Thibaut Lepage, M. Kammoun, Quentin Schmetz
et al.
Abstract Hydrogen is viewed as a sustainable strategic alternative to fossil fuels, especially in the field of road and air transport. Currently, hydrogen production is derived from fossil fuels or is manufactured by splitting water. A novel option, H2-generation from lignocellulosic biomass, based on renewable resources is currently in a pilot-scale demonstration or at a commercial stage. The present study reviews the thermochemical, biological, and electrochemical approaches used for biomass-to-hydrogen. The advantages, limitations, and major improvements of each process are presented. A techno-economic assessment is also established based on the production cost, technology readiness level, and industrial scalability. The objective is to allow industrial producers to visualise the degree of technological maturity of each option, clarify the necessary development efforts before reaching the commercial stage, determine the most relevant and competitive routes, and assess the suitability of biomass as a feedstock for renewable hydrogen production. In the reviewed results, the thermochemical process, particularly gasification, partial oxidation, and steam reforming, presented the best yield for H2 production. Steam gasification is the best compromise because it is suitable for wet and dry biomass, and it does not require an oxidising agent. As for biological conversion, dark fermentation is more worthwhile than photo-fermentation due to its lower energy consumption. Additionally, the electrochemical process is feasible for biomass. The findings of this study indicate that biomass-hydrogen-based processes are promising options that contribute to the H2 production capacity but require improvements to produce larger competitive volumes.
We compute the capacity of entanglement in two dimensional dilaton gravity in a setting where Hawking radiation, backreaction, and islands can be treated analytically. Our focus is the eternal black hole of the Russo Susskind Thorlacius model coupled to N conformal matter fields. Unlike previous gravitational computations, which were mostly carried out in JT gravity, the RST model forces one to deal with a genuinely dynamical conformal factor and with the global constraints of the replica construction. The main technical step is therefore to solve the replica deformation on the orbifold globally at first order near n=1, including the homogeneous sector fixed by single valuedness and by the requirement of a fixed microcanonical state. For a single interval we obtain a time independent generalized capacity, parallel to the generalized entropy. For two intervals, even in the late time factorization regime, the global solution generates an interaction term between replica fixed points; after Lorentzian continuation this produces a time dependent capacity on the two QES saddle, despite the corresponding entropy plateau. We discuss the regime of validity of the resulting expressions and explain how the large size of the two QES capacity implies a highly non uniform saddle competition near n=1, providing a concrete mechanism for sharp features of the capacity at the Page transition.
Dian Afif Arifah, Ratih Andhika Akbar Rahma, Triana Harmini
et al.
Introduction: As the center-cultured region in Indonesia, Ponorogo Regency is dominated by traditional manufacturing industries which support regional economic growth. Most production in this sector is labor-intensive and depends on manual handling processes, which may increase the risk of work-related musculoskeletal disorders (WMSDs). This study aims to develop a model to evaluate and predict ergonomic hazards using a neural network algorithm, focusing on the relationship between manual handling postures and musculoskeletal pain in 12 body regions. Method: A cross-sectional study involved data of 250 workers measured using used Nordic Musculoskeletal questionnaire and manual handling exposure checklist based on SNI 9011:2021. A neural network model was developed based on GLM’s output to explore the complex interrelationships between manual handling postures (X variables) and musculoskeletal pain across 12 body regions (Y variables). Result: The outputs identified carrying object over 9 meters (X10), one-handed lifting (X3), and trunk twisting (X2), with X10 confirmed as the most predictor for multiple outcomes, affecting six regions. Neural network models demonstrated adequate learning capacity with stable architecture, proved by average CEE values ranging from 0.21 to 0.54. The models showed improved predictive accuracy across epochs. Conclusion: The finding shows that NN modelling may be expanded to include broader industries in Indonesia's traditional manufacturing sector as an integrated data-based information system application. However, further validation using external datasets is recommended to enhance generalizability.
