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Modelling real processes often results in several suitable models. In order to be able to distinguish, or discriminate, which model best represents a phenomenon, one is interested, e.g., in so-called T-optimal designs. These consist of the (design) points from a generally continuous design space at which the models deviate most from each other under the condition that they are best fitted to those points. Thus, the T-criterion represents a bi-level optimization problem, which can be transferred into a semi-infinite one but whose solution is very unstable or time consuming for non-linear models and non-convex lower- and upper-level problems. If one considers only a finite number of possible design points, a numerically well tractable linear semi-infinite optimization problem arises. Since this is only an approximation of the original model discrimination problem, we propose an algorithm which alternately and adaptively refines discretizations of the parameter as well as of the design space and, thus, solves a sequence of linear semi-infinite programs. We prove convergence of our method and its subroutine and show on the basis of discrimination tasks from process engineering that our approach is stable and can outperform the known methods. [ABSTRACT FROM AUTHOR]

In this era of industrial revolution and diminishing petroleum reserves, alternative options (woody biomass) for building next-generation fuels need to be critically explored. Here, process engineering aspects of 5-hydroxymethylfurfural (5-HMF) from Bambusa bamboo in ionic liquid ([Bmim]Cl) in the presence of a catalyst, CrCl3, in batch reactor have been explored in great detail. 5-HMF upon hydrogenation liberates 2,5-dimethylfuran (DMF), which is a liquid fuel. To maximize the production of platform chemical-5-HMF, effects of different mixing speeds (150–1200 rpm) and temperatures (120–160 °C) were captured on the yield of 5-HMF and glucose. A highest 5-HMF yield (25%) was obtained at 120 °C and 1200 rpm within a reaction time of 6 h. Moreover, a kinetic analysis of transformation of biomass into 5-HMF was carried out using curve fitting to estimate kinetic constants k1, k2, and k3. Mixing at asymptotic limits, i.e., no mixing (0 rpm) and very high mixing (1200 rpm), enables us to devise mixing regimes: 0–400 rpm, mass transfer–limited regime; 400–1100 rpm, intermediate regime; and > 1100 rpm, reaction-limited regime. Thus, tremendous improvement in reaction rate constants (k1, k2, k3) was observed when operated at higher mixing conditions (reaction-limited regime). Mixing limitation for this IL-based system can be eliminated by operating the reactor above 800 rpm, whereas lower temperature restricts conversion of 5-HMF to LA and FA. Hence, higher mixing speeds, i.e., > 1100 rpm, and lower temperature, i.e., ≈ 120 °C, can be suggested as the optimum operating conditions for maximization of platform chemical-5-HMF in the catalytic conversion of Bambusa bamboo in batch reactor. [ABSTRACT FROM AUTHOR]

Production scheduling is a critical factor to enhancing productivity in manufacturing engineering and combinatorial optimization research. The complexity and dynamic nature of production systems necessitates innovative solutions. The Job Shop Flexible Programming Problem (FJSP) provides a realistic environment for production, where processing times are variable and uncertain, and multiple objectives need optimization. To solve the Multi-Objective Flexible Fuzzy Job Shop problem with partial flexibility (P-MOFfJSP), this paper proposes a hybrid metaheuristic approach that combines the Teaching–Learning-based Optimization (TLBO) algorithm with a Genetic Algorithm. The proposed algorithm of Adaptive TLBO (TLBO-A) uses two genetic operators (mutation and crossover) with an adaptive population reconfiguration strategy, ensuring solution space exploration and preventing premature convergence. We have evaluated the TLBO-A algorithm's performance on benchmark instances commonly used in programming problems with fuzzy variables. The experimental analysis indicates significant results, demonstrating that the adaptive strategy improves the search for suitable solutions. The proposed algorithm (TLBO-A) exhibits low variations (around 11%) compared to the best mono-objective heuristic for the fuzzy makespan problem, indicating its robustness. Moreover, compared with other heuristics like traditional TLBO, the variations decrease to around 1%. However, TLBO-A stands out as it aims to solve a multi-objective problem, improving the fuzzy makespan, and identifying good results on the Pareto frontier for the fuzzy average flow time, all within this low variation margin. Our contribution addresses the challenges of production scheduling in fuzzy time environments and proposes a practical hybrid metaheuristic approach. The TLBO-A algorithm shows promising results in solving the P-MOFfJSP, highlighting the potential of our proposed methodology for solving real-world production scheduling problems. [ABSTRACT FROM AUTHOR]

Modern manufacturing has to cope with dynamic and changing circumstances. Market fluctuations, the effects caused by unpredictable material shortages, highly variable product demand, and worker availability all require system robustness, flexibility, and resilience. To adapt to these new requirements, manufacturers should consider investigating, investing in, and implementing system autonomy. Autonomy is being adopted in multiple industrial contexts, but divergences arise when formalizing the concept of autonomous systems. To develop an implementation of autonomous manufacturing systems, it is essential to specify what autonomy means, how autonomous manufacturing systems are different from other autonomous systems, and how autonomous manufacturing systems are identified and achieved through the main features and enabling technologies. With a comprehensive literature review, this paper provides a definition of autonomy in the manufacturing context, infers the features of autonomy from different engineering domains, and presents a five-level model of autonomy — associated with maturity levels for the features — to ensure the complete identification and evaluation of autonomous manufacturing systems. The paper also presents the evaluation of a real autonomous system that serves as a use-case and a validation of the model. [ABSTRACT FROM AUTHOR]

Thin-film flows are frequently used in process engineering and are therefore of great interest in research. In most cases, waves occur which have a positive effect on the material and heat transport and significantly influence the dynamics of the film flow. A combination of µ-particle image velocimetry and confocal chromatic distance measurement is used to measure phase-averaged flow velocities in naturally developed film waves. A trigger signal is sent to the PIV system and a recording starts when a wave reaches the wave crest threshold. This makes it possible to trigger the µPIV system depending on the film thickness. The focus of this work is on the triggering of the wave crest. The functionality and quality of the measurements depend on the correct positioning of the confocal chromatic sensor, with the light spot at the level of the camera section. Simultaneously, the minimum, average, and maximum film thickness, as well as the wave amplitude and wave frequency, are recorded. Film flow is investigated on a glass plate at various angles (5°, 10°, 15°) of inclination, Reynolds numbers (30-200), and measurement positions. The results are compared with the theoretical solutions according to Nusselt. In addition, the influence of the tilt angle and the measuring position is explained. The measuring position, in particular, has a non-negligible influence since the characteristics of the film flow are largely dependent on the streamwise measuring position. Compared to the theoretical solutions according to Nusselt, the velocities and film thicknesses are significantly greater due to the presence of the waves. With increasing inclination angle and streamwise measuring position, the measured mean and maximum velocities of the film flow increase. They almost double when the inclination angle increases from 5° to 15°. When comparing the velocities depending on the measuring position (300–500 mm), it is noticeable that the velocities increase between 5 and 15%, depending on the angle, due to wave development and gravity. [ABSTRACT FROM AUTHOR]

