In this paper, the potential capacity and spatial efficiency of future autonomous lane-free traffic in urban environments are explored using a combination of analytical and simulation-based approaches. For lane-free roadways, a simple analytical approach is employed, which shows not only that lane-free traffic offers a higher capacity than lane-based traffic for the same street width, but also that the relationship between capacity and street width is continuous under lane-free traffic. To test the potential capacity and properties of lane-free signal-free intersections (automated intersection management), two approaches were simulated and compared, including a novel approach which we call OptWULF. This approach uses a multi-agent conflict-based search approach with a low-level planner that uses a combination of optimization and simple window-based reservation. With these simulations, we confirm the continuous relationship between capacity and street width for intersection scenarios. We also show that OptWULF results in an even utilization of the entire drivable area of the street and intersection area. Furthermore, we show that OptWULF is capable of handling asymmetric demand patterns without any substantial loss in capacity compared to symmetric demand patterns.
Vincenzo Lunetto, Manuela De Maddis, Franco Lombardi
et al.
Friction stir welding (FSW) is a pivotal technology with ongoing relevance across industries. Renowned for its ability to join materials with dissimilar melting points while mitigating thermal distortions, FSW offers relevant advantages over traditional fusion welding. However, the adoption of FSW for high-strength alloys poses notable challenges, including: (i) accelerated tool wear, (ii) the need for special tool features tailored to these alloys, and (iii) a narrow process window. This review provides a comprehensive overview of FSW as an advanced technique for joining metal alloys for several industrial fields. Emphasis is on materials such as Mg-, Cu-, Ti-, and Ni-based alloys, automotive steels, stainless steels, and maraging steels. The research highlights the critical influence of tool design—main dimensions, features, and materials—and process parameters—rotational and welding speeds, tilt angle, and plunge depth or vertical load—also considering their influences on defect formation. Detailed insights are provided into material flow and the formation of the different weld regions, including SZ, TMAZ, and HAZ.
Debojjal Bagchi, Kyle Bathgate, Kenneth N. Mitchell
et al.
This paper presents a framework for estimating the capacity of a multimodal maritime port system handling vessels of multiple classes. Port system capacity can be categorized into two distinct types: operating capacity, defined as the maximum number of vessels that can be processed over an extended period under stable operating conditions, and ultimate capacity, defined as the absolute maximum vessel throughput achievable irrespective of stability. Distinguishing between these two capacity measures is critical for long-term planning and resilience analysis, as ports may temporarily operate above sustainable levels following disruptions or during demand surges. Despite the importance of this distinction, existing port capacity models generally do not provide methods to compute port-level capacity estimates that clearly differentiate between operating and ultimate capacity. We introduce methods to estimate both capacity measures for seaport systems. We apply the proposed framework using the Port of Houston, Texas as a case study. Operating capacity is estimated using a parsimonious queueing-theoretic model, while ultimate capacity is estimated by fitting an ordinary differential equation model to simulation outputs. We estimate an operating capacity of approximately 0.9 vph and an ultimate capacity of approximately 1.4 vph for the Port of Houston. Sensitivity analysis of key port resources indicates that liquid-bulk terminals constitute the primary bottlenecks under stable operating conditions, whereas pilot availability becomes the dominant bottleneck following disruptions. These methods can be used in port planning to determine the expected operational and resilience gains of a given infrastructure intervention, or to identify bottlenecks in a complex, multimodal port environment.
Millend Roy, Agostino Capponi, Vladimir Pyltsov
et al.
Electricity markets are under increasing pressure to maintain reliability amidst rising renewable penetration, demand variability, and occasional price shocks. Traditional capacity market designs often fall short in addressing this by relying on expected-value metrics of energy unserved, which overlook risk exposure in such systems. In this work, we present CapOptix, a capacity pricing framework that interprets capacity commitments as reliability options, i.e., financial derivatives of wholesale electricity prices. CapOptix characterizes the capacity premia charged by accounting for structural price shifts modeled by the Markov Regime Switching Process. We apply the framework to historical price data from multiple electricity markets and compare the resulting premium ranges with existing capacity remuneration mechanisms.
Great Britain aims to meet growing electricity demand and achieve a fully decarbonised grid by 2035, targeting 70 GW of solar photovoltaic (PV) capacity. However, grid constraints and connection delays hinder solar integration. To address these integration challenges, various connection reform processes and policies are being developed [1]. This study supports the connection reforms with a model that estimates regional PV capacity at the NUTS 3 level, explaining 89% of the variation in capacity, with a mean absolute error of 20 MW and a national mean absolute percentage error of 5.4%. Artificial surfaces and agricultural areas are identified as key factors in deployment. The model has three primary applications: disaggregating national PV capacity into regional capacity, benchmarking regional PV deployment between different regions, and forecasting future PV capacity distribution. These applications support grid operators in generation monitoring and strategic grid planning by identifying regions where capacity is likely to be concentrated. This can address grid connection delays, plan network expansions, and resolve land-use conflicts.
