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Over the last decades, scheduling theory has been put into practice so as to tackle production process as a combinatorial optimisation problem and specifically as a PFSP (Permutation Flowshop Scheduling Problem). Often, NP-completeness of PFSP leads numerous research towards suggesting heuristic algorithms. The objective is to propose an optimal or near-to-optimal order of n jobs processing on m machines with a minimum completion time of all jobs. In this paper, a two-phase heuristic algorithm will be presented, named IFRS (Improved FRS). FRS, within the first phase, is going to be used to find a superior order of jobs out of Taillard's instances; then the order of jobs will be improved again in phase 2. While running IFRS, a completely new idea, named Pixelation of time matrices, will be used for the very first time and provide a final pattern, which can be used in any heuristic algorithm with makespan criterion. After using the hard benchmark instances of Taillard, the superiority of IFRS over 20 algorithms is going to be shown by tables and statistical graphics. Due to its low makespan values, IFRS benefits flow production, as does NEH. [ABSTRACT FROM AUTHOR]

This document discusses the importance of carbon reduction engineering in combating global climate change. It highlights the role of production and supply chain management in achieving carbon neutrality and reducing carbon emissions. The document presents 20 selected research papers that cover various aspects of production optimization in carbon reduction engineering, including carbon reduction in the production process, low-carbon and sustainable supply chains, assessment and optimization methodologies, and policy instruments. The papers provide insights into topics such as scheduling optimization, renewable energy production, carbon emission efficiency, sustainable consumption, government subsidizing arrangements, and the impact of policy instruments on carbon reduction. The document expresses gratitude to the authors and reviewers for their contributions and acknowledges the support of the International Journal of Production Research. [Extracted from the article]

This research examines the transformative potential of artificial intelligence (AI) in general and Generative AI (GAI) in particular in supply chain and operations management (SCOM). Through the lens of the resource-based view and based on key AI capabilities such as learning, perception, prediction, interaction, adaptation, and reasoning, we explore how AI and GAI can impact 13 distinct SCOM decision-making areas. These areas include but are not limited to demand forecasting, inventory management, supply chain design, and risk management. With its outcomes, this study provides a comprehensive understanding of AI and GAI's functionality and applications in the SCOM context, offering a practical framework for both practitioners and researchers. The proposed framework systematically identifies where and how AI and GAI can be applied in SCOM, focussing on decision-making enhancement, process optimisation, investment prioritisation, and skills development. Managers can use it as a guidance to evaluate their operational processes and identify areas where AI and GAI can deliver improved efficiency, accuracy, resilience, and overall effectiveness. The research underscores that AI and GAI, with their multifaceted capabilities and applications, open a revolutionary potential and substantial implications for future SCOM practices, innovations, and research. [ABSTRACT FROM AUTHOR]

This paper presents a novel solution approach for a variant of the job shop scheduling problem with machine unavailability due to both condition-based preventive maintenance and corrective maintenance following random breakdowns. We first provide an exact mathematical formulation of the problem under simplifying assumptions, namely that the number of breakdowns for each job position on each machine is known, the degradation rates are fixed, and the preventive and corrective maintenance durations are deterministic parameters. Moreover, to handle the more realistic case of stochastic machine degradation, random breakdowns, and uncertain maintenance durations, a simulation-optimisation algorithm is proposed. The real makespan function is first approximated using multiple surrogate measures, which are optimised through independent genetic algorithms. Then, the fittest solutions obtained from these surrogate measures are simulated, and the best among them is added to an elite list, which is included in the genetic algorithms' populations for the next iteration. Schedule robustness is ensured by using an objective function that consists of the weighted average of the expected makespan and its 90th percentile. Furthermore, to reduce the likelihood of falling into a local optimum, a stopping criterion based on simulated annealing is implemented. Numerical experimentation on extended benchmark instances confirmed the validity of the mathematical formulation and the favourable performance of the proposed simulation-optimisation algorithm in terms of computational time and solution quality. [ABSTRACT FROM AUTHOR]

Combined heat and power economic dispatch (CHPED) is one of the foremost subjects in the operation of power systems. In this article, a new variant of cuckoo search (CS) algorithm, elitist CS (yECS), is advanced to tackle CHPED. During the optimisation process of the original CS as well as lots of its variants, the guidance of search directions relies merely upon the best individual, causing the loss of other beneficial information and further influencing their performance potentials. Therefore, in yECS, an elitist mechanism is developed to fully utilise the beneficial information of other elite individuals. Specifically, three new iterative strategies are developed, one for the global search phase and the other two for the local search phase. Further, a coordinated mechanism is put forward to effectively integrate the local iterative strategies, thus helping yECS in maintaining an appropriate balance between exploitation and exploration. The superior performance of yECS is firstly substantiated via CEC 2017 test suite and two engineering design problems and then it is utilised to address CHPED problems. All optimal dispatch results acquired by yECS are feasible and in most cases display remarkable improvements over the results determined by other CS variants and some recently-published literature results. [ABSTRACT FROM AUTHOR]