Thread milling is increasingly used in engineering manufacturing because of its advantages, but there must be some error in the thread due to the machining principle. In response to this problem, this paper discussed the causes of thread milling error and established the machining error model for the full machining process of thread milling. Firstly, the geometric model of thread milling is developed based on the parametric definitions of the cutter tooth surface, thread surface, and tool-path. Then, based on the cutter motion characteristics of the thread milling penetration and retraction segments and machining segment, the machining error model is established. The calculation of machining error is transformed into solving for the relationship between the position of the intersection of the cutter profile line and the thread profile line generated in the cross-section, which means that the three-dimensional problem is transformed into the two-dimensional problem. Finally, the proposed model is proven via thread milling experiments. The simulation results of machining error agree well with the experimental results, which prove the correctness and validity of the model. The model provides theoretical support for further research into thread milling machining error. [ABSTRACT FROM AUTHOR]

The industrial domain is undergoing a major transformation, pushed forward by emerging technologies originating from research or industry. The resulting trend, better known by the term Industry 4.0, advances automation within these manufacturing companies by providing ubiquitous interconnection. This enables the integration of technologies mainly used in the Industrial Internet of Things (IIoT), Cyber-physical Systems (CPS) or Big Data with the goal to optimize production processes or facilitate intelligent decision-making. While those processes are progressively supported by methodologies coming from the area of artificial intelligence (AI) like machine learning algorithms, sustainable and consistent storing of production data becomes increasingly important. Concluding, production systems engineering and information engineering are correlating with each other, as the respective results could be used to the advantage of the respective other discipline. In order to address these issues while developing such flexible production systems, the Reference Architecture Model Industrie 4.0 (RAMI 4.0) has been introduced. However, practical applications are lacking as this standard is mainly described in theory, which makes it difficult to actually apply this framework. Thus, the main goal of this paper is to specify a detailed architecture description of the Information Layer to ensure the practical application of RAMI 4.0, which allows stakeholders to utilize model-based Systems Engineering (MBSE) for developing data aspects of industrial systems on the one hand and enable Information Engineering on the other hand. Supported by the concept of the Zachman Framework, the resulting architecture is applied and validated with the help of a real-world case study. [ABSTRACT FROM AUTHOR]

Numerical models representing geological reservoirs can be used to forecast production and help engineers to design optimal development plans. These models should be as representative as possible of the true dynamic behavior and reproduce available static and dynamic data. However, identifying models constrained to production data can be very challenging and time consuming. Machine learning techniques can be considered to mimic and replace the fluid flow simulator in the process. However, the benefit of these approaches strongly depends on the simulation time required to train reliable predictors. Previous studies highlighted the potential of the multi-fidelity approach rooted in cokriging to efficiently provide accurate estimations of fluid flow simulator outputs. This technique consists in combining simulation results obtained on several levels of resolution for the reservoir model to predict the output properties on the finest level (the most accurate one). The degraded levels can correspond for instance to a coarser discretization in space or time, or to less complex physics. The idea behind is to take advantage of the coarse level low-cost information to limit the total simulation time required to train the meta-models. In this paper, we propose a new sequential design strategy for iteratively and automatically training (kriging and) cokriging based meta-models. As highlighted on two synthetic cases, this approach makes it possible to identify training sets leading to accurate estimations for the error between measured and simulated production data (objective function) while requiring limited simulation times. [ABSTRACT FROM AUTHOR]

In order to enhance the strength of 3D-printed parts made of polymer materials and reduce the anisotropy caused by the fused filament fabrication process, this paper proposes an inter-layer interleaved composite path planning method based on the directional partition of the principal stress field. This method not only ensures intra-layer reinforcement under specific working conditions but also increases the strength of the printed part by enhancing the adhesion of adjacent layers through staggered filling between layers. The mechanical performance is improved by 10 to 30% compared to other conventional filling patterns such as the zigzag filling algorithm. Inter-layer interleaved composite enhancement path planning method is suitable for path planning of structures with complex shapes and is easily integrated into existing general computer-aided engineering processes. [ABSTRACT FROM AUTHOR]

The paper discusses the aspects of granulometric and morphological analysis of powders used in the technology of selective laser melting. Studies have been carried out to obtain statistical data concerning the particle size distribution, as well as their integral and differential distribution. The aspects of independent significance in this work had to do with identifying the morphological features of the particles. Experimental studies were carried out using certified and validated equipment. Conclusions were drawn about the specific features of the analyzed powders, and directions for further research were proposed, including the formation of databases of powder materials, which can be of value to process engineers who use additive technologies in practice. [ABSTRACT FROM AUTHOR]

Turning is recognized as one of the main manufacturing processes. Feed and cutting depth are investigated by researchers who aim to continuously improve this process. Faced with sustainability challenges, "greener" manufacturing engineering is sought, using statistical methods and machining techniques with minimal or no use of cutting fluid. Generally, roughness quantifies the quality of the machined surface, an important property in tribology. Classical amplitude parameters are insufficient to determine functional properties and are necessary to associate them with more significant ones. Chip formation also influences surface quality, as it is related to the interaction at the chip/tool interface. This study investigated the influence of the feed rate and cutting depth on the Rq, Rsk, Rku, Rk, Rpk and Rvk roughness parameters, and the chip thickness ratio during longitudinal dry turning of AISI 1020 steel. Trend measurements, a normality test, factorial analysis of variance and linear correlation were used in the methodology. Results showed predominant influence and correlation of the feed on the roughness parameters and the chip thickness ratio compared to the cutting depth. The analysis performed on the insert rake surface led to the hypothesis that the chip breaker geometry provides changes in chip deformation as the cutting depth increases. Through linear and polynomial trend lines drawn on the graphs, it was found that the roughness and the chip thickness ratio have a positive correlation with the feed, but in different proportions. [ABSTRACT FROM AUTHOR]

Over the past few decades, several methods have been introduced for the recognition of machining features from design files. These methods entail considerable effort to infer the characteristics of machining features from the design and manufacturing information commonly expressed in different and incompatible formats on the geometry, topography, dimensions, tolerances, and surface finishes. The competence of these traditional methods in implementing their assignment is still challenging. The deep learning approach as an advanced facility in artificial intelligence is considered as a promising substitute. This approach has already been successfully and efficiently employed in different facets of social life. Its use in manufacturing engineering and industries is considered as a breakthrough, especially in some disciplines such as process planning, and machining feature recognition is in its embryonic phase. The input data in the present methods is the output information of a computer-aided design system. This is commonly faced with some limitations, the most serious ones of which are the loss of data from the design file occurring due to the geometric interference of features and slow extraction of features due to the extensive and excessive information existing in the design files. In the present study, a deep learning-based system named MFR-Net has been developed for the recognition of machining features from the images of workpieces. In addition to CAD systems, other tools such as the camera images of workpieces can also be employed to enter the input. The MFR-Net can also identify other relevant information needed for machining, including the position of the features, dimensions, and various symbols such as numbers, decimal points, and positive and negative signs employed in tolerances, parentheses, etc. [ABSTRACT FROM AUTHOR]