Additive manufacturing (AM) and topology optimization (TO) emerge as vital processes in modern industries, with broad adoption driven by reduced expenses and the desire for lightweight and complex designs. However, iterative topology optimization can be inefficient and time-consuming for individual products with a large set of parameters. To address this shortcoming, machine learning (ML), primarily neural networks, is considered a viable tool to enhance topology optimization and streamline AM processes. In this work, a machine learning (ML) model that generates a parameterized optimized topology is presented, capable of eliminating the conventional iterative steps of TO, which shortens the development cycle and decreases overall development costs. The ML algorithm used, a conditional generative adversarial network (cGAN) known as Pix2Pix-GAN, is adopted to train using a variety of training data pairs consisting of color-coded images and is applied to an example of cantilever optimization, significantly enhancing model accuracy and operational efficiency. The analysis of training data numbers in relation to the model’s accuracy shows that as data volume increases, the accuracy of the model improves. Various ML models are developed and validated in this study; however, some artefacts are still present in the generated designs. Structures that are free from these artefacts achieve 91% reliability successfully. On the other hand, the images generated with artefacts may still serve as suitable design templates with minimal adjustments. Furthermore, this research also assesses compliance with two manufacturing constraints: the limitations on build space and passive elements (voids). Incorporating manufacturing constraints into model design ensures that the generated designs are not only optimized for performance but also feasible for production. By adhering to these constraints, the models can deliver superior performance in future use while maintaining practicality in real-world applications.
This paper details the utilization of the focused ion beam digital image correlation (FIB-DIC) technique for measuring in-plane displacements and the employment of the height digital image correlation (hDIC) technique as a two-step DIC method for determining displacements without an out-of-plane component within the region of interest. Consideration is given to the microscopy data’s measurement scale and resolution to confirm the capability of both techniques to conduct micro-scale correlations with nano-scale sensitivity, making them suitable for investigating the residual elastic strains formed due to processing. The sequential correlation procedure of the FIB-DIC technique has been optimized to balance accuracy and performance for correlating sequential scanning electron microscope (SEM) images. Conversely, the hDIC technique prioritizes the accurate correlation of SEM images directly with the reference state without a sequential procedure, offering optimal computational performance through advanced parallel computing tools, particularly suited for correlating profilometry data related to large-scale displacements. In this study, the algorithm of the hDIC technique is applied as a two-step DIC to evaluate the elastic strain relaxation on the surface of a ring core drilled using a focused ion beam. Both techniques are utilized to correlate the same SEM images collected during the monitoring of the ring drilling process. A comparison of the correlation results of both techniques is undertaken to quantify the near-surface residual elastic strains, with an analysis conducted to discern the accuracy of the hDIC algorithm. Furthermore, the distinctions between the two techniques are delineated and discussed.
Armin Reckert, Valentin Lang, Steven Weingarten
et al.
Multi-Material Jetting (MMJ) is an additive manufacturing process empowering the printing of ceramics and hard metals with the highest precision. Given great advantages, it also poses challenges in ensuring the repeatability of part quality due to an inherent broader choice of built strategies. The addition of advanced quality assurance methods can therefore benefit the repeatability of part quality for widespread adoption. In particular, quality defects caused by improperly configured droplet overlap parameterizations, despite droplets themselves being well parameterized, constitute a major challenge for stable process control. This publication deals with the automated classification of the adequacy of process parameterization on green parts based on in-line surface measurements and their processing with machine learning methods, in particular the training of convolutional neural networks. To generate the training data, a demo part structure with eight layers was printed with different overlap settings, scanned, and labeled by process engineers. In particular, models with two convolutional layers and a pooling size of (6, 6) appeared to yield the best accuracies. Models trained only with images of the first layer and without the infill edge obtained validation accuracies of 90%. Consequently, an arbitrary section of the first layer is sufficient to deliver a prediction about the quality of the subsequently printed layers.
Mesin diesel di gunakan di banyak sektor menuntut kualitas bahan bakar yang bersih untuk mecapai perfomance mesin yang baik. Untuk menjaga kebersihan bahan bakar dilakukan proses penyaringan mengunakan filter element. Mengukur kualitas bahan bakar sebelum dan sesudah penyaringan menjadi informasi penting bagi penguna untuk megambil keputusan untuk mengurangi biaya pemeliharaan dan keseluruhan biaya operasional. Dari permasalahan yang ada peneniti bertujuan untuk mengembangkan alat berfungsi untuk dapat mengidentifikasi kualitas kerbersihan bahan bakar diesel sesuai dengan standar ISO cleanliness 18/16/13 dan performance filter elemen yang digunakan kondisi bekerja atau tidak. Modul ini mengunakan PLC dan HMI untuk tampilan muka dan pressure transmitter untuk modul ukur yang bertujuan untuk menjadi alat yang siap pakai untuk kondisi industri dan pertambangan. modul ini akan membantu penguna sebagai sistem awal yang mengeluarkan informasi kualitas dari bahan bakar diesel yang disaring dan perkiraan performance filter yang di gunakan.