Laser directed energy deposition (L-DED) typically uses a 1 – 3 mm diameter laser spot size to melt feedstock material. Smaller spot sizes can benefit L-DED in certain applications (i.e., repair), by creating smaller beads. This study examined how a 0.3-mm-diameter laser spot affects the bead formation, microstructure, and mechanical properties (as-built) of 18Ni-300 maraging steel. The main printing parameters (i.e., laser power, scan speed, powder flow rate) were varied in different combinations to optimize for single-track bead geometry, before printing cubes. Parameters for optimal bead aspect ratio (AR) and dilution (D) had an average energy and mass density of 74.5 J/mm2 and 0.018 g/mm2. Compared to larger laser spot diameters, the use of a 0.3-mm laser size influenced bead characteristics (i.e., AR, D, dominant parameters). Additionally, cube fabrication resulted in cubes with ≥ 99% density but had external geometric defects. Titanium oxides were identified through scanning electron microscopy (SEM) and energy dispersive X-ray spectroscopy (EDS) and were attributed to the smaller interaction volume between the laser beam and powder streams with a small laser spot size that plays a significant role within the processing window defined. Additionally, analysis of tensile fracture surfaces showed the oxide particles significantly impacted tensile properties, resulting in premature failure compared to expected values. Electron backscatter diffraction (EBSD) and thermal simulations demonstrated that locations with the highest cooling rate had the highest amount of retained austenite (RA) and finest microstructure. [ABSTRACT FROM AUTHOR]

Electroplating enhances the mechanical, thermal, and tribological properties of components in industries like aerospace, computing, pharmaceuticals, and telecommunications. To achieve high-quality electroplated coatings, control of process parameters is essential. This study focuses on the challenges associated with the black nickel electroplating process, particularly its nonlinearity which makes traditional linear methods inadequate. We employed machine learning techniques to develop models capable of predicting defects, coating colour, and coating mass specifically for black nickel electroplating process adhering to the MIL-P-18317 specification, a boric acid-free method that is more environmentally friendly but more sensitive to bath conditions, hence making process optimization more challenging. Our research addresses significant gaps in the literature, focusing on boric acid-free black nickel plating, which requires unique modelling approaches different from other electroplating processes. Unlike previous studies that mainly focused on predicting coating mass, our models also predict defects and colour, ensuring comprehensive quality control. The best-performing models achieved an F1 score of 0.9875 for defect prediction, an F1 score of 0.95 for colour prediction, and R 2 = 0.9561, MAE = 0.0039, and MSE = 0.00001 for mass prediction. Additionally, we developed a novel optimization algorithm based on these models to fine-tune process parameters, ensuring that the resulting coatings meet strict standards for quality, appearance, and productivity. The validity of our approach was confirmed through experimental results. This research demonstrates how machine learning can help surface finishers, by providing effective strategies for optimizing processes in nonlinear scenarios, thereby improving product quality and productivity. [ABSTRACT FROM AUTHOR]

The article focuses on the review of HPGR operation mainly from a technological point of view. A critical review of recent achievements in HPGR technology was carried out, and major research areas and interests were distinguished and characterized. The economic potentials of HPGR presses were analyzed from the scope of the energy consumption. The HPGR researches were grouped into the following subjects: construction and design of HPGR machines, HPGR feed characterizations, the process course and assessment of comminution operations in HPGR devices, modeling and optimization of HPGR operation, HPGR-based technological circuits, and impact of HPGR operation on effectiveness of beneficiation processes. Apart from that, quantitative and qualitative analyses of the publications registered in the Web of Science database were performed. The evaluation has been performed according to the most frequently published institutions, countries, and journals. The analysis of merit content was performed through a critical review of research areas and investigative topics characterized by keywords. [ABSTRACT FROM AUTHOR]

Laser powder bed fusion (LPBF) combined with liquid silicon infiltration (LSI) holds promise for fabricating intricate SiSiC components. However, its suitability for producing SiC components without free silicon, crucial for acidic or alkaline resistance, remains uncertain. In this study, SiC components were fabricated through successive processes of LPBF, polycarbosilane impregnation (PI), carbonization, LSI, and acid etching using granulated and nongranulated SiC powders. Their properties were compared systematically. The results showed that PI treatment significantly improved the performance of samples made from non-granulated SiC; the bulk density and compressive strength after pyrolysis increased by 33 % and 325 %, respectively, and the bending strength after LSI increased by 26.2 %. These samples also exhibited a higher strength retention after acid etching. In contrast, PI treatment did not benefit the granulated SiC samples. Further analysis suggested that these differences stem from the distinct particle shapes and surface microstructures of granulated and nongranulated SiC, highlighting the need for process optimisation tailored to different SiC raw materials. [ABSTRACT FROM AUTHOR]

In this study, zirconia-modified alumina support (S) was used to investigate Ga-promoted Ni catalysts for dry reforming of methane (DRM). The catalysts (Ni + (0–3) wt% Ga/S) were prepared using the wet impregnation method and calcined at 700 °C for 3 h. The inclusion of Ga enhanced the surface area, basicity, and metal-support interaction of the Ni-Ga/S catalysts. Smaller Ni particles containing Ga were seen in the TEM. The most active and stable catalyst was Ni + 2.0 Ga/S, having a conversion of 35% CO2 and 28% CH4 at 600 °C and displaying less (17%) carbon deposition. Furthermore, the DRM process was optimized by a mathematical model. The model determined the optimal conditions as follows: temperature (800 °C), gas flow rate (GHSV—30,000 ml h−1gcat−1), and methane to carbon dioxide ratio (1:1). The model predicts CH4 and CO2 conversions of 76.76% and 82.0%, respectively, and an H2/CO ratio of 1.02, compared to experimental results showing CH4 conversion at 74.56%, CO2 conversion at 83.25%, and an H2/CO ratio of 1.01. The model demonstrates excellent agreement with the experimental observations, exhibiting less than 3% error. [ABSTRACT FROM AUTHOR]