Organizations are actively seeking efficient solutions for the management and protection of their assets. However, Cybersecurity is a vast and complex domain, especially for large enterprises because it requires an interdisciplinary approach. Knowledge Graphs are one of the mechanisms that organizations use to explore security among assets and possible attacks. The grounding of concepts is fundamental to implementing Knowledge Graphs, and it is one of the most relevant ontology applications. Therefore, Cybersecurity Ontologies have emerged as an important research subject. The first contribution of this paper is a search for previously existing works that have defined Cybersecurity Ontologies. We found twenty-eight ontologies in this search. Based on this result, we propose a Cybersecurity Terminological Validation and a Framework for Classifying Ontologies. Then, we provide a cross-analysis of these two proposals and present a proposal of best practices for improving the ontological approach in the cybersecurity domain. We also discuss the impact of this proposal with regard to the Ontology Engineering process. Our goal is to provide a solution that meets the organization's needs in terms of Cybersecurity and to contribute to Ontology Engineering research. [ABSTRACT FROM AUTHOR]

The engineering of mechatronic production systems is complex and requires various disciplines (e.g., systems, mechanical, electrical and software engineers). Model-driven engineering (MDE) supports systems development and the exchange of information based on models and transformations. However, the integration and adoption of different modeling approaches are becoming challenges when it comes to cross-disciplinary work. VIATRA is a long-living enduring and mature modeling framework that offers rich model transformation features to develop MDE applications. This study investigates the extent to which VIATRA can be applied in the engineering of mechatronic production systems. For this purpose, two model transformation case studies are presented: "SysML – AutomationML" and "SysML4Mechatronics – AutomationML." Both case studies are representative of structural modeling and interdisciplinary data exchange during the development of mechatronic production systems. These case studies are derived from other researchers in the community. A VIATRA software prototype implements these case studies as a batch-oriented transformation and serves as one basis for evaluating VIATRA. To report on our observations and findings, we built on an evaluation framework from the MDE community. This framework considers 14 different characteristics (e.g., maturity, size, execution time, modularity, learnability), according to the Goal-Question-Metric paradigm. To be able to evaluate our findings, we compared VIATRA to ATL. We applied all cases to a lab-size mechatronic production system. We found that, with VIATRA, the same functions for model transformation applications can be achieved as with ATL, which is popular for model transformations in both the MDE and the mechatronic production systems community. VIATRA combines the relational, imperative, and graph-based paradigms and enables the development and execution of model-to-model (M2M) and model-to-text (M2T) transformations. Furthermore, the VIATRA internal DSL is based on Xtend and Java, making VIATRA attractive and intuitive for users with less experience in modeling than in object-oriented programming. Thus, VIATRA leads to an interesting alternative for the model-driven engineering of mechatronic production systems. It has the potential to reduce the complexity during the development of model transformations. To conclude, this paper evaluates the applicability of VIATRA, its strengths and limitations. It provides lessons learned and insights that can stimulate further research in the MDE for mechatronic production systems. [ABSTRACT FROM AUTHOR]

Machine learning methods have shown potential for the optimization of production processes. Due to the complex relationships often inherent in those processes, the success of such methods is often uncertain and unreliable. Therefore, understanding the (algorithmic) behavior and results of machine learning methods is crucial to improve the prediction of production processes. Here, mathematical tools may help. This paper shows how efficient algorithms for the training of neural networks and their retraining in the framework of transfer learning are expressed in a discrete as well as a time-continuous formulation. The latter can be analyzed and investigated using mathematical techniques from kinetic gas dynamics. The results obtained provide a first step towards explainable artificial intelligence. Based on the mathematical description, an adapted ensemble method for retraining of neural networks is proposed and compared with backpropagation algorithms. The process of training and retraining is a common task and therefore demonstrated for two very different production processes. The first one involves the prediction of specific cutting forces and the second one the prediction of particle properties in a plasma spraying coating process. For both use cases, the presented algorithms are applied and their performance is evaluated giving thereby an indication how mathematically inspired methods improve classical tasks in production processes. [ABSTRACT FROM AUTHOR]

Modern cutting tools like end mills, drilling tools, and reamers underlie high requirements regarding geometrical accuracy, cutting edge quality, and production costs. However, the potential for process optimization is limited due to the process kinematics during grinding. Consequently, a novel tool grinding process for the manufacture of cutting tools has been developed recently at the Institute for Production Engineering and Machine Tools (IFW). This continuous generating grinding process allows the simultaneous production of all flutes and circumferential flank faces of rotational symmetrical cutting tools. The present paper focuses on the geometrical process design and develops a method to determine the necessary basic rack and process parameters in order to create a desired cutting edge geometry by continuous generating grinding. The developed method can define all parameters with an accuracy of up to 5 µm and 0.2° within a simulation in five iteration steps and allows not only the quantitative design of the cutting tool geometry but a qualitative modification of the flute geometry as well. Subsequently performed grinding tests showed that the presented method allows the design of grinding worms for continuous generating grinding of cutting tools and enables the successful implementation of these processes. [ABSTRACT FROM AUTHOR]

This paper presents investigation of surface veracity of Wire EDM machined surfaces of Al / ZrO 2 (p) - MMC obtained utilizing the optimal parametric combinations. The effect of process parameters on the desired performance characteristics, i.e., minimum spark gap (SG), maximum material removal rate (MRR) and minimum surface roughness [SR( R a & R t )], is also presented. The second-order numerical expressions developed by response surface methodology (RSM) are used as a function in.M file. Genetic Algorithm and Direct Search Toolbox of MATLAB ® are utilized to identify the multi-objective optimum set of the process parameters for the desired performance characteristics. In the present paper, surface veracity aspects that include surface defects and thickness of recast layer are investigated for the machined surfaces obtained after Wire EDM to achieve the desired optimum performance characteristics. From the investigation, it can safely be concluded that for the Wire EDM of Al / ZrO 2 (p) - MMC s, lower parameter setting values of pulse width and short pulse time along with higher parameter setting value of time between pulses are desirable from the view point of good surface veracity of the machined surfaces as well as to obtain low average recast layer thickness ( Rl tavg ). The Rl tavg decreases as weight fraction of ZrO 2 (p) increases in Al / ZrO 2 (p) - MMC from 5 to 15%. The investigation of surface veracity of the Wire EDM obtained surfaces will provide the significant guidelines to the production engineers to plan the need of subsequent operations to improve the surface veracity and further the life of the products fabricated using these MMCs. [ABSTRACT FROM AUTHOR]

