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Imaging the internal architecture of fast-vibrating structures at micrometer scale and kilohertz frequencies poses great challenges for numerous applications, including the study of biological oscillators, mechanical testing of materials, and process engineering. Over the past decade, X-ray microtomography with retrospective gating has shown very promising advances in meeting these challenges. However, breakthroughs are still expected in acquisition and reconstruction procedures to keep improving the spatiotemporal resolution, and study the mechanics of fast-vibrating multiscale structures. Thereby, this works aims to improve this imaging technique by minimising streaking and motion blur artefacts through the optimisation of experimental parameters. For that purpose, we have coupled a numerical approach relying on tomography simulation with vibrating particles with known and ideal 3D geometry (micro-spheres or fibres) with experimental campaigns. These were carried out on soft composites, imaged in synchrotron X-ray beamlines while oscillating up to 400 Hz, thanks to a custom-developed vibromechanical device. This approach yields homogeneous angular sampling of projections and gives reliable predictions of image quality degradation due to motion blur. By overcoming several technical and scientific barriers limiting the feasibility and reproducibility of such investigations, we provide guidelines to enhance gated-CT 4D imaging for the analysis of heterogeneous, high-frequency oscillating materials. [ABSTRACT FROM AUTHOR]

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]

Plastic waste is an environmental challenge, but also presents a biotechnological opportunity as a unique carbon substrate. With modern biotechnological tools, it is possible to enable both recycling and upcycling. To realize a plastics bioeconomy, significant intrinsic barriers must be overcome using a combination of enzyme, strain, and process engineering. This article highlights advances, challenges, and opportunities for a variety of common plastics. The way we generate and treat plastic waste is a key consideration in the transition from a take-make-waste model to a truly circular economy. Here, authors discuss their views on how biotechnology could contribute to solving the plastic problem, and the barriers we need to overcome to make such approaches a reality. [ABSTRACT FROM AUTHOR]

Chemical leaching of coals would be required to produce cleaner coals for some special applications where physical beneficiation may not be effective enough. This would also help in recovering Li and rare earth metals besides in the sequestration of CO2. About 20 Indian coals having complexly distributed moderate to high ash contents were sequentially treated with various alkali–acid such as NaOH-HCl, HF, HCl, HCl-HF, and NaOH-HCl-HF leaching. This aimed to establish and design the best stepwise sequential process for the highest degree of demineralisation through a chemical leaching process. Kinetics and process intensification studies were carried out. More than 80% demineralisation of Madhaipur and Neemcha coals was observed using the best sequential treatment designed presently. The repeated stepwise treatment of the alkali and the acid was also studied, which was found to significantly enhance the degree of demineralisation of coals. The integrated process of alkali–acid leaching followed by solvent extraction (Organo-refining) and vice versa of the treated coal was also studied for producing cleaner coals. [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]

Blue denim, a billion-dollar industry, is currently dyed with indigo in an unsustainable process requiring harsh reducing and alkaline chemicals. Forming indigo directly in the yarn through indican (indoxyl-β-glucoside) is a promising alternative route with mild conditions. Indican eliminates the requirement for reducing agent while still ending as indigo, the only known molecule yielding the unique hue of blue denim. However, a bulk source of indican is missing. Here, we employ enzyme and process engineering guided by techno-economic analyses to develop an economically viable drop-in indican synthesis technology. Rational engineering of PtUGT1, a glycosyltransferase from the indigo plant, alleviated the severe substrate inactivation observed with the wildtype enzyme at the titers needed for bulk production. We further describe a mild, light-driven dyeing process. Finally, we conduct techno-economic, social sustainability, and comparative life-cycle assessments. These indicate that the presented technologies have the potential to significantly reduce environmental impacts from blue denim dyeing with only a modest cost increase. Conventional blue denim dyeing has both environmental and health-related consequences. Here, Bidart et al. use enzyme engineering to develop a viable method for the bulk production of indican and demonstrate dying processes which could significantly reduce the negative consequences of this billion-dollar industry. [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]

This article discusses the role of hydrogen in the transition away from carbon-based energy sources. It highlights the importance of process systems engineering (PSE) in guiding the operation and control of the industrial metabolism during this transition. The article emphasizes the need for engineering solutions that consider factors such as energy efficiency, storage alternatives, and the transportability and safety of hydrogen as an energy carrier. It also explores the potential of hydrogen in various sectors, such as oil refining, ammonia synthesis, and transportation. The article concludes by discussing the challenges and opportunities associated with the energy transition and the importance of technology-driven policies in evaluating energy efficiency and affordability. [Extracted from the article]

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]

Binder jet 3D printing (BJ3DP) can manufacture large-scale complex and customizable metal parts with low cost and high efficiency, showing extensive application prospect in the field of manufacturing engineering. However, very few studies have focused on optimizing printing parameters to achieve near-full densification BJ3DP 316L stainless steel (SS) parts. Therefore, the printing-related parameters, i.e., layer thickness, roller traverse speed, and binder concentration, were optimized in the present study. The dimensional quality of green samples revealed that the layer thickness of 125 μm, binder concentration of 60%, and roller traverse speed of 20 pps were the optimal parameters for printing green samples. Following sintering, the BJ3DP 316L SS samples printed by the optimal parameters exhibited a relative density of ~ 99.5%, along with X-/Y-/Z-direction linear shrinkages of ~ 18.7, ~ 17.4, and ~ 19.8%, respectively, in good agreement with those of the simulation results. Z-direction showed the highest value of linear shrinkage due to the presence of gravity during sintering. Meanwhile, the sintered 316L SS samples printed by the optimal parameters possess Vickers hardness of ~ 192 HV, which exceeds the values of the BJ3DP-produced 316L SS samples reported in the available literature, arising from their high relative density and the presence of ~ 6.9 vol.% δ-ferrite phase. [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]