Henri Ponda, Nur Fadilah Fatma, Anggie Fitri Rahmawaty
PT. Arwana Citramulia Tbk is a manufacturing company that produces ceramics. The company faces challenges in managing fluctuating demand, making it difficult to effectively plan production capacity. The objective of this research is to determine the ideal production capacity for the Nobu Pink ceramic type. The analysis of the production capacity for Nobu Pink ceramics was conducted at PT. Arwana Citramulia Tbk Plant I using the Capacity Requirement Planning (CRP) method, with capacity calculations performed using the bill of labor (BOL) method. The research results indicate that the production capacity for 25 x 25 cm Nobu Pink ceramics is 633 pieces per hour or 15,192 pieces per day. A comparison of available capacity with required capacity shows a shortage of capacity at the Kiln workstation in each period. A proposed solution for the company is to add 3 hours of overtime work on Saturdays, which would meet the capacity shortage.
Ignacio Santamaria, Mohammad Soleymani, Eduard Jorswieck
et al.
This paper addresses the problem of maximizing the capacity of a multiple-input multiple-output (MIMO) link assisted by a beyond-diagonal reconfigurable intelligent surface (BD-RIS). We maximize the capacity by alternately optimizing the transmit covariance matrix, and the BD-RIS scattering matrix, which, according to network theory, should be unitary and symmetric. These constraints make the optimization of BD-RIS more challenging than that of diagonal RIS. To find a stationary point of the capacity we maximize a sequence of quadratic problems in the manifold of unitary matrices. This leads to an efficient algorithm that always improves the capacity obtained by a diagonal RIS. Through simulation examples, we study the capacity improvement provided by a passive BD-RIS architecture over the conventional RIS model in which the phase shift matrix is diagonal.
Adding a capacity constraint to a hidden-action principal-agent problem results in the same set of Pareto optimal contracts as the unconstrained problem where output is scaled down by a constant factor. This scaling factor is increasing in the agent's capacity to exert effort.
Channels with synchronization errors, exhibiting deletion and insertion errors, find practical applications in DNA storage, data reconstruction, and various other domains. Presence of insertions and deletions render the channel with memory, complicating capacity analysis. For instance, despite the formulation of an independent and identically distributed (i.i.d.) deletion channel more than fifty years ago, and proof that the channel is information stable, hence its Shannon capacity exists, calculation of the capacity remained elusive. However, a relatively recent result establishes the capacity of the deletion channel in the asymptotic regime of small deletion probabilities by computing the dominant terms of the capacity expansion. This paper extends that result to binary insertion channels, determining the dominant terms of the channel capacity for small insertion probabilities and establishing capacity in this asymptotic regime. Specifically, we consider two i.i.d. insertion channel models: insertion channel with possible random bit insertions after every transmitted bit and the Gallager insertion model, for which a bit is replaced by two random bits with a certain probability. To prove our results, we build on methods used for the deletion channel, employing Bernoulli(1/2) inputs for achievability and coupling this with a converse using stationary and ergodic processes as inputs, and show that the channel capacity differs only in the higher order terms from the achievable rates with i.i.d. inputs. The results, for instance, show that the capacity of the random insertion channel is higher than that of the Gallager insertion channel, and quantifies the difference in the asymptotic regime.
Viacheslav E. Bazhenov, Andrey V. Sannikov, Elena P. Kovyshkina
et al.
In the investment casting process, the pattern made of wax is obtained in a die for further formation of a shell mold. The problem of die-filling by pattern wax is significant because it influences the quality of the final casting. This work investigates three commercial pattern waxes’ fluidity with a newly developed injection fluidity test. It was shown that the fluidity of waxes increased with increasing injection temperature and pressure, and the simultaneous increase in temperature and pressure gives a much more significant enhancement of fluidity than an increase in temperature or pressure separately. The rheological behavior of the waxes was also investigated at different temperatures using a rotational viscosimeter, and temperature dependences of waxes’ dynamic viscosity were determined. It was shown that wax viscosity is increased more than ten times with decreasing temperature from 90 to 60 °C. A good correlation between wax fluidity and its viscosity is observed, which is different from metallic alloys, where the solidification behavior is more critical. The difference in wax flow behavior in comparison with metallic melts is associated with the difference in dynamic viscosity, which for investigated waxes and metallic melts is 3000–27,000 mPa·s and 0.5–6.5 mPa·s, respectively. The difference in investigated filled waxes’ fluidity is observed, which can be associated with the type and amount of filler. The twice-increasing fraction of cross-linked polystyrene decreases fluidity twice. At the same time, terephthalic acid has a minor influence on wax fluidity.