Optimizing machining parameters and pass numbers under machining stability constraint is important for manufacture engineers planning a multi-pass milling operation. Considering that milling stability varies with the machine tool dynamics when the tool and workpiece position changes, a generalized regression neural network (GRNN) is adopted to obtain the position-dependent stability constraint. Then a multi-objective optimization model of a multi-pass milling is established, where the objectives are the total cutting time (Tc) and surface roughness (Ra) and the variables are the machining position and cutting parameters for each pass. A non-dominated sorting genetic algorithm (NSGA-II) is introduced to solve the multi-objective optimization model for providing an optimal Pareto front, from which one satisfactory solution well balancing the Tc and Ra is selected by combining the entropy-weighted algorithm (EWA) and technique for ordering preferences by similarity to ideal solution (TOPSIS). A case study was carried out to establish a multi-objective optimization model after the milling stability constraint and surface roughness were predicted with the aid of GRNN and back-propagation neural network (BPNN), respectively. An ideal solution containing optimal machining position and cutting parameters was solved using NSGA-II, EWA, and TOPSIS, which was compared with solutions of two mono-objective models for optimizing Tc and Ra, respectively, to validate its feasibility. The observed stability and acceptable Ra value of the milling test under the ideal solution also indicated that the proposed optimization method can realize a trade-off between two conflict objectives and fascinate the process planning of a multi-pass milling. [ABSTRACT FROM AUTHOR]

The removal of platinoids (Pd, Rh, and Ru) from irradiated fuel the yield of which is kilograms per ton is a complex problem due to a number of technological and economic reasons. The paper proposes ways of a phased approach to the introduction of technology for the recovery of platinum-group metals (PGM) at one of the radiochemical enterprises. Information is presented on the dynamics of production volume and prices of PGMs in the world over the past decades and on the possibility of using reactor platinoids in various fields. According to the authors, it is of interest to consider the problem of isolating and using reactor Pd in ​​hydrogen power engineering and in the processing of waste from radiochemical enterprises. [ABSTRACT FROM AUTHOR]

Collagen extracted from fishes has been appearing as an alternative for commercial porcine and bovine collagen and it has been considered interesting especially for membrane manufacturing in tissue engineering. Despite the positive in vitro effects of fish collagen membranes, there is still no understanding of all the benefits that this natural biomaterial plays in the wound healing process, due to the lack of compilation of the results obtained in animal studies. In this sense, the purpose of this study was to perform a systematic review of the literature to examine the effects of fish collagen membranes for skin wound healing in experimental models of skin wound. The search was carried out according to the orientations of Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA), and the descriptors of the Medical Subject Headings (MeSH) were defined: "fish," collagen," "skin," and "in vivo". A total of 10 articles were retrieved from the databases PubMed and Scopus. After the elegibility analyses, this review covers the different origins of fish collagen reported in the different papers from the beginning of 2015 through the middle of 2021. The results were based mainly on histological analysis and macroscopic evaluation, and fish skin collagen was responsible for improving the wound healing rate and the process of reepithelization and collagen deposition. In conclusion, fish skin collagen has shown positive results in in vivo studies and may be a potential biomaterial in tissue engineering. [ABSTRACT FROM AUTHOR]

Process reuse technology has been widely studied and applied in the manufacturing industry. However, the current NC machining process reuse generally assumes that the micro process is compatible with the macro process but, in fact, reusable processes from similar local structures from multiple parts are usually incompatible with each other when under the overall manufacturing requirements of the target parts. As a result, a large number of user interactions are still required for modification and adjustment in practical engineering applications and process reuse do not provide a significant improvement to the design efficiency. Therefore, an efficient NC process scheme generation method based on reusable macro and micro process fusion is proposed in this paper. Firstly, according to the calculation of semantic distance of process design intention, the micro process is mapped to the macro process to realize fusion of the two processes, and a compatibility credibility evaluation model is established to evaluate the fusion results. Then, when the fusion result is credible, the machining areas corresponding to the process scheme are adjusted and optimized at the geometric level. The adjustment and optimization of machining areas provides the integration of machining areas and the optimization of the machining sequence. Finally, the effectiveness and feasibility of the proposed method are verified through a test of example parts. [ABSTRACT FROM AUTHOR]

Game-based learning (GBL) is familiar in several respects, as it can embed problem-solving challenges in an interactive virtual environment to improve students' motivation. Designing a stealth assessment is a tedious task that requires experienced professionals to manually derive evidence and competency approaches to precisely deduce student knowledge and skills. This necessitates that stealth assessment designers perform manual feature engineering and develop probabilistic graphical models. Since the manual feature engineering process is complex, this paper develops Optimal Deep Learning with GBL (ODL-GBL) for stealth assessment. The proposed ODL-GBL model involves parameter-tuned bi-directional long short-term memory (Bi-LSTM) with an oppositional salp swarm algorithm. The optimal Bi-LSTM model extracts hierarchical features from low-level data in an automated way (e.g., a series of student behaviours in a GBL platform) to infer evidence for competencies. The proposed ODL-GBL model alleviates the need for the costlier and more tedious process of developing evidence models. A detailed experimental analysis is used to validate the effective predictive performance of the proposed method. Among all the compared methods, the Bi-LSTM model shows superior performance, with accuracies of 60.56% and 61.34% under 70 and 40 hidden units, respectively. [ABSTRACT FROM AUTHOR]

Engineering plastics have specific properties in strength, hardness, impact resistance, and aging persistence, often used for structural plates and electronic components. However, the holes made by the drilling process always shrink after the cutting heat dispersion due to their high thermal expansion coefficient. Drilling parameters must be discussed thoughtfully especially in the small-hole fabrication to acquire a stable hole quality. This study developed parameter models by the Taguchi-based neural network method to save the experimental resources on the drilling of engineering plastic, polyetheretherketone (PEEK). A three-level full-factorial orthogonal array experiment, L27, was first conducted for minimizing the thrust force, hole shrinkage in diameter, and roundness error. In terms of the network modeling, four variables were designated to the input layer neurons included the three drilling parameters (spindle speed, depth of peck-drilling, feed rate) and the thrust force detected, and that of the output layer neurons were two hole characteristics of diameter shrinkage and roundness. The models were trained by a stepped-learning procedure to expand the network's field information stage by stage. After three stages of training, the models developed can provide precise simulations for the network's training sets. For the non-trained cases, the prediction accuracy of the hole's characteristics discussed was below 1 μm in the drilling of a 1-mm-diameter hole. [ABSTRACT FROM AUTHOR]

Prototyping, whether physical, virtual, or computational, is an important step in the engineering design process. Iterative prototyping strategies are commonly taught in engineering curricula and implemented in industry, but there may be other ways to approach the prototyping process. Engineers often use physical prototypes to learn about their designs, communicate ideas, and validate effectiveness. In this study, the effects of iterative and parallel prototyping strategies are compared through a design competition with a heavy focus on gaining knowledge from the physical models. Design success, engineering design self-efficacy, and solution space exploration are considered to evaluate the different effects of these two prototyping strategies. Results suggest that a parallel prototyping strategy yields greater design success, increased confidence and reduced anxiety when conducting engineering design, and greater exploration of the solution space. In addition, participants seem largely unaware of these benefits based a post-prototyping survey. This work shows the value of parallel prototyping, which has implications for how prototyping is taught to engineering novices and how engineering designers in industry should approach the prototyping process. This study also provides strong evidence for a need to study the benefits and drawbacks of a parallel prototypin approach in more complex situations. [ABSTRACT FROM AUTHOR]