Lithium-ion batteries (LIBs) have attracted significant attention due to their considerable capacity for delivering effective energy storage. As LIBs are the predominant energy storage solution across various fields, such as electric vehicles and renewable energy systems, advancements in production technologies directly impact energy efficiency, sustainability, and cost-effectiveness. The fast-growing demand for improved battery performance, such as higher energy densities and reduced costs, necessitates continuous innovation to meet these requirements. Furthermore, LIBs play a pivotal role, making it crucial to track and adopt emerging manufacturing techniques that contribute to cleaner and more efficient energy solutions. This review surveys multiple aspects of LIB manufacturing, beginning with electrode-level advancements in novel materials and process optimization and extending to the cell-level and innovative cell designs. It further investigates automotive battery production, the significance of battery management systems, and the interdisciplinary aspects of battery pack design. The emerging domain of all-solid-state technologies is also scrutinized, focusing on criteria, architectural designs, manufacturing processes, and the innovative application of 3D printing technology. Finally, this paper emphasizes the transformative potential of digitalization in LIB manufacturing, elucidating its advantages, challenges, and future prospects. [ABSTRACT FROM AUTHOR]

Immobilization methods for cellulase entrapped into alginate beads with glutaraldehyde (GA) cross-linking were investigated. Cellulase entrapped into alginate-polyethylene glycol beads with glutaraldehyde cross-linking (SA-PEG-E) showed more superiority both in terms of catalytic performance and reusability than other methods. Several conditions during SA-PEG-E immobilization such as cellulase concentration, GA amount, pH of immobilization, cross-linking time and hardening temperature were evaluated to study their effect on immobilized enzyme activity, immobilization yield and immobilization efficiency. Based on the results, GA amount, pH of immobilization and hardening temperature were selected for process optimization of SA-PEG-E using the Box-Behnken design of response surface methodology. The results showed that the optimum immobilized conditions were obtained with a GA amount of 0.8 mL, pH of immobilization 4.7, and hardening temperature of 47.5 °C when the cellulase concentration was 7.5 mg/mL and cross-linking time was 2 h. Then, the characterization of SA-PEG-E was studied. The optimum pH of SA-PEG-E was observed to be 4, 1 unit lower than that of the free enzyme (pH 5). The optimum temperature was the same for both free and immobilized cellulase at 50 °C. In addition, the SA-PEG-E exhibited broad pH and temperature adaptability. [ABSTRACT FROM AUTHOR]

[Display omitted] • The FW valorization is a potential source of HC due to HTC/L. • HTC/L converted FW into HC with C (>55 %), AC (<2%), and HHV (19.2–32.5 MJ/kg). • HC produced at 320 and 340 °C demonstrated better fuel properties over 220 °C. • The DOE approach offered the optimization of solid and liquid fractions. • Household FW was converted to energy, fatty acid, and P via HTC/L. Hydrothermal carbonization (HTC) increasingly appears as an eco-friendly method for managing food waste (FW). In this work, a combination of FW was subjected to HTC, and products were critically evaluated. This involved a lab-scale pressure reactor and optimization of HTC conditions: temperature (220–340 °C) and residence time (90–260 min) via central composite design type of response surface methodology (CCD-RSM). Results showed varying temperatures and residence time to impact the hydrochar (HC) and hydrothermal carbonization aqueous phase (HTC-AP) properties. Although HC produced through HTC exhibited lower ash content (<2%) despite higher fixed carbon (>55 %) with respect to the raw FW, the heating value of HC ranged from 19.2 to 32.5 MJ/kg. Temperature primarily influenced FW conversion, affecting carbonaceous properties. Saturated fatty acids (SFA) were found to be predominant in the HTC-AP under all tested operating conditions (77.3, 48.4, and 37.1 wt% for HTC at 340, 280, and 220 °C in 180 min, respectively). Total phosphorus recovery in HC and HTC-AP respectively peaked at 340 °C and 220 °C in 180 min. The study concludes that HTC holds promise for energy-dense biofuel production, nutrient recovery, and fostering a circular economy. [ABSTRACT FROM AUTHOR]

This study investigates the performance of serpentine and wavy serpentine millireactors. Millireactors, with their microscale features, offer advantages such as heightened surface-to-volume ratio, improved heat transfer coefficients, and reduced mass transfer pathways. The serpentine and wavy serpentine millireactors, characterized by their channel structures, demonstrate superior energy efficiency, reaction speed, and scalability compared to conventional reactors. The focus of this research is on understanding the mass transfer rates and residence time distribution (RTD) in these millireactors, crucial for optimizing their design and operation. The residence time distribution studies providing insights into the mixing characteristics and overall flow behavior. The (RTD) data was fitted to different models. Mass transfer studies examine the movement of acetic acid between aqueous and organic phases, with the volumetric mass transfer coefficient (KLa) as a key parameter. Results from the serpentine millireactor indicate a partially mixed flow behavior with a dispersion number of 0.611, while the wavy serpentine millireactor exhibits a dispersion number of 0.130, indicating improved mixing efficiency. The mass transfer studies yield KLa values in the range of 0.31–1.42 min−1 and 0.49–2.71 min−1 for serpentine and wavy serpentine millireactors, respectively. Efficiency assessments reveal 72.31% and 83.21% for serpentine and wavy serpentine millireactors, respectively. The study concludes that the use of serpentine and wavy serpentine millireactors represents an effective method for process intensification, offering valuable insights for chemical engineering and process optimization applications. The results contribute to advancing the millireactor technology and its potential in continuous flow chemical manufacturing. [ABSTRACT FROM AUTHOR]

