Metal matrix composites (MMCs) reinforced with Copper Oxide (CuO) and Aluminum (Al) 6061 (Al6061) alloys are being studied to determine their mechanical, physical, and dry sliding wear properties. The liquid metallurgical stir casting method with ultrasonication was employed for fabricating Al6061-CuO microparticle-reinforced composite specimens by incorporating 2–6 weight percent (wt.%) CuO particles into the matrix. Physical, mechanical, and dry sliding wear properties were investigated in Al6061-CuO MMCs, adopting ASTM standards. The experimental results show that adding CuO to an Al6061 alloy increases its density by 7.54%, hardness by 45.78%, and tensile strength by 35.02%, reducing percentage elongation by 40.03%. Dry wear measurements on a pin-on-disc apparatus show that Al6061-CuO MMCs outperform the Al6061 alloy in wear resistance. Al6061-CuO MMCs’ strength has been predicted using many strengthening mechanism models and its elastic modulus through several models. The strengthening of Al6061-CuO MMCs is predominantly influenced by thermal mismatch, more so than by Hall–Petch, Orowan strengthening, and load transfer mechanisms. As the CuO content in the composite increases, the strengthening effects due to dislocation interactions between the matrix and reinforcement particles, the coefficient of thermal expansion (CTE) difference, grain refinement, and load transfer consistently improve. The Al6061-CuO MMCs were also examined using an optical microscope (OM), energy-dispersive spectroscopy (EDS), X-ray diffraction (XRD), and scanning electron microscopy (SEM) before and after fracture and wear tests. The investigation shows that an Al6061-CuO composite material with increased CuO reinforcement showed higher mechanical and tribological characteristics.
André F. V. Pedroso, Francisco J. G. Silva, Raul D. S. G. Campilho
et al.
Automotive industries require constant technological development and the capacity to adapt to market needs. Hence, component suppliers must be able to adapt to persistent trend changes and technical improvements, acting in response to customers’ expectations and developing their manufacturing methods to be as flexible as possible. Concepts such as layout flexibility, management of industrial facilities, and building information modeling (BIM) are becoming ever more addressed within the automotive industry in order to envision and select the necessary information exchanges. Given this question and based on the gap in the literature regarding this subject, this work proposes a solution, developing a novel tool that allows the monitoring and assignment of newer/relocated equipment to the switchboards within a given industrial plant. The solution intends to increase the flexibility of production lines through the assessment, analysis, improvement, and reorganization of the electrical load distribution to develop projects accurately implying layout changes. The tool is validated with an automotive manufacturer. With the implementation of this open-source tool, a detailed electrical flow management system is accomplished, and it has proven successful and essential in raising levels of organizational flexibility. This has guaranteed the company’s competitiveness with effective integrated administration methods and tools, such as a much easier study upon inserting new/relocated equipment without production line breaks.
The thermodynamic properties of the Boltzmann hard sphere system is discussed. It was found that zero point energy decreases with temperature so slowly that it turned out to be an almost a constant addition to the classical value. In result the heat capacity of the system differs little from the classical value of 1.5 k everywhere except for the narrow region of low temperatures, where heat capacity drops to zero. The predicted linear temperature contribution to the heat capacity like in ideal Fermi gas was clearly detected in the quantum hard sphere system at the lowest temperatures.
Abstract Eggshells offer many advantages as adsorbents, such as affordability without special preparations other than pulverization and calcination. However, the manufacturing industry generally has a severe problem with high concentrations of heavy metals in wastewater. The purpose of this study was to use eggshell byproducts and calcined eggshell treatment for the adsorption of copper in an aqueous solution. The reaction time, metal concentration, adsorbent dose, temperature, and pH were evaluated using primary factors followed by the response surface method (RSM) to investigate the optimum conditions for eggshell byproducts and calcined eggshell adsorption treatment. The results of the one-factor-at-a-time experiment showed that the optimal adsorption rate was obtained from treatment at 24 h, 25 mg/L, 10 mg, and 25 °C. In addition, the effect of pH on the adsorption rates of eggshells and eggshells with membrane were detected at pH values of 5 and 5.9 and found to be 95.2, 90.5, and 73.3%. The reaction surface experiment showed that the best adsorption rate reached 99.3% after calcination at 900 °C for 2 h and a 20 min reaction. The results showed that eggshells, eggshell membranes, eggshells with membrane, and calcined eggshells could be applied to remove copper ions from industrial wastewater. The adsorption capacity of the calcined eggshell is better than that of the non-calcined eggshell and has good neutrality in acidic industrial wastewater. Therefore, it is convenient and practical for practical production and application. Likewise, this study conveys promising findings in the context of improving wastewater treatment based on a circular economy approach to waste reuse in the food industry and represents a valuable direction for future research.