Coenzyme Q (CoQ) serves as an electron carrier in aerobic respiration and has become an interesting target for biotechnological production due to its antioxidative effect and benefits in supplementation to patients with various diseases. Here, we review discovery of the pathway with a particular focus on its superstructuration and regulation, and we summarize the metabolic engineering strategies for overproduction of CoQ by microorganisms. Studies in model microorganisms elucidated the details of CoQ biosynthesis and revealed the existence of multiprotein complexes composed of several enzymes that catalyze consecutive reactions in the CoQ pathways of Saccharomyces cerevisiae and Escherichia coli. Recent findings indicate that the identity and the total number of proteins involved in CoQ biosynthesis vary between species, which raises interesting questions about the evolution of the pathway and could provide opportunities for easier engineering of CoQ production. For the biotechnological production, so far only microorganisms have been used that naturally synthesize CoQ10 or a related CoQ species. CoQ biosynthesis requires the aromatic precursor 4-hydroxybenzoic acid and the prenyl side chain that defines the CoQ species. Up to now, metabolic engineering strategies concentrated on the overproduction of the prenyl side chain as well as fine-tuning the expression of ubi genes from the ubiquinone modification pathway, resulting in high CoQ yields. With expanding knowledge about CoQ biosynthesis and exploration of new strategies for strain engineering, microbial CoQ production is expected to improve. [ABSTRACT FROM AUTHOR]

The present study aims to increase pyruvate production by engineering Yarrowia lipolytica through modifying the glycerol metabolic pathway. Results: Wild-type Yarrowia lipolytica (Po1d) was engineered to produce six different strains, namely ZS099 (by over-expressing PYK1), ZS100 (by deleting DGA2), ZS101 (by over-expressing DAK1, DAK2, and GCY1), ZS102 (by over-expressing GUT1 and GUT2), ZS103 (by over-expressing GUT1) and ZSGP (by over-expressing POS5 and deleting GPD2). Production of pyruvate from engineered and control strains was determined using high-performance liquid chromatography (HPLC). Subsequently, the fermentation conditions for producing pyruvate were optimized, including the amount of initial inoculation, the addition of calcium carbonate (CaCO3), thiamine and glycerol. Finally, for scaled-up purposes, a 20-L fermentor was used. It was observed that pyruvate production increased by 136% (8.55 g/L) in ZSGP strain compared to control (3.62 g/L). Furthermore, pyruvate production by ZSGP reached up to 110.4 g/L in 96 h in the scaled-up process. We conclude that ZSGP strain of Y. lipolytica can be effectively used for pyruvate production at the industrial level. [ABSTRACT FROM AUTHOR]

As a nontraditional processing technology, electrochemical discharge machining (ECDM) can process glass and engineering ceramics precisely. This technology has proven to be a potential process for glass 3D microstructure. However, the key to expanding the application of ECDM is how to improve machining accuracy. This research conducted micro-hole and microgroove machining. Power voltage and frequency influence on hole processing efficiency, hole entrance diameter, and hole limit depth was explored. We considered four factors affecting ECDM—the voltage and frequency of the pulse power supply, the tool electrode feed rate, and the rotation speed. We studied their influence on the roughness of the microgrooves. The results show that machining efficiency, entrance diameter, and limit depth of micro-holes increased with the increase in voltage but decreased with the increase in power frequency. The results show that the roughness of microgrooves has an apparent positive correlation with the power voltage. In contrast, it had an evident negative correlation with the power frequency and electrode speed. The bottom surface roughness of microgrooves can be as minor as 0.605 μm. Various complex 3D microstructures on the glass surface by layer-by-layer method proved the great potential of ECDM. [ABSTRACT FROM AUTHOR]

To meet the requirement of lightweight, an increasing number of solid shafts are designed to be hollow in transportation industry. In this study, a novel method of flat-knifing cross-wedge rolling (FCWR) with single guide is proposed including a modified roller, a horizontal mill, and a single-guide structure, and its key problems are studied by numerical simulations and experimental tests. A mathematical model of FCWR roller is established, which reveals that the wedge length of rollers is effectively reduced by modifying knifing wedge from normalized roller. Furthermore, a horizontal multifunctional mill is invented and constructed to carry out the FCWR experiment with single guide. According to the results from the numerical simulations and corresponding experiments, it is observed that the typical defects of hole expansion and knifing groove are absolutely avoided because the improved flat-knifing wedge produces a radial force to shrink the inner hole and avoid the deformation concentration of the outer surface during knifing stage. Moreover, the single-guide rolling performed in the horizontal mill efficiently improves rolling stability because the workpiece is restricted into a smaller workspace. To the authors' knowledge, all these integrated improvements of FCWR roller, single-guide rolling, and horizontal mill are innovative, which are of great engineering significance to manufacture hollow shafts on account of the advantages of avoiding forming defect, reducing roller diameter, improving rolling stability, and simplifying mill structure. [ABSTRACT FROM AUTHOR]

In this study, we examine innovative design practices on the Saint-Nazaire Airbus factory shop floor. The engineering and manufacturing engineering departments are in charge of the design of products and their industrialization, even though the factory is usually seen as a place for manufacturing, rather than design. However, there is also design activity in a factory that is devoted to the optimization of manufacturing processes. In this study, we highlight an alternative form of design that relies on a collective exploratory approach. A total of 30 projects from the Saint-Nazaire Airbus factory were selected and analyzed. Of these, two were selected as case studies to illustrate the factory's different design methods. Subsequently, quantitative analysis provided evidence of the existence of two design regimes: closed prescription and expandable prescription. The resulting solutions were examined, and it was found that designs under the expandable prescription regime provided more robust long-term solutions. This study offers new perspectives for reexamining innovation in manufacturing and exploring design activity on factory shop floors. [ABSTRACT FROM AUTHOR]

The smart factories that are already beginning to appear employ a completely new approach to product creation. Smart products are uniquely identifiable and know both their current status and alternative routes to achieving their target state. Smart factories allow individual customer requirements to be met, meaning that even one-off items can be manufactured profitably. In smart industry, dynamic business and engineering processes enable last-minute changes to design and production, delivering the ability to respond flexibly to disruptions and failures on behalf of suppliers. This paper presents a case study of product development and design process renovation according to changeability paradigm in one-of-a-kind industrial environment. It demonstrates how integration of changeability with agile design strategies crucially contribute to improve the operations of a highly individualized product development business. Successful management of 'never-ending' engineering changes appears to be the most important aspect in this field. Contribution of the presented work is a generalized framework that demonstrates how companies in such specific environments can improve competitiveness through the utilization of changeability concepts. The included case study validated the proposed changeability model and offers valuable insights into how to implement this in practice. [ABSTRACT FROM AUTHOR]