The article discusses the application of computer simulation in the optimization of production processes, particularly in the context of analyzing scenarios related to the addition of new production lines. The conducted research and simulations have shown that computer simulation is a key tool for precise modeling and analysis of various options, allowing for better understanding and optimization of production activities. The article presents the theoretical foundations of simulation along with practical examples of its application, focusing on assessing the impact of different production line configurations on the overall system's efficiency. The analysis of benefits includes shortening the production cycle time, increasing flexibility, and improving operational efficiency. The challenges associated with implementing computer simulation, such as the need for specialized knowledge and the necessity for continuous updates of simulation models, are also discussed. Based on the research and analyses conducted, the article demonstrates that computer simulation is an effective tool supporting strategic and operational decision-making in production management, particularly in the context of expanding production infrastructure. [ABSTRACT FROM AUTHOR]

ABSTRACT The thermoacidophilic type III pullulan hydrolase derived from Thermococcus kodakarensis (TK‐PUL) can function as both α‐amylase and pullulanase. Here, TK‐PUL is used to prepare resistant starch type III (RS3), and reaction conditions were controlled to facilitate preparation and improve preparation efficiency. RS3‐type cornstarch is prepared by TK‐PUL enzymatic hydrolysis synergistic dual autoclaving‐cooling treatment. The optimum conditions for preparing RS3‐type cornstarch were as follows: treatment of 15.55% cornstarch slurry (pH 4.99) with 13.83 U/g (cornstarch) TK‐PUL at 90 °C for 11.55 h, followed by high‐pressure heat treatment (110 °C, 30 min)/retrogradation (4 °C, 24 h) for two cycles. The RS3 yield is 40.70% under optimal processing conditions. RS3 comprised irregular and rough surface contour block‐like structures, as well as block‐like structures created by adhesion and accumulation of granules. X‐ray diffraction analysis revealed that the crystallinity type of RS3 transformed from B+V‐ to V‐type, in contrast to the initial A‐type of cornstarch. This novel RS3 preparation process using TK‐PUL enzymatic hydrolysis synergistic dual autoclaving‐cooling treatment lays the foundation for using TK‐PUL in RS preparation. [ABSTRACT FROM AUTHOR]

Aiming at the chattering problem generated in the machining process of thin-walled parts with complex curved surfaces, a process optimization strategy based on the non-uniform allowance of ɛ influence factor is proposed from the finishing margin with the goal of simplicity and practicality. Firstly, the machining allowance is planned using the ɛ influence factor, and then the theoretical evaluation of the chattering stability of milling operations for uniform allowance blades and non-uniform allowance blades based on the ɛ influence factor by semi-discrete time-domain analysis is carried out and compared, and the results show that the stabilizing region of the optional spindle speed and depth of cut is increased by about 50%, thus effectively improving the productivity. Finally, through the test machining of the whole impeller and the surface contour inspection of the whole impeller, it is verified that the process optimization strategy based on the non-uniform allowance of ɛ influence factor has an obvious effect on the suppression of chattering, which is instructive for the machining and manufacturing of thin-walled parts with complex curved surfaces. [ABSTRACT FROM AUTHOR]

Single-crystal sapphire (α-Al2O3) has a wide range of applications in a variety of extreme environments due to its excellent mechanical and chemical properties as well as its stability under extreme service conditions. In addition, sapphire is a hard and brittle material, which makes it difficult to avoid the introduction of subsurface damage during processing, and the existence of a subsurface damage layer seriously affects the performance of the optical system. Therefore, this study focuses on the mechanism of subsurface damage in sapphire grinding, the suppression of damage depth, and the accurate and fast prediction of damage depth. A subsurface damage model for ultrasonic vibration–assisted grinding of sapphire was established by combining ultrasonic vibration single abrasive grain kinematics and dynamics analysis. The mechanism of ultrasonic vibration–assisted grinding was investigated by combining force and acoustic emission (AE) signals. And the effects of ultrasonic vibration–assisted grinding on the surface quality, subsurface damage form, and depth were analyzed. A comprehensive prediction model of SSD based on surface roughness (Sz) was established by combining indentation fracture mechanics and Gaussian process regression. Finally, by analyzing the influence law of each process parameter on the subsurface damage depth (SSD), the grinding process parameters were optimized, and the subsurface damage suppression strategy was proposed. This study provides theoretical guidance for the high-efficiency and low-damage grinding of sapphire. [ABSTRACT FROM AUTHOR]

In this work, the permeation flux equations, Richardson and Paripatyadar kinetic model, and lumen-shell mass balances were combined to develop a simulation model for the integrated CO 2 separation-dry reforming of methane (DRM) reaction in La 0.6 Sr 0.4 Co 0.8 Fe 0.2 O 3-δ (LSCF)-carbonate hollow fibre membrane reactor. The interactions of the reactants for the DRM and reverse water gas shift reactions were simulated at different operating parameters. Greater membrane area gave higher CO 2 and CH 4 conversions and H 2 /CO (syngas) molar ratio of ∼1. Lower temperature of 700 °C provided higher DRM performance in membrane reactor, which was due to the higher CO 2 permeation from the higher electronic conductivity of LSCF. Higher CH 4 amount in the sweep gas facilitated CO 2 permeation, conversion, syngas yield, and ratio. The optimum DRM performance of the membrane reactor was achieved at a lumen-to-shell flow rate ratio of 0.5, whereas the limiting factor was the CH 4 availability at the shell side. [Display omitted] • Richardson and Paripatyadar (R–P) kinetic model was used to simulate DRM and RWGS. • Permeation of CO 2 across LSCF-molten carbonate hollow fibre membrane was simulated. • MATLAB simulation of DRM in a ceramic-carbonate membrane reactor was developed. • Parametric study on temperature, membrane area, and flow conditions were presented. • RSM was used to optimize the DRM performance of membrane reactor. [ABSTRACT FROM AUTHOR]