A fabricação de calçados pode ser considerada uma das atividades industriais mais antigas, clássicas e tradicionais, com o processo intensivo de mão de obra. A adoção de novas tecnologias encontra resistência devido à facilidade de contratar a mão-de-obra operacional com salários relativamente baixos, principalmente em países em desenvolvimento. No entanto, o avanço tecnológico sugere a mudança no referido cenário. Este trabalho de pesquisa teve como objetivo evidenciar as alternativas de adoção da tecnologia IoT na produção de calçados. Optou-se pelo estudo de caso múltiplo, em sete indústrias de calçados de grande porte, da região sul do Brasil, utilizando a abordagem qualitativa, com a coleta de dados por meio de entrevistas em profundidade, levantamento documental e observação não participante sistemática. Os resultados facultaram a identificação de diversas atividades operacionais, na produção de calçados, que poderiam ser beneficiadas com a adoção da tecnologia IoT.
Production management. Operations management, Production capacity. Manufacturing capacity
Antonios Dimopoulos, Ilias Zournatzis, Tat-Hean Gan
et al.
Laser Powder Bed Fusion (LPBF) is one of the most commonly used and rapidly developing metal Additive Manufacturing (AM) technologies for producing optimized geometries, complex features, and lightweight components, in contrast to traditional manufacturing, which limits those characteristics. However, this technology faces difficulties with regard to the construction of overhang structures and warping deformation caused by thermal stresses. Producing overhangs without support structures results in collapsed parts, while adding unnecessary supports increases the material required and post-processing. The purpose of this study was to evaluate the various support and process parameters for metal LPBF, and propose optimized support structures to minimize Support Volume, Support Removal Effort, and Warping Deformation. The optimization approach was based on the Design of Experiments (DOE) methodology and multi-response optimization, by 3D printing and studying overhang geometries from 0° to 45°. For this purpose, EOS Titanium Ti64 Grade 5 powder was used, a Ti6Al4V alloy commonly employed in LPBF. For 0° overhangs, the optimum solution was characterized by an average Tooth Height, large Tooth Top Length, low X, Y Hatching, and high Laser Speed, while for 22.5° and 45° overhangs, it was characterized by large Tooth Height, low Tooth Top Length, high X, Y Hatching, and high Laser Speed.
Attractor neural networks (ANNs) are one of the leading theoretical frameworks for the formation and retrieval of memories in networks of biological neurons. In this framework, a pattern imposed by external inputs to the network is said to be learned when this pattern becomes a fixed point attractor of the network dynamics. The storage capacity is the maximum number of patterns that can be learned by the network. In this paper, we study the storage capacity of fully-connected and sparsely-connected networks with a binarized Hebbian rule, for arbitrary coding levels. Our results show that a network with discrete synapses has a similar storage capacity as the model with continuous synapses, and that this capacity tends asymptotically towards the optimal capacity, in the space of all possible binary connectivity matrices, in the sparse coding limit. We also derive finite coding level corrections for the asymptotic solution in the sparse coding limit. The result indicates the capacity of network with Hebbian learning rules converges to the optimal capacity extremely slowly when the coding level becomes small. Our results also show that in networks with sparse binary connectivity matrices, the information capacity per synapse is larger than in the fully connected case, and thus such networks store information more efficiently.
Goran D. Putnik, Vaibhav Shah, Zlata Putnik
et al.
In many discourses, popular as well as scientific, it is suggested that the “massive” use of Artificial Intelligence (AI), including Machine Learning (ML), and reaching the point of “singularity” through so-called Artificial General Intelligence (AGI), and Artificial Super-Intelligence (ASI), will completely exclude humans from decision making, resulting in total dominance of machines over human race. Speaking in terms of manufacturing systems, it would mean that the intelligence and total automation would be achieved (once the humans are excluded).