Road construction is an important engineering process involving two of the most common materials, namely asphalt and concrete, both of which having various environmental effects. This study aims to review documents from 2000 to 2020 using the scientometric analysis method. The documents about carbon footprint of road pavements were obtained from Web of Science (WOS) and analyzed using scientometric analysis tools in terms of citation, the number of published documents and top journals, institutions, authors, countries, and keywords. Finally, the trends of researches on carbon emission from road pavement construction and the building sector were compared in brief. This investigation can provide researchers with the right perspective for future studies in this field. [ABSTRACT FROM AUTHOR]

Glass-fibre-reinforced unsaturated polyester (GFRUP) is one the thermoset composites that has many applications in manufacturing engineering. CO2 laser cutting of this composite has been significantly developed in industries. It is believed that the presence of glass fibre network in the matrix of thermoset affects the laser cutting process of this composite. In addition, the three-dimensional matrix of monomers along with carbon atoms in the atomic network's joints can also influence the laser cutting parameters. The aim of this research is to experimentally investigate the effect of these complicated structural properties on the CO2 laser-air cutting of GFRUP sheets. First, the maximum cutting speeds, in which the cut occurs, were obtained for various sheet thicknesses using a variety of powers. The kerf width was measured with using an optical stereo-microscope. Then, the laser cutting mechanism was discussed using the SEM of the cut edge surfaces. The volume cutting efficiency was also studied. The results indicate that the upper kerf width is wider than the lower kerf width. The upper kerf width is larger than the spot size of 0.3 mm. The chaotic striations on the cut edge surface are the resolidified carbon-glass fibres compounds and can be seen as something like stalactites which are in black due to the carbon dust. Opening of the lower kerf due to the shrinkage of the resolidified carbon-glass fibre compounds is reported as a phenomenon. The cutting efficiency decreases as the sheet thickness increases. [ABSTRACT FROM AUTHOR]

With the development of metabolic engineering and synthetic biology, a large number of industrially important products has been developed using engineered microorganisms, thereby promoting the sustainable development of society and the economy. However, this approach is challenged by the increasing demand for more complicated biosynthesis or bioconversion tasks. The emergence of coculture engineering, which divides the labor between cocultured strains, presents a new perspective for conducting heterologous biosynthesis and improving the bioproduction performance of target products. This new approach reduces the metabolic burden on each strain and improves the conversion of products beyond the limit of the metabolic capacity of a single microbial strain. Recent advances using coculture engineering for the production of natural products, chemicals and biofuels, and environmental applications are reviewed. Future directions and potential challenges for future research in this field are also discussed. [ABSTRACT FROM AUTHOR]

In this contribution, a concept is presented that combines different simulation paradigms during the engineering phase. These methods are transferred into the operation phase by the use of data-based surrogates. As an virtual production scenario, the process combination of thermoforming continuous fiber-reinforced thermoplastic sheets and injection overmolding of thermoplastic polymers is investigated. Since this process is very sensitive regarding the temperature, the volatile transfer time is considered in a dynamic process chain control. Based on numerical analyses of the injection molding process, a surrogate model is developed. It enables a fast prediction of the product quality based on the temperature history. The physical model is transferred to an agent-based process chain simulation identifying lead time, bottle necks and quality rates taking into account the whole process chain. In the second step of surrogate modeling, a feasible soft sensor model is derived for quality control over the process chain during the operation stage. For this specific uses case, the production rejection can be reduced by 12% compared to conventional static approaches. [ABSTRACT FROM AUTHOR]

A three-dimensional computer model was developed for the integrated chemical engineering process of water shutoff of a high-water-cut oil-bearing rock formation at given geophysical characteristics of the bottomhole formation zone and given rate and volume of a water shutoff solution injected into the formation. The model makes it possible to determine the parameters of filling of pores of the formation with the water shutoff solution, which are necessary for calculating the well's oil and water production rates. The adequacy of the developed mathematical model was checked against the results of a comparative analysis of the calculated values of the water cut and the oil production rate with the actual well performance data after the water shutoff: the mean absolute errors in oil production rate and water cut were 0.5 t/day and 0.9%, respectively. [ABSTRACT FROM AUTHOR]

Engineering simulation accelerates the development of reliable and repeatable design processes in various domains. However, the computing resource consumption is dramatically raised in the whole development processes. Making the most of these simulation data becomes more and more important in modern industrial product design. In the present study, we proposed a workflow comprised of a series of machine learning algorithms (mainly deep neuron networks) to be an alternative to the numerical simulation. We have applied the workflow to the field of dental implant design process. The process is based on a complex, time-dependent, multi-physical biomechanical theory, known as mechano-regulatory method. It has been used to evaluate the performance of dental implants and to assess the tissue recovery after the oral surgery procedures. We provided a deep learning network (DLN) with calibrated simulation data that came from different simulation conditions with experimental verification. The DLN achieves nearly exact result of simulated bone healing history around implants. The correlation of the predicted essential physical properties of surrounding bones (e.g. strain and fluid velocity) and performance indexes of implants (e.g. bone area and bone-implant contact) were greater than 0.980 and 0.947, respectively. The testing AUC values for the classification of each tissue phenotype were ranging from 0.90 to 0.99. The DLN reduced hours of simulation time to seconds. Moreover, our DLN is explainable via Deep Taylor decomposition, suggesting that the transverse fluid velocity, upper and lower parts of dental implants are the keys that influence bone healing and the distribution of tissue phenotypes the most. Many examples of commercial dental implants with designs which follow these design strategies can be found. This work demonstrates that DLN with proper network design is capable to replace complex, time-dependent, multi-physical models/theories, as well as to reveal the underlying features without prior professional knowledge. [ABSTRACT FROM AUTHOR]

The theory of fractional integral inequalities plays an intrinsic role in approximation theory also it has been a key in establishing the uniqueness of solutions for some fractional differential equations. Fractional calculus has been found to be the best for modeling physical and engineering processes. More precisely, the proportional fractional operators are one of the recent important notions of fractional calculus. Our aim in this research paper is developing some novel ways of fractional integral Hermite–Hadamard inequalities in the frame of a proportional fractional integral with respect to another strictly increasing continuous function. The considered fractional integral is applied to establish some new fractional integral Hermite–Hadamard-type inequalities. Moreover, we present some special cases throughout discussing this work. [ABSTRACT FROM AUTHOR]

Wetting is a fundamental and important phenomenon that is encountered in various engineering processes, and particularly, the understanding of mesoscopic dynamic wetting of not only pure liquids but also suspensions with nanoparticles is required with the recent miniaturization of devices. Therefore, it is important to establish a technique to precisely measure the dynamic behavior of liquid thin films near the three-phase contact line. In the present study, the mesoscopic wetting behavior of nonvolatile polydimethylsiloxane (PDMS, 20 cSt) and a PDMS suspension containing PDMS-modified SiO2 nanoparticles at a concentration of 1 wt% is measured using a phase-shifting imaging ellipsometer (PSIE) developed in our previous study, although the aggregates of nanoparticles are formed in the suspension. The PSIE can measure the two-dimensional thickness profile of liquid thin films from nanometer to micrometer scales. From the measured thickness profiles, we investigate the effect of the suspended nanoparticles on the apparent contact angle θa and precursor film length Lp. The dependence of θa and Lp on the velocity of contact line U ranging from 10–8 to 10–6 m/s is characterized. The contact angles of both fluids reflect the Cox–Voinov law, i.e., the θa is proportional to U1/3. At the same U, the θa of the suspension is higher than that of pure PDMS. The Lp of both fluids is proportional to U−1 and is the same at the same U. Furthermore, the Lp and thickness profiles at the nanometer scale are consistent with adiabatic precursor film theory. [ABSTRACT FROM AUTHOR]