ABSTRACT In recent years, significant advancements in digital information management and new capabilities within Industry 4.0/5.0 systems have transformed production systems, enabling mass customisation as a new realistic paradigm. Additive manufacturing (AM), or 3D printing, represents a revolutionary approach by allowing the creation of highly personalised products without significantly increasing costs or production time. Efficient utilisation of AM resources requires effective production planning and management, particularly in scheduling production orders, which involves complex nesting logic due to the nonidentical nature of the pieces produced. This work aims to generate actionable knowledge for practitioners, enhancing their ability to understand and effectively tackle these challenges. To achieve this, various deterministic heuristics are proposed to solve the nesting/batching process, and their impact on the quality of final scheduling and computational time is analysed. Real datasets are used to evaluate these strategies, solving larger‐sized problems than those previously addressed, to assess resolution capacity. This approach allows for practical rules (easily assimilable by practitioners) to be derived, which ultimately enhance the efficiency of AM systems. The results demonstrate that generating heterogeneous builds—distinct in average heights or volumes—not only improves makespan values by approximately 2%, but also, significantly accelerates the scheduling optimisation process. For the largest instances, computational time is reduced from over 1100 s to just 22 s, representing a remarkable 184% reduction. The underlying intuition for this drastic CPU time reduction is that heterogeneous builds benefit MILP solvers by tightening relaxed solutions; that is, fractional values for binary variables tend to align more closely with the final optimal values.
Masoud M. Pour, Lars O. Schmidt, Blair E. Carlson
et al.
The transition to high-energy-density lithium metal batteries (LMBs) is essential for advancing electric vehicle (EV) technologies beyond the limitations of conventional lithium-ion batteries. A key challenge in scaling LMB production is the precise, contamination-free separation of lithium metal (LiM) anodes, hindered by lithium’s strong adhesion to mechanical cutting tools. This study investigates high-speed, contactless laser cutting as a scalable alternative for shaping double-coated LiM anodes. The effects of pulse duration, pulse energy, repetition frequency, and scanning speed were systematically evaluated using a nanosecond pulsed laser system on 30 µm LiM foils laminated on both sides of an 8 µm copper current collector. A maximum single-pass cutting speed of 3.0 m/s was achieved at a line energy of 0.06667 J/mm, with successful kerf formation requiring both a minimum pulse energy (>0.4 mJ) and peak power (>2.4 kW). Cut edge analysis showed that shorter pulse durations (72 ns) significantly reduced kerf width, the heat-affected zone (HAZ), and bulge height, indicating a shift to vapor-dominated ablation, though with increased spatter due to recoil pressure. Optimal edge quality was achieved with moderate pulse durations (261–508 ns), balancing energy delivery and thermal control. These findings define critical laser parameter thresholds and process windows for the high-speed, high-fidelity cutting of double-coated LiM battery anodes, supporting the industrial adoption of nanosecond laser systems in scalable LMB electrode manufacturing.
Saleh Seyedzadeh, Vyron Christodoulou, Adam Turner
et al.
This paper proposes a non-invasive, data-driven methodology for monitoring and optimising machine utilisation in manufacturing environments. By analysing high-resolution power consumption data, the system automatically classifies machine states (off, idling, and working, and segments operational periods into discrete production events. Unsupervised learning techniques enable the identification of production patterns, product typologies, and anomalies, supporting improvements in operational efficiency and quality control. The approach also estimates energy consumption and cost using time-of-use tariffs, offering insights into both performance and sustainability. Experimental evaluations across multiple industrial settings demonstrate the method’s robustness, with high agreement with production records and significant potential for reducing idle time, improving scheduling, and enhancing resource allocation. This work presents a scalable and interpretable analytics framework to support data-driven decision-making in modern manufacturing operations.
Fotios Panagiotis Basamakis, Dimosthenis Dimosthenopoulos, Angelos Christos Bavelos
et al.
The recent technological advancements in today’s manufacturing industry have extended the quality control operations for welding processes. However, the realm of pre-welding inspection, which significantly influences the quality of the final products, remains relatively uncharted. To this end, this study proposes an innovative vision system designed to extract the seam gap width and centre between two components before welding and make informed decisions regarding the initiation of the welding process. The system incorporates a deep learning semantic segmentation network for identifying and isolating the desired gap area within an acquired image from the vision sensor. Then, additional processing is performed, with conventional computer vision techniques and fundamental Euclidean geometry operations for acquiring the desired width and the centre of that area with a precision of 0.1 mm. Additionally, a control graphical interface has been implemented that allows the operator to initiate and monitor the entire inspection procedure. The overall framework is applied and tested on a manufacturing case study involving the laser welding operations of sheet metal parts, and although it is designed to handle gaps of different shapes and sizes, it is mainly focused on obtaining the characteristics of butt weld gaps.