Carsten Schmidt, Adrian Morlock, Rainer Griesbaum
et al.
Users of material extrusion printers are faced with a wide range of prices. It is unknown which printer price can achieve the required part quality. However, the price and the resulting quality of a printer are decisive factors for the process, especially at small- and medium-sized companies. This study investigated the correlation between the printer price and part quality based on dimensional accuracy, surface quality, strength, and visual appearance. In this paper, 14 printers with different prices were examined. The relationship of printer price and part defects, elongation at break, and the accuracy of roundings could be identified (the regressions achieved a <i>p</i>-value under 0.5 and an R<sup>2</sup> over 0.4). A relationship with surface roughness, tensile strength, or other dimensional accuracy characteristics could not be found (the regressions achieved an R<sup>2</sup> under 0.4 or anomalies could be detected in the regression analysis). In the performed investigations, more-expensive printers were not necessarily associated with an improvement in these quality characteristics. No relationship between the printer price and the standard deviation, e.g., less variation in part quality, could be identified. This paper provides valuable insights into the relationship of part quality and printer price. The performed research improved upon the existing literature in terms of the number of investigated printers, the observed price range, and the number of tested quality characteristics. The results and approach of this paper will help users select an appropriate printer, and the findings can be used in the sourcing and technology selection phases.
We use a formal correspondence between thermodynamics and inference, where the number of samples can be thought of as the inverse temperature, to study a quantity called ``learning capacity'' which is a measure of the effective dimensionality of a model. We show that the learning capacity is a useful notion of the complexity because (a) it correlates well with the test loss and it is a tiny fraction of the number of parameters for many deep networks trained on typical datasets, (b) it depends upon the number of samples used for training, (c) it is numerically consistent with notions of capacity obtained from PAC-Bayes generalization bounds, and (d) the test loss as a function of the learning capacity does not exhibit double descent. We show that the learning capacity saturates at very small and very large sample sizes; the threshold that characterizes the transition between these two regimes provides guidelines as to when one should procure more data and when one should search for a different architecture to improve performance. We show how the learning capacity can be used to provide a quantitative notion of capacity even for non-parametric models such as random forests and nearest neighbor classifiers.
In order to improve efficiency when manufacturing gas turbine components, alternative machining techniques need to be explored. In this work, abrasive water jet (AWJ) machining by milling has been investigated as an alternative to traditional milling. Various test campaigns have been conducted to show different aspects of using AWJ milling for the machining of superalloys, such as alloy 718. The test campaigns span from studies of individual AWJ-milled tracks, multi-pass tracks, and the machining of larger components and features with complex geometry. In regard to material removal rates, these studies show that AWJ milling is able to compete with traditional semi/finish milling but may not reach as high an MRR as rough milling when machining in alloy 718. However, AWJ milling requires post-processing which decreases the total MRR. It has been shown that a strong advantage with AWJ milling is to manufacture difficult geometries such as narrow radii, holes, or sharp transitions with kept material removal rates and low impact on the surface integrity of the cut surface. Additionally, abrasive water jet machining (AWJM) offers a range of machining possibilities as it can alter between cutting through and milling. The surface integrity of the AWJM surface is also advantageous as it introduces compressive residual stress but may require post-processing to meet similar surface roughness levels as traditional milling and to remove unwanted AWJM particles from the machined surface.
This study aims to formulate the mission, objectives and strategy of operation or manufacturing for an automotive component factory that will produce a new product. The scope of this research is a case study at PT Inkoasku. Structured interviews were conducted with managers at PT Inkoasku related to customers and suppliers (external analysis) and quality, process, inventory, capacity, and supply chain (internal analysis). The questions are aimed at knowing the current external and internal operating conditions when only producing wheels and further needs when starting to produce disc’ as a new product. In addition to interviews, observations were also conducted on the operating system at PT Inkoasku. The results of external analysis show that the winner of orders from PT Inkoasku's operating system is price, while quality and delivery are order qualifications. Therefore, the operating strategies that can be applied include: choosing the type of batch process in running production, adding lean-six sigma in the quality system, automating the machine that will be used to produce disc’, optimizing the Material Requirement Planning (MRP) system and the min-max policy. in inventory management as well as mapping the procurement of goods and services and implementing Vendor-Managed Inventory (VMI) as a supply chain strategy.
Keywords: operation management, operation strategy, manufacturing strategy, automotive component industry, QPICS