Chronically implanting microelectrodes for high-resolution action potential recording is critical for understanding the brain. The smallest and most flexible electrodes, most suitable for chronic recordings, are also the most difficult to insert due to buckling against the thin but hard-to-penetrate brain meninges. To address such implantation challenges without introducing further damage to the brain, this paper presents our design and prototype of an inchworm-type insertion device that conducts a grip-feed-release incremental motion for planar microelectrode insertion. To optimize the operating parameters of the developed inchworm insertion device, experimental studies were conducted on the polyvinyl chloride-based brain-mimicking phantom to investigate the effects of (1) incremental insertion depth, (2) inserter drive shaft rotary speed, and (3) the resulting inchworm insertion speed, on the phantom (1) penetration rupture force and (2) dimpling depth at rupture. Analysis showed that all three factors had a statistically significant impact on the rupture force and dimpling depth. A moderate level of the resulting insertion speed yielded the lowest rupture force and dimpling depth at rupture. Low insertion speed levels were associated with higher rupture force while high insertion speeds led to a large variance in dimpling depth and potential insertion failure. To achieve such a moderate insertion speed, it would be preferred for both the incremental insertion depth and the drive shaft rotary speed to be at a moderate level. Such findings lay the foundation for enabling previously impossible buckling-free insertion of miniaturized flexible planar microelectrodes deep into the brain. [ABSTRACT FROM AUTHOR]

Electrohydrodynamic (EHD) printing is an additive manufacturing technique capable of microscale and nanoscale structures for biomedical, aerospace, and electronic applications. To realize stable printing at its full resolution, the monitoring of jetting behavior while printing and optimization of the printing process are necessary. Various machine vision control schemes have been developed for EHD printing. However, in-line machine vision systems are currently limited because only limited information can be captured in situ toward quality assurance and process optimization. In this article, we presented a machine learning-embedded machine vision control scheme that is able to characterize jetting and recognize the printing quality by using only low-resolution observations of the Taylor Cone. An innovative approach was introduced to identify and measure cone-jet behavior using low-fidelity image data at various applied voltage levels, stand-off distances, and printing speeds. The scaling law between voltages and the line widths enables quality prediction of final printed patterns. A voting ensemble composed of k-nearest neighbor (KNN), classification and regression tree (CART), random forest, logistic regression, gradient boost classifier, and bagging models was employed with optimized hyperparameters to classify the jets to their corresponding applied voltages, achieving an 88.43% accuracy on new experimental data. These findings demonstrate that it is possible to analyze jetting status and predict high-resolution pattern dimensions by using low-fidelity data. The voltage analysis based on the in situ data will provide additional insights for system stability, and it can be used to establish the error functions for future advanced control schemes. [ABSTRACT FROM AUTHOR]

Additive manufacturing (AM), owing to its unique manufacturing approach, can drive manufacturing towards higher levels of sophistication, flexibility, and intelligence within the context of Industry 4.0. However, the complexity of AM systems and their nature as data-intensive manufacturing domains present challenges in producing high-quality products. With the advancement of digital and computer technologies, data-driven machine learning (ML) has been widely applied in AM, as it provides effective methods for quality control, process optimization, and complex system modeling. This paper succinctly summarizes the various phases of utilizing ML to assist in AM processes. It elucidates the advantages of using ML over traditional methods in each phase, starting from the pre-processing phase of design for additive manufacturing (DfAM) and parameter optimization, through the processing phase of defect detection, to the post-processing phase of part quality assessment. The objective of DfAM is to optimize product design, taking into account the interactions between multiple variables. The nonlinear capability of ML can be effectively utilized in DfAM. Defect detection aims to ensure the repeatability, durability, and reliability of parts. Combining sensors with ML can guarantee part quality. Lastly, assessing part quality and inspecting various subsystems of AM are conducted from the perspective of the part's microstructure. This review explores the application of ML in addressing numerous issues related to the AM process workflow. It provides a comprehensive and systematic summary of the application of various ML models at different phases, investigates the potential of newer ML technologies to assist AM in the future, and concludes with the limitations of current ML applications in AM as well as future development directions. By intersecting these two dynamic fields, it aims to enrich the AM research domain. [ABSTRACT FROM AUTHOR]

In-situ consolidation of thermoplastic composites using Automated Fibre Placement (AFP) technology is an emerging manufacturing technique, offering tailored composite properties through customised processing parameters. Multiple competing parameters during AFP manufacturing influence the quality and mechanical performance of the laminates. These lay-up parameters are interrelated, and often require comprehensive experimental characterisation which is costly and time-intensive. This study aims to optimise the fracture toughness of in-situ consolidated thermoplastic composite (AS4/APC-2) and investigate the mechanisms that contribute to it. Taguchi's method is employed to efficiently analyse the effect of various process parameters at multiple levels. Based on the obtained results, a considerable effect of process parameters on Mode I and II fracture toughness is observed. The statistical analysis reveals that the Hot Gas Torch (HGT) temperature required for AFP processing significantly affects the Mode I fracture toughness, contributing to 33.8%. Whereas, the consolidation force, another key processing parameter in AFP notably affects Mode II fracture toughness, with the contribution of 81.8%. The analysis of variance (ANOVA) reveals interdependent processing parameter relations for both fracture modes. A validation test showed good agreement between the predicted fracture toughness and the experimental test. [ABSTRACT FROM AUTHOR]