Stanislav V. Chernyshikhin, Dmitry D. Zherebtsov, Leonid V. Fedorenko
et al.
Over the last decade, laser powder bed fusion (LPBF) received increased attention as a method of producing complex-shaped products from various materials. Recent results indicate the potential of this technology for the production of intermetallic NiTi alloys with shape memory. Several studies have demonstrated a strong influence of the LPBF process conditions on the resulting material properties, i.e., the martensitic phase transformation temperatures, reversible/irreversible strain after cyclic loading, phase composition, chemical composition, etc. However, the mechanisms of functional properties altering during LPBF consolidation remain unexplored in the present state-of-the-art. This study aims to advance the knowledge about tailoring material properties of NiTi under laser influence. In this work, thin-walled samples were manufactured from pre-alloyed NiTi powder via LPBF in a wide window of laser power and scanning speed, excluding hatch spacing by employing a single track-based scanning strategy to reveal the pure effect of the laser’s influence. NiTi samples were characterized by various methods such as differential scanning calorimetry, X-ray diffraction, and mechanical tests. Established relationships between NiTi properties and the LPBF process conditions provide the basis for the development of NiTi production protocols with controlled functional properties.
Paulo Afonso, Dyeniffer Packer Eigenstuhler, Antonio Zanin
et al.
The aim of this study was to analyze the impact of conditioning factors in the adoption and use of interorganizational cost management practices in manufacturing companies. The data was collected through a questionnaire applied to the controllers of the manufacturing companies listed in B3. The sample consisted of 33 companies. The findings contribute to improving the understanding of how conditioning factors (Products, Components, Relationship Levels, Chain Types and Mechanisms) influence interorganizational cost management practices. The results indicate that conditioning factors, Relationship Levels and Mechanisms can influence the use of inter-organizational cost management practices. According to the results, it can be inferred that throughout the organization's trajectory it is necessary to establish partnerships with suppliers. In general, it is highlighted that sharing information between companies in the supply chain provides greater trust, favoring everyone involved in the supply chain.
Production management. Operations management, Production capacity. Manufacturing capacity
M Yazid Diratama, Tri Prakosa, Gamawan Ananto
et al.
Ragum-125 Polman adalah salah satu produk hasil produksi Polman Bandung yang berfungsi sebagai alat pencekam. Ragum-125 Polman terdiri dari beberapa komponen penyusun dimana rahang tetap dan gerak menjadi komponen inti dan utama. Proses pembuatan Ragum-125 Polman, khususnya proses pemesinan rahang tetap dan gerak, masih menggunakan banyak mesin-mesin konvensional sehingga, dibutuhkan banyak alat bantu jig (penepat) dan fixture (penetap), sehingga waktu produksinya lama. Proses pengejaannya membutuhkan empat belas (14) buah set-up dan banyak tahapan proses dari mesin ke mesin yang berbeda, sehingga rentan terjadi kesalahan proses. Mesin perkakas CNC memiliki kemampuan untuk mengerjakan banyak operasi pemesinan dalam satu kali set-up serta dapat meminimalisir kesalahan proses karena gerakannya dikendalikan secara numerik. Meskipun demikian, untuk memanfaatkan kelebihan mesin perkakas CNC, dibutuhkan perencanaan set-up (set-up planning) yang baik dan benar agar proses bisa berjalan sesuai spesifikasi yang ditetapkan. Penelitian ini membahas perencanaan set-up dan pemilihan mesin perkakas untuk proses pemesinan fitur rahang tetap dan gerak ragum 125 Polman Bandung yang terdiri dari 4 tahap yaitu pengelompokan fitur berdasarkan tool approach direction (TAD), pemilihan datum set-up, formasi set-up dan penentuan mesin perkakas, serta pengurutan operasi pemesinan dan pengurutan set-up. Dalam setiap tahapan terdapat Aturan yang berfungsi sebagai petunjuk untuk proses perencanaan set-up. Dalam penelitian ini dihasilkan 19 Aturan dan menghasilkan 2 set-up untuk rahang tetap dan 1 set-up untuk rahang gerak menggunakan mesin CNC horizontal Mitsubishi H4Bn yang mana berhasil menurunkan jumlah set-up dari 14 set-up menjadi 3 set-up dalam proses pemesinan rahang tetap dan gerak ragum 125 Polman Bandung.