The hypothesis presented in this paper is that there is a limit of AI/ML autonomy capacity, and more concretely, the ML algorithms will be not able to become totally autonomous and, consequently, the human role will be indispensable. In the context of the question, the authors of this paper introduce the notion of the manufacturing singularity and present an intelligent machine architecture towards the manufacturing singularity, arguing that the intelligent machine will always be human dependent. In addition, concerning the manufacturing, the human will remain in the centre of Cyber-Physical Systems (CPS) and in Industry 4.0. The methodology to support this argument is inductive, similarly to the methodology applied in a number of texts found in literature, and based on computational requirements of inductive inference based machine learning. The argumentation is supported by several experiments that demonstrate the role of human within the process of machine learning. Based on the exposed considerations, a generic architecture of intelligent CPS, with embedded ML functional modules in multiple learning loops, is proposed in order to evaluate way of use of ML functionality in the context of CPS. Similar to other papers found in literature, due to the (informal) inductive methodology applied, considering that this methodology does not provide an absolute proof in favour of, or against, the hypothesis defined, the paper represents a kind of position paper. The paper is divided into two parts. In the first part a review of argumentation from literature in favour of and against the thesis on the human role in future was presented, as well as the concept of the manufacturing singularity was introduced. Furthermore, an intelligent machine architecture towards the manufacturing singularity was proposed, arguing that the intelligent machine will be always human dependent and, concerning the manufacturing, the human will remain in the centre. The argumentation is based on the phenomenon related to computational machine learning paradigm, as intrinsic feature of the AI/ML , through the inductive inference based ML algorithms, whose effectiveness is conditioned by the human participation. In the second part, an architecture of the Cyber-Physical (Production) Systems (CPPS) with multiple learning loops is presented, together with a set of experiments demonstrating the indispensable human role. Finally, a discussion of the problem from the manufacturing community point of view on future of human role in Industry 4.0 as the environment for advanced AI/ML applications is registered.
Additive manufacturing (AM) has become a viable option for producing structural parts with a high degree of geometrical complexity. Despite such trend, accurate material properties, under diversified testing conditions, are scarce or practically non-existent for the most recent additively manufactured (AMed) materials. Such data gap may compromise component performance design, through numerical simulation, especially enhanced by topological optimisation of AMed components. This study aimed at a comprehensive characterisation of laser powder bed fusion as-built 18Ni300 maraging steel and its systematic comparison to the conventional counterpart. Multiaxial double-notched specimens demonstrated a successful depiction of both plastic and damage behaviour under different stress states. Tensile specimens with distinct notch configurations were also used for high stress triaxiality range characterisation. This study demonstrates that the multiaxial double-notched specimens constitute a viable option towards the inverse plastic behaviour calibration of high-strength additively manufactured steels in distinct state of stress conditions. AMed maraging steel exhibited higher strength and lower ductility than the conventional material.
Martin Krajčovič, Beáta Furmannová, Patrik Grznár
et al.
The requirement to achieve effective solutions in the shortest possible time in the manufacturing environment is essential, and it can be solved only by effective production planning methods. The scientific problem is that traditional methods for creating and assessing the production plans are insufficient for the future and it is necessary to look for new alternatives. The planners in the framework of designing the production layouts and subsequent capacity planning of the employees are missing the information, methods and tools for making clear decisions. The production costs in general and especially the costs for the workforce create a large part of the operating costs in many manufacturing enterprises. The scientific goal of the article is to present a design of the system for parametric modelling and assessing the working utilisation of the production staff intended for reducing costs. The described solution is based on object-oriented analysis and contains a methodology of planning and controlling the production process in the industrial environment. The designed methodology was used for developing a planning module of project software and was shown through a case study in a company dealing with the production of automotive components. Effective modelling of the digital copy of the manufacturing system in the software environment is one of the most difficult and important steps for developing reliable information systems for planning and inspection in the industry. The methodology’s results in a company are that the solution can be used as a basis for the aggregate production planning that brings savings and efficiency increases. The research results can be used in any company with strictly defined working positions, working activities, and limiting conditions.