Direct ink writing (DIW) is one of the facile and versatile manufacturing techniques for additive manufacturing of ceramics. 3D printed porous boehmite structures fabricated via DIW can offer significant benefits in terms of their high specific surface area and robust mechanical properties, rendering them suitable for applications like catalysis, adsorption, etc. The characteristics of the boehmite structures produced through 3D printing are significantly influenced by several parameters associated with the DIW process, including but not limited to solids concentration in the ink, resolution, and drying time. In this work, for the first time, we try to optimize the DIW parameters for 3D printing of porous boehmite structures to achieve better shape stability, resolution, and mechanical properties. A shear-thinning boehmite ink was prepared by peptization of boehmite powder in nitric acid and the rheological properties were optimized by varying concentrations of boehmite and nitric acid. Boehmite ink with a solid loading of 37 % and a pH of 4 was found to have the optimal rheological properties and printability. A Menger sponge porous structure was printed by DIW using the optimized ink with different sample sizes and resolutions. The resolution and size were optimized for better shape accuracy of printing and structural integrity. Crack-free boehmite structures were obtained by optimizing the drying time. Boehmite structures printed with higher resolution resulted in enhanced compressive strength and energy absorption, i.e., compressive strength and energy absorption of 0.5 mm resolution boehmite structure is enhanced by 208 % and 144 %, respectively, compared to the 1 mm resolution structure. [ABSTRACT FROM AUTHOR]

During hydrogen refueling, the data values determining the state of charge (SoC) of a vehicle can be missing due to internal and external factors. This causes inaccurate SoC estimation, resulting in oversupply or undersupply. To overcome this issue, an attention-based hydrogen refueling imputation (AHRI) model, which restores missing values, is proposed in this paper. In particular, considering that data variables can vary depending on the environmental conditions and equipment in a hydrogen refueling station (HRS), we use the attention mechanism. It determines the primary features, which improves the predictive performance and helps adapt to new conditions. Using the observed data during hydrogen refueling, we train the proposed AHRI model and verify its efficacy. Experimental results show that the proposed AHRI model outperforms existing imputation models significantly. Here, AHRI achieves 0.95 and 0.82 in terms of R 2 when 20% and 40% of the values are missing, respectively. These results indicate that the proposed model can be used to solve the data missing problems in HSRs. [ABSTRACT FROM AUTHOR]

Stropharia rugosoannulata is a valuable medicinal and food fungus with high nutritive value. Freeze-drying addresses the storage and transportation challenges of fresh Stropharia rugosoannulata, expanding its market while preserving its flavor and quality more effectively than other drying methods. This study optimizes the vacuum freeze-drying process for Stropharia rugosoannulata using an orthogonal experiment method. The process parameters were optimized to determine their effects on the quality of the vacuum freeze-dried product, including pre-freezing temperature, pre-freezing time, and freeze-drying time. The optimal conditions were identified as a pre-freezing time of 60 h, a pre-freezing temperature of −80 °C, and a freeze-drying time of 72 h. The optimal product exhibited a bright color close to its natural state, with minimal browning and its natural white color maintained post-drying. During the drying process, the internal structure of the raw materials remained intact. After drying, the finished product retained its natural form, making it suitable for sale on the market. The soluble protein content of the vacuum freeze-dried Stropharia rugosoannulata reached 68 mg/g. Optimizing the freeze-drying process can better preserve the tissue structure and bioactive substances of Stropharia rugosoannulata, providing a reference for high-quality food processing and showing potential for sustainable development. [ABSTRACT FROM AUTHOR]

Featured Application: Digital Twin used in real-time monitoring of operations performed on CNC technological equipment that will retrieve the relevant parameters of machine working. This article presents the development and implementation of a real-time monitoring solution designed for CNC machines, specifically applied to 150 industrial printing machines, leveraging Digital Twin (DT) technology. The system integrates an SQL database with Android and.NET interfaces, ensuring seamless data synchronization across all machines and optimizing production processes. The real-time monitoring enables immediate reflection of operational changes, enhancing predictive maintenance and reducing machine downtime. A notable feature of the system is its 1 s average data synchronization rate per machine, managing 150 resources distributed over a 10,000 mp area. This fast synchronization improves workflow coordination, reducing production time by approximately 10%, and minimizing operator delays caused by material issues, machine malfunctions, or product defects. The integration of advanced analytics further supports real-time decision-making, predictive maintenance, and performance optimization, aligning the solution with the objectives of Industry 4.0 and Industry 5.0 initiatives. This version reflects the specific results of the research, including the 1 s synchronization rate, the 10% reduction in production time, and the scalability of the system for 150 resources. [ABSTRACT FROM AUTHOR]

Copyright of Science & Technology of Food Industry is the property of Science & Technology of Food Industry Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Food Research & Development is the property of Food Research & Development Editorial Department and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Food Research & Development is the property of Food Research & Development Editorial Department and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Food Research & Development is the property of Food Research & Development Editorial Department and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Food Research & Development is the property of Food Research & Development Editorial Department and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Food Research & Development is the property of Food Research & Development Editorial Department and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