Antonio Formisano, Luca Boccarusso, Dario De Fazio
et al.
Incremental sheet forming represents a relatively recent technology, similar to the layered manufacturing principle of the rapid prototype approach; it is very suitable for small series production and guarantees cost-effectiveness because it does not require dedicated equipment. Research has initially shown that this process is effective in metal materials capable of withstanding plastic deformation but, in recent years, the interest in this technique has been increasing for the manufacture of complex polymer sheet components as an alternative to the conventional technologies, based on heating–shaping–cooling manufacturing routes. Conversely, incrementally formed polymer sheets can suffer from some peculiar defects, like, for example, twisting. To reduce the risk of this phenomenon, the occurrence of failures and poor surface quality, a viable way is to choose toolpath strategies that make the tool/sheet contact conditions less severe; this represents one of the main goals of the present research. Polycarbonate sheets were worked using incremental forming; in detail, cone frusta with a fixed-wall angle were manufactured with different toolpaths based on a reference and a stair strategy, in lubricated and dry conditions. The forming forces, the forming time, the twist angle, and the mean roughness were monitored. The analysis of the results highlighted that a stair toolpath involving an alternation of diagonal up and vertical down steps represents a useful strategy to mitigate the occurrence of the twisting phenomenon in incremental formed thermoplastic sheets and a viable way of improving the process towards a green manufacturing process.
The two-user broadcast channel (BC) with receivers connected by bidirectional cooperation links of finite capacities, known as conferencing decoders, is considered. A novel capacity region outer bound is established based on multiple applications of the Csiszár-Körner identity. Achievable rate regions are derived by using Marton's coding as the transmission scheme, together with different combinations of decode-and-forward and quantize-bin-and-forward strategies at the receivers. It is shown that the outer bound coincides with the achievable rate region for a new class of semi-deterministic BCs with degraded message sets; for this class of channels, one-round cooperation is sufficient to achieve the capacity. Capacity result is also derived for a class of more capable semi-deterministic BCs with both common and private messages and one-sided conferencing. For the Gaussian BC with conferencing decoders, if the noises at the decoders are perfectly correlated (i.e., correlation is either 1 or -1), the new outer bound yields exact capacity region for two cases: i) BC with degraded message sets; ii) BC with one-sided conferencing from the weaker receiver to the stronger receiver. When the noises have arbitrary correlation, the outer bound is shown to be within half a bit from the capacity region for these same two cases. Finally, for the general Gaussian BC, a one-sided cooperation scheme based on decode-and-forward from the stronger receiver to the weaker receiver is shown to achieve the capacity region to within $\frac{1}{2}\log (\frac{2}{1-|λ|})$ bits, where $λ$ is the noise correlation. An interesting implication of these results is that for a Gaussian BC with perfectly negatively correlated noises and conferencing decoders with finite cooperation link capacities, it is possible to achieve a strictly positive rate using only an infinitesimal amount of transmit power.
We are interested in the branching capacity of the range of a random walk in $\mathbb Z^d$.Schapira [28] has recently obtained precise asymptotics in the case $d\ge 6$ and has demonstrated a transition at dimension $d=6$. We study the case $d=5$ and prove that the renormalized branching capacity converges in law to the Brownian snake capacity of the range of a Brownian motion. The main step in the proof relies on studying the intersection probability between the range of a critical Branching random walk and that of a random walk, which is of independent interest.
Alexandra Carvalho, Vivek Nair, Sergio G. Echeverrigaray
et al.
We have investigated the lithium capacity of the 2H phase of niobium sulphide (NbS2) using density functional theory calculations and experiments. Theoretically, this material is found to allow the intercalation of a double layer of Li in between each NbS2 layer when in equilibrium with metal Li. The resulting specific capacity (340.8 mAh/g for the pristine material, 681.6 mAh/g for oxidized material) can reach more than double the specific capacity of graphite anodes. The presence of various defects leads to an even higher capacity with a partially reversible conversion of the material, indicating that the performance of the anodes is robust with respect to the presence of defects. Experiments in battery prototypes with NbS2-based anodes find a first specific capacity of about 1,130 mAh/g, exceeding the theoretical predictions.