The processing and interpretation of microseismic (MS) signals are the basis for obtaining source information of MS events in rock engineering. However, MS signal processing is subjective and time-consuming, in which different strategies have to be empirically adopted to cope with different issues. To facilitate MS signal analysis, this study proposes an entirely data-driven method based on local mean decomposition (LMD), which has been applied in various signal processing fields. Through some numerical experiments, the superiorities (e.g., smaller reconstruction errors and fewer sifting iterations) of the proposed method over other methods are demonstrated, and the proposed method is identified as an effective filtering and frequency analysis tool to process recorded MS signals. Our analysis results show that the proposed method can effectively remove interfering noise existing in MS signals collected from the Lianghekou hydropower station during underground rock excavation. Moreover, the proposed method can be employed to analyze the frequency revolution features of MS signals before and after rock mass failure. In addition, it is found that MS signal frequency migrates to a low-frequency range when approaching rock failure, which can be regarded as an early warning indicator of rock instability. The proposed method is believed to facilitate MS monitoring and offer a new clue to conduct MS signal processing and interpretation. [ABSTRACT FROM AUTHOR]

We analyze the accumulated experience of application of the technologies of utilization of the wastes of machine-building and metallurgy. It is shown that the most promising directions of utilization of wastes are connected with the development of technologies combining the procedures of recycling with the processes of formation of the functional coatings intended for the restoration and hardening of machine parts. We also indicate the efficiency of the procedure of electroerosion dispersion of the wastes of conducting materials used to obtain nano- and micro-sized powders suitable for the deposition of functional coatings. It is shown that the electric-contact welding of the wastes of tool-making industry enables us to obtain functional coatings as a result of combination of the processes of recycling, hardening, and restoration of machine parts without their melting. We also propose the technology of hardening of the plane surfaces of working elements of the agricultural equipment by electric-contact welding of the wastes of tool steel, which makes it possible to make their service life 1.8–2.0 times longer as compared with the service life of new commercially produced working tools. [ABSTRACT FROM AUTHOR]

Clostridium acetobutylicum DSM 792 has been characterized as a native acetone-butanol-ethanol (ABE) fermenting bacterium. C. acetobutylicm is used as a model organism to improve solvent production by metabolic engineering strategies. In this study, the ABE pathway in C. acetobutylicum was modified by overexpressing the pyruvate decarboxylase gene from Zymomonas mobilis under the control of 2 promoters, namely Pferr and Ppta (promoters of genes encoding pyruvate ferredoxin oxidoreductase and phosphotransacetylase) and the recombinant strains were designated as DSM 792(pPDC1) and DSM 792(pPDC2). The specific activity of the alcohol dehydrogenases in the pdc-expressing strains was higher than that in the wild-type strain. In batch fermentation experiments, the total ABE production from the recombinant strains DSM 792 (pPDC1) and DSM 792 (pPDC2) was 1.57 and 7.9 g/L, respectively, which was higher when compared to the wild type ABE production (0.64 g/L) under uncontrolled pH. The alcohol to acetone ratio (BE/A) was found to be 2.28, 2.77, and 5.26 in wild type, DSM 792 (pPDC1), and DSM 792 (pPDC2), respectively. This suggests that the re-routing of pyruvate by overexpression of the pdc could play a role in the generation of more reducing equivalents towards ethanol and butanol production. [ABSTRACT FROM AUTHOR]

Mass customization (MC) is recognized as a new manufacturing paradigm for market competition, and several empirical studies have discussed elements of organizational structure that are conducive to MC. A previous study proposed a model of quality at source and identified cultural values that can be changed by cultural artifacts, such as the stop-the-line system and the Andon system. This paper explores the impacts of three groups of MC enabler variables (personalized design, modular design, and automated processes) on two internal dimensions of organizational culture (work cooperation and relationship harmony).This paper surveyed 160 samples across five manufacturing industries. The results confirm that the three MC enablers (personalized design, modular design, and automated processes) correlate positively with two internal dimensions of the organizational culture of Chinese manufacturers (work cooperation and relationship harmony). Of the two internal dimensions, work cooperation enhances organizational performance, while relationship harmony has no impact on organizational performance. Modular design, automated processes, and work cooperation are powerful tools in the implementation of MC and organizational performance. Personalized design and relationship harmony are useful in MC. However, these tools are intermediate and limited, and they do not contribute to the general performance of the entire organization. Thus, in terms of internal- and team-oriented cultural factors, work cooperation is more important than relationship harmony in the implementation of MC. Relationship harmony is a tool for managing work atmosphere, and this is a new characteristic feature of MC as a new operational paradigm. [ABSTRACT FROM AUTHOR]

A feature line, which is a narrow and long curved surface on a smooth panel, is an essential element of the exterior style of the car. However, it is difficult to make the feature lines accurately, especially with a small radius and a large angle, in a typical automotive panel stamping process. A simple stamping process to find the minimum formable radius of the subtle feature line for automotive outer panel manufacturing was employed. This study presented three methods to find the minimum formable radius of the feature line; analytical approach, finite element analysis, and experiments. The three approaches showed similar and consistent results. In the experiment, the minimum formable radius of the subtle feature line was 20 mm, 8 mm, 7 mm, and 4 mm, when the punch angles of 175 °, 170 °, 165 °, and 160 °, respectively. This study showed that no matter how well-engineered dies and punches, there are limits to stamping the subtle feature line in automotive manufacturing. The results of this study could be used in the design process and the die engineering process of automotive panel making. [ABSTRACT FROM AUTHOR]

Ethyl-acetate is important for the flavor and aroma of the alcoholic beverages, therefore, there have been extensive efforts toward increasing its production by engineering yeast strains. In this study, we reported a new approach to breed non-genetic modified producing yeast strain with higher ethyl-acetate production for beer brewing. First, we demonstrated the positive effect of higher acetic acid concentration on inducing the expression of acetyl-CoA synthetase (ACS). Then, we applied adaptive laboratory evolution method to evolve strain with higher expression level of ACS. As a result, we obtained several evolved strains with increased ACS expression level as well as ethyl-acetate production. In 3 L scale fermentation, the optimal strain EA60 synthesized more ethyl-acetate than M14 at the same time point. At the end of fermentation, the ethyl-acetate production in EA60 was 21.4% higher than M14, while the other flavor components except for acetic acid were changed in a moderate degree, indicating this strain had a bright prospect in industrial application. Moreover, this study also indicated that ACS1 played a more important role in increasing the acetic acid tolerance of yeast, while ACS2 contributed to the synthesis of cytosol acetyl-CoA, thereby facilitating the production of ethyl-acetate during fermentation. [ABSTRACT FROM AUTHOR]