In the article, the problem of multi-criteria optimization of quality control mechanisms is analyzed. The presented method assumes the use of the NPGA genetic algorithm to simultaneously manage costs and the level of detecting non-conformities. The main assumption of the presented approach is to treat individual quality control procedures as vectors, whose elements are probability generating functions of defect detection. Each of these procedures generates certain operational costs and covers specific types of defects within its scope. The task of the presented algorithm is to indicate which procedure and to what extent should operate to ensure an appropriate level of non-conformity detection while minimizing costs. The article presents the theoretical foundations of the developed algorithm and the results of its implementation. The software has been developed in C++ with a particular focus on performance aspects. Its essence lies in the implementation of data structures introduced in the theoretical part, as well as methods for their rapid processing. Thanks to this approach, the entire program is scalable and can be used to solve multidimensional optimization problems. The presented approach may also find application in other areas of enterprise management. This will be possible primarily in cases where the effectiveness of procedures or devices is primarily evaluated based on probability. Therefore, the presented methods can provide effective optimization of other areas related to enterprise management. [ABSTRACT FROM AUTHOR]

Copyright of Food Research & Development is the property of Food Research & Development Editorial Department and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Food Research & Development is the property of Food Research & Development Editorial Department and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Food Research & Development is the property of Food Research & Development Editorial Department and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

For decades, solid solution strengthening with up to 9 wt.% Ni has been the only successful strategy to obtain high strength and high impact toughness in bcc‐structured (ferritic or tempered martensitic) cryogenic steels. Until now coherent nano‐precipitates, an effective strengthening agent at room temperature, cannot be used to improve properties at cryogenic temperatures. Here, a new type of Mo‐rich nano‐B2 precipitates formed in a 6.5 wt.% Ni steel upon doping with 0.2 wt.% of Mo is reported. These precipitates are not only fully coherent with the matrix but are also shearable at 77 K. A high precipitate number density in excess of 2 × 1024 m−3 has been achieved by an industrially feasible process optimization, which brings both the cryogenic strength and impact toughness of the steel to the same levels as those of 9Ni steels. The Mo‐rich B2 precipitation strengthening, therefore, opens a new avenue for the design and development of low‐cost high‐performance cryogenic steels. [ABSTRACT FROM AUTHOR]

Low‐angle bevel mesa structures are the key technological ideas needed to produce high‐quality vertical GaN devices. Nevertheless, the literature lacks sufficient information regarding the techniques that can yield the best results for obtaining the bevel angles smaller than approximately 5°.[1] Notably, for high p–n junctions NA/ND ratios, such low angles are necessary for the application of negative beveled edge termination and the efficient lowering of electric field at the surface.[1–6] In this work, the optimization of the beveled‐mesa fabrication technique for vertical GaN devices is demonstrated employing a plasma etching of the photoresist mask fabricated using a reflow process. By adjusting the resist reflow process parameters, in particular various BCl3 Cl2−1 ratios and inductively coupled plasma–reactive ion etching plasma power, a range of beveled mask angles as low as 10°–12° is achieved. However, those processes all show etching selectivity close to 1:1 between GaN and the photoresist. Even lower bevel mesa angles are reported for a novel process optimization that resulted in improving GaN to photoresist selectivity by a factor of 5–10. As a result, even smaller bevel mesa angles close to 1°–2° are able to be attained. The developed approach has the advantage of being relatively simple and cost effective. [ABSTRACT FROM AUTHOR]

The search for low-cost and effective adsorbents for the removal of organic dyes from contaminated water is urgently needed. The substantial amount of waste mushroom cultivation substrates generated in practical production can serve as an ideal material for the preparation of adsorbents. In this study, we investigated the main control parameters affecting the performance of magnetic mushroom substrate biochar and optimized the process of preparing biochar by using the Plackett–Burman and central composite design methods. Various analytical techniques including SEM, EDX, BET, and VSM were used to characterize the biochar. The results indicate that the carbonization temperature had the most significant impact on the yield and adsorption performance of biochar. Under the conditions of a carbonization temperature of 600 °C, a carbonization retention time of 1 h, and an impregnation ratio of 0.1, the yield and methylene blue adsorption value of magnetic biochar were 42.54% and 2297.04 μg/g, respectively, with a specific surface area of 37.17 m2/g. This biochar effectively removed methylene blue from the solution, demonstrating a high economic efficiency for wastewater treatment and pollution control. Furthermore, the adsorption–desorption cycle studies revealed its excellent stability and reusability. Additionally, based on the response surface methodology, a three-dimensional surface model of the adsorption performance of magnetic biochar under different carbonization conditions was established, providing a theoretical basis for the preparation of magnetic biochar from agricultural wastes. [ABSTRACT FROM AUTHOR]

Incremental sheet forming has emerged as an excellent alternative to other material forming procedures, incrementally deforming flat metal sheets into complex three-dimensional profiles. The main characteristics of this process are its versatility and cost-effectiveness; additionally, it allows for greater formability compared to conventional sheet forming processes. Recently, its application has been extended to polymers and composites. The following review aims to present the current state of the art in the incremental sheet forming of polycarbonate, an outstanding engineering plastic, beginning with initial studies on the feasibility of this process for polymers. Attention is given to the advantages, drawbacks, and main applications of incrementally formed polycarbonate sheets, as well as the influence of process parameters and toolpath strategies on features such as formability, forming forces, deformation and failure mechanisms, geometric accuracy, surface quality, etc. Additionally, new hybrid forming methods for process optimisation are presented. Finally, a discussion is provided on the technical challenges and future research directions for incremental sheet forming of polycarbonate and, more generally, thermoplastics. Thus, this review aims to offer an extensive overview of the incremental forming of polycarbonate sheets, useful to both academic and industrial researchers working on this topic. [ABSTRACT FROM AUTHOR]