Measuring capacity in the grading process is an important step in battery production. The traditional capacity acquisition method requires considerable time and energy consumption; therefore, an accurate capacity estimation is crucial in reducing production costs. Herein, a capacity prediction method for lithium‐ion batteries based on improved random forest (RF) is proposed. This method extracts features from the voltage data of the entire formation process and the first 25% of the grading process, saving 56.7% of the energy consumption and 74.6% of the time in the grading process. The importance of these features is ranked by RF, the best feature subset is selected, and the RF algorithm with parameters optimized by the genetic algorithm is applied to establish a capacity prediction model. The results show that the proposed model can accurately predict the capacity, with root mean square error (RMSE) and mean absolute percentage error (MAPE) values of only 191.89 mAh and 0.13%, respectively. Compared with other regression algorithms, the model shows a lower RMSE and MAPE and a higher capacity prediction accuracy for low‐capacity cells.
Srividhya Venkataraman, Imran Khan, Peyman Habibi
et al.
Plants have been explored as a platform to produce pharmaceutical proteins for over 20 years. Important features such as the cost-effectiveness of production, the ease of scaling up to manufacturing capacity, the lack of cold chain requirements and the ability to produce complex therapeutic proteins which are biologically and functionally identical to their mammalian counterparts, make plants a strong alternative for vaccine production. This review article focuses on both the expression as well as the downstream purification processes for plant made vaccines. Expression strategies including transgenic, transient and cell suspension cultures are outlined, and various plant tissues targeted such as leaves and seeds are described. The principal components used for downstream processing of plant made vaccines are examined. The review concludes with a reflection of the future benefits of plant production platforms for vaccine production.
Metal additive manufacturing is gaining broad interest and increased use in the industrial and academic fields. However, the quantification and commercialization of standard parts usually require extensive experiments and expensive post-characterization, which impedes the rapid development and adaptation of metal AM technologies. In this work, a similarity-based acceleration (S-acceleration) method for design of experiments is developed to reduce the time and costs associated with unveiling process-property (porosity defects) relationships during manufacturing. With S-acceleration, part semantic features from machine-setting parameters and physics-effects informed characteristics are explored for measuring mutual part similarities. A user-defined simplification rate of experiments is proposed to purposely remove redundant parts before conducting experiments printing without sacrificing information gain as original full factorial experiment design. This S-acceleration design of experiments is demonstrated on a Concept Laser M2 machine for the experimental plan of modeling relationships between process parameters and part porosity defects. The printed part has 2 mm diameter by 4 mm tall pin geometry considering variations in build location and orientation, laser settings and powder feedstock are held constant. In total, 242 parts are measured to create a ground truth data set of porosity levels by using X-ray tomography microscopy. The S-acceleration method is assessed for performance considering 40%, 50%, and 60% of user-defined experiment simplification rates. The repeated experiments are removed without ignoring the minority experiments outlier, assuring a similar process-property relation in the original experiment plan. The experiment number is significantly reduced based on part similarity with minimal compromise of model accuracy and obtained knowledge.
Collaborative robots are increasingly present in industry to support human activities. However, to make the human-robot collaborative process more effective, there are several challenges to be addressed. Collaborative robotic systems need to be aware of the human activities to (1) anticipate collaborative/assistive actions, (2) learn by demonstration, and (3) activate safety procedures in shared workspace. This study proposes an action classification system to recognize primitive assembly tasks from human motion events data captured by a Dynamic and Active-pixel Vision Sensor (DAVIS). Several filters are compared and combined to remove event data noise. Task patterns are classified from a continuous stream of event data using advanced deep learning and recurrent networks to classify spatial and temporal features. Experiments were conducted on a novel dataset, the dataset of manufacturing tasks (DMT22), featuring 5 classes of representative manufacturing primitives (PickUp, Place, Screw, Hold, Idle) from 5 participants. Results show that the proposed filters remove about 65\% of all events (noise) per recording, conducting to a classification accuracy up to 99,37\% for subjects that trained the system and 97.08\% for new subjects. Data from a left-handed subject were successfully classified using only right-handed training data. These results are object independent.