The history of the formation and development of a new field of research in chemical technology, such as the theory of chemical process systems engineering, including methods for the analysis, optimization, and synthesis of chemical process systems (CPSs), is briefly surveyed. A classification of the modern engineering main types is given. Methods and techniques for chemical processes (CPs) and chemical process systems intensification and methods for digitalized physical and chemical engineering, the computer simulation of the texture of nanocomposites, and computer-aided chemical diagnostics in materials science are described. Methods and algorithms are given for the analysis of fractal and statistical characteristics of unsteady gas flows in complex gas pipelines. Methods and techniques for energy conservation in chemical process systems are briefly described; the basic concepts of resource and energy-saving logistics are outlined. A brief description of the principles of the computer-aided synthesis of optimal energy- and resource-efficient chemical process systems is given. The essence of digital transformation and automated management of the operation of production facilities of the chemical, petrochemical, and fuel and energy complexes is briefly outlined. The essence of the multilevel training of chemical engineers and technologists in energy-saving environmentally safe chemical process systems engineering is stated. The main topical priority fields of research in energy- and resource-efficient environmentally safe chemical process systems engineering are proposed. [ABSTRACT FROM AUTHOR]

To meet lightweight demands, complex support or near-net shaped structures are increasingly applied in modern industry sectors. Wire arc additive manufacturing (WAAM) of these structures requires a profound knowledge of the interactions between material properties, welding process and mechatronic engineering. This study shows a new WAAM strategy, termed skeleton arc additive manufacturing (SAAM), adapted especially for building freeform wire-structured aluminium component, and further contributes a bead modelling study to establish welding parameters, geometric features and material properties relationship. The results show that good geometrical features and sound metallurgical properties can be achieved for wire-structure aluminium parts using the proposed approach. The strut diameter gradually increased with the amplified welding arc-on time and wire feed speed; however, they present significant changes in the microstructural evolution and mechanical strength. A sample demonstration further displays an effective path to create typically wire-structure part with good quality and offers the main processing challenge and coping strategy during SAAM. [ABSTRACT FROM AUTHOR]

Objective: Experimental procedures have been used to monitor cellular responses at the dentin/pulp interface. Aiming to divert from in vivo studies and oversimplified two-dimensional assays, three-dimensional (3D) models have been developed. This review provides an overview of existing literature, regarding 3D in vitro dentin/pulp reconstruction. Material & Methods: PubMed, Scopus, Cochrane Library and Web of Science- were systematically searched for attributes between 1998 and 2020. The search focused on articles on the development of three-dimensional tools for the reconstruction of a dentin/pulp complex under in vitro conditions, which were then screened and qualitatively assessed. Article grouping according to mode of implementation, resulted in five categories: the customised cell perfusion chamber (CPC) (n = 8), the tooth bud model (TBM) (n = 3), the 3D dentin/pulp complex manufactured by tissue engineering (DPC) (n = 6), the entire tooth culture (ETC) (n = 4) and the tooth slice culture model (TSC) (n = 5). Results: A total of 26 publications, applying nine and eight substances for pulp and dentin representation respectively, were included. Natural materials and dentin components were the most widely utilized. The most diverse category was the DPC, while the CPC group was the test with the highest longevity. The most consistent categories were the ETC and TSC models, while the TBM presented as the most complete de novo approach. Conclusions: All studies presented with experimental protocols with potential upgrades. Solving the limitations of each category will provide a complete in vitro testing and monitoring tool of dental responses to exogenous inputs. Clinical Relevance: The 3D dentin/pulp complexes are valid supplementary tools for in vivo studies and clinical testing. [ABSTRACT FROM AUTHOR]

In the early 2000s, shipyards in South Korea were the most competitive in the world for commercial vessels because of their accumulated production and quality management capabilities. In the mid-2000s, the rising trend of international oil prices and predicted market growth of offshore structures caused large shipyards in South Korea to start focusing on the construction of offshore structures. However, these shipyards suffered massive losses because they did not properly reflect the characteristics of offshore structures, which differ from those of commercial vessels. Such losses can largely be classified into engineering losses and production losses. This study aimed to address problems related to outfitting installation work from a production perspective. An algorithm was developed to define an efficient spool installation sequence and was verified through actual data of offshore structure. The developed algorithm should make it possible to plan spool installation work by determining an efficient spool installation sequence. In addition, the algorithm can assist in prompt decision making and eliminate wasted cost and time by providing with an efficient spool installation sequence. [ABSTRACT FROM AUTHOR]

Implementing machine learning in an enterprise involves tackling a wide range of complexities with respect to requirements elicitation, design, development, and deployment of such solutions. Despite the necessity and relevance of requirements engineering approaches to the process, not much research has been done in this area. This paper employs a case study method to evaluate the expressiveness and usefulness of GR4ML, a conceptual modeling framework for requirements elicitation, design, and development of machine learning solutions. Our results confirm that the framework includes an adequate set of concepts for expressing machine learning requirements and solution design. The case study also demonstrates that the framework can be useful in machine learning projects by revealing new requirements that would have been missed without using the framework, as well as, by facilitating communication among project team members of different roles and backgrounds. Feedback from study participants and areas of improvement to the framework are also discussed. [ABSTRACT FROM AUTHOR]

Purpose: Traditionally, α-lactose monohydrate is the carrier of choice in dry powder inhaler (DPI) formulations. Nonetheless, other sugars, such as D-mannitol, have emerged as potential alternatives. Herein, we explored different particle engineering processes to produce D-mannitol carriers for inhaled delivery. Methods: Wet-sieving and spray-congealing were employed as innovative techniques to evaluate the impact of engineering on the particle properties of D-mannitol. To that end, the resulting powders were characterized concerning their solid-state, micromeritics and flowability. Afterwards, the engineered carrier particles were blended with inhalable size beclomethasone dipropionate to form low dose (1 wt%) DPI formulations. The in vitro aerosolization performance was evaluated using the NEXThaler®, a reservoir multi-dose device. Results: Wet-sieving generated D-mannitol particles with a narrow particle size distribution and spray-congealing free-flowing spherical particles. The more uniform pumice particles with deep voids and clefts of wet-sieved D-mannitol (Pearl300_WS) were beneficial to drug aerosolization, only when used in combination with a ternary agent (10 wt% of 'Preblend'). When compared to the starting material, the spray-congealed D-mannitol has shown to be promising in terms of the relative increase of the fine particle fraction of the drug (around 100%), when used without the addition of ternary agents. Conclusions: The wet-sieving process and the related aerosolization performance are strongly dependent on the topography and structure of the starting material. Spray-congealing, has shown to be a potential process for generating smooth spherical particles of D-mannitol that enhance the in vitro aerosolization performance in binary blends of the carrier with a low drug dose. [ABSTRACT FROM AUTHOR]