Torrefaction was applied to enhance the fuel properties of sawdust (SD) and bark wood (BW), biomass wastes from the rubberwood processing industry. Design Expert (DE) software was used in an experimental design to study the effects of affecting factors including torrefaction temperature and time as well as the biomass size towards the desirable properties such as HHV, mass yield, fixed carbon content, and eco-efficiency values. Promising results showed that the HHVs of the torrefied SD (25 MJ/kg) and BW (26 MJ/kg) were significantly increased when compared to preheated SD (17 MJ/kg) and preheated BW (17 MJ/kg) and in a range similar to that of coal (25–35 MJ/kg). The TGA, FTIR, biomass compositions, and O/C ratios suggested that thermochemical reactions played a significant role in the torrefaction at which thermal degradation coupled with possible in situ chemical reactions took place, to some extent. The optimal conditions of the torrefaction were identified at 320 °C and 30 min for SD, and 325 °C and 30 min for BW. The maximum HHVs at the optimal condition were 22, 23, and 20 MJ/kg while the eco-efficiency values were 29.18, 27.89, and 13.72 kJ/kg CO2_eq*THB for torrefied SD, torrefied BW, and coal, respectively. The findings of this study indicate that torrefied rubberwood residues enhanced HHV, eco-efficiency, and less contribution to CO2 emissions compared to fossil fuels. [ABSTRACT FROM AUTHOR]

Clostridium autoethanogenum is a model microbe capable of converting carbon monoxide (CO) into bioethanol and useful biochemicals. Successful process optimization when using CO as the primary carbon source requires an understanding of the influence of process parameters and growth medium components on cell growth and end‐product formation. We conducted a full factorial analysis of the effects of CO total pressure, pH, yeast extract (YE), and cysteine concentrations on acetic acid, ethanol, and biomass production utilizing C. autoethanogenum with CO as the primary substrate. Maximum ethanol production of 0.71 g L–1 was obtained at a pH of 4.5, yeast extract concentration of 0.5 g L–1 , CO pressure of 1.8 atm, and cysteine concentration of 1.5 g L–1. There was a tenfold enhancement when the pH was lowered from 6 to 4.5 and the YE concentration was reduced to 0.5 g L–1. Response optimization using the reduced gradient algorithm confirmed these results. The information presented in this study could prove valuable for process engineering design by assisting with the selection of parameters that yield syngas blends specifically intended for increased ethanol production. [ABSTRACT FROM AUTHOR]

Purpose: Bloodstream infections (BSIs) are associated with significant morbidity, mortality and costs, while prolonged blood culture (BC) diagnosis may delay the initiation of targeted therapy. This study evaluates the impact of an optimized microbiology laboratory process on turnaround times, antibiotic use, clinical outcomes and economics for hospitalized BSI patients. Methods: A pre-post study was conducted in a Chinese hospital in which BSI derived BC results before (Oct. 2020- Sep. 2021) and after (Oct. 2021- Sep. 2022) newly implemented microbiology diagnostics and workflow changes were analyzed. Turnaround times, antibiotic initiation, length of stay and in-hospital costs were compared. Results: From 213 included patients, 134 were pre-optimization (pre-op) and 79 were post-optimization (post-op) cases. The median time from blood sample collection (BSC) to pathogen identification (ID) decreased from 70.12 to 47.43 h post-op (P < 0.001). The median time from BSC to the first ID report related initiation of pathogen-directed antibiotic use decreased from 88.48 to 47.85 h post-op (P < 0.001). The average hospital stay decreased from 19.54 to 16.79 days and 30-day readmissions declined from 18.7 to 13.9%, while the mean total antimicrobial drug usage costs decreased by 3,889 CNY per patient (P = 0.022) after optimization. Conclusions: Implementing new diagnostics technologies and optimizing laboratory workflows significantly reduced antimicrobial drug usage costs, shortened the time to ID results and improved the timeliness of appropriate antibiotic choices to treat BSIs. Investments in faster testing and process improvements were clearly beneficial for patient outcomes and healthcare economics. [ABSTRACT FROM AUTHOR]

In order to improve the utilization value of the erythritol mother liquor, the separation and purification of the erythritol mother liquor was selected in this study. The selected chromatographic separation programme for erythritol crystallizing mother liquor is as follows: Firstly, erythritol is resolved from mannitol and arabitol with DTF-01Ca (Suqing Group) resin and then mannitol is resolved from arabitol with 99Ca/320 (Dowex) resin. At the same time, the chromatographic conditions of the DTF-01Ca (Suqing Group) and 99Ca/320 (Dowex) resins were optimized, resulting in an optimal separation temperature and mobile phase flow rate of 70 °C, 10 ml/min. On this basis, a single-column chromatographic model was used to calculate the TD model parameter ( N ) and the mass transfer coefficient ( k m ) of the separation of erythritol mother liquor by DTF-01Ca (Suqing Group) and 99Ca/320 (Dowex) resins. The adsorption isotherms, TD model parameter ( N ) and the mass transfer coefficient ( k m ) provides data references for the design and operation of the simulated moving beds (SMB) separation system for the industrial-scale separation of erythritol crystallizing mother liquor. [ABSTRACT FROM AUTHOR]