Oleaginous microalgae can produce triacylglycerol (TAG) under stress, yet the underlying mechanism remains largely unknown. Here, we show that, in Nannochloropsis oceanica, a bZIP-family regulator NobZIP77 represses the transcription of a type-2 diacylgycerol acyltransferase encoding gene NoDGAT2B under nitrogen-repletion (N+), while nitrogen-depletion (N−) relieves such inhibition and activates NoDGAT2B expression and synthesis of TAG preferably from C16:1. Intriguingly, NobZIP77 is a sensor of blue light (BL), which reduces binding of NobZIP77 to the NoDGAT2B-promoter, unleashes NoDGAT2B and elevates TAG under N−. Under N+ and white light, NobZIP77 knockout fully preserves cell growth rate and nearly triples TAG productivity. Moreover, exposing the NobZIP77-knockout line to BL under N− can double the peak productivity of TAG. These results underscore the potential of coupling light quality to oil synthesis in feedstock or bioprocess development. Microalgae are promising feedstock for oil production. The authors report that a transcription factor NobZIP77 can regulate oil synthesis by sensing the blue light, and explore these findings to greatly enhance oil productivity via genetic and process engineering in Nannochloropsis oceanica. [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]

Multiphase flow modeling is crucial for the oil and gas industry, enabling safer and more cost-effective subsea well development. In the past 60 years, there have been significant advancements in this field, addressing complex challenges in petroleum engineering. Different topics have been developed over time, some documented in literature while others require further research. The key question is the current level of development and capabilities of advanced simulators based on multiphase flow models. This paper classifies multiphase flow models and methods, including transient mechanistic models. In petroleum engineering, multiphase simulations utilize two primary methods: steady-state and unsteady-state (transient) methods. Steady-state methods are categorized into correlations and mechanistic models, depending on the formulation of the flow equation. Meanwhile, transient methods in the system code can be further classified into empirical slug specification models, slug-capturing models, and mechanistic models. This study also discusses the applications in oil engineering and lists applications in oil production engineering, specifically subsurface and surface systems. It covers steady-state and dynamic multiphase flow simulators in the oil industry, highlighting their continuous development for better simulations. The paper compares dynamic and steady-state multiphase flow simulators, discussing their applications and differences. The authors designed and constructed the tables, providing recommendations for unexplored research areas. The study concludes that there has been development in multiphase flow models and simulators, but there are still areas requiring further research. [ABSTRACT FROM AUTHOR]

Ranking aggregation is an ancient problem with some characteristic elements: a number of experts, who individually rank a set of objects according to a certain (subjective) attribute, and the need to aggregate the resulting expert rankings into a collective judgment. Although this problem is traditionally very popular in fields such as social choice, psychometrics, and economics, it can also have several interesting applications in manufacturing, e.g., for customer-oriented design, reliability engineering, production management, etc. Through a case study related to a cobot-assisted manual (dis)assembly, the paper illustrates an operational methodology and various useful tools that assist in tackling the problem practically, effectively, and with a critical mind. Some of the proposed tools allow to estimate the degree of concordance among experts, and the collective judgment’s consistency and robustness. The paper is aimed at scientists and practitioners in manufacturing. [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]

Here we introduce the Galaxy-SynBioCAD portal, a toolshed for synthetic biology, metabolic engineering, and industrial biotechnology. The tools and workflows currently shared on the portal enables one to build libraries of strains producing desired chemical targets covering an end-to-end metabolic pathway design and engineering process from the selection of strains and targets, the design of DNA parts to be assembled, to the generation of scripts driving liquid handlers for plasmid assembly and strain transformations. Standard formats like SBML and SBOL are used throughout to enforce the compatibility of the tools. In a study carried out at four different sites, we illustrate the link between pathway design and engineering with the building of a library of E. coli lycopene-producing strains. We also benchmark our workflows on literature and expert validated pathways. Overall, we find an 83% success rate in retrieving the validated pathways among the top 10 pathways generated by the workflows. Automated design and build processes can rapidly accelerate work in synthetic biology and metabolic engineering. Here the authors present Galaxy-SynBioCAD, a toolshed for synthetic biology, metabolic engineering, and industrial biotechnology that they use to build and execute Galaxy scientific workflows from pathway design to strain engineering through the automated generation of scripts driving robotic workstations. [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]

Titanium-based alloy products manufactured by Selective Laser Melting (SLM) have been widely used in biomedical applications, owing to their high biocompatibility, significantly good mechanical properties. In order to improve the Ti–6Al–4V SLM-fabricated part quality and help the manufacturing engineers choose optimal process parameters, an optimization methodology based on an artificial neural network was developed to relate four key process parameters (laser power, laser scanning speed, layer thickness, and hatch distance) and two target properties of a part fabricated by the SLM technique (density ratio and surface roughness). A supervised learning deep neural network based on the backpropagation algorithm was applied to optimize input parameters for a given set of quality part outputs. Several methods were utilized to solve undesired problems occurring during neural network training to increase the model accuracy. The model's performance was proven with a value of R2 of 99% for both density ratio and surface roughness. A selection system was then built, allowing users to choose the optimal process parameters for fabricated products whose properties meet a specific user requirement. Experiments performed with the optimal process parameters recommended by the optimization system strongly confirmed its reliability by providing the ultimate part qualities nearly identical to those defined by the user with only about 0.9–4.4% of errors at the maximum. Finally, a graphical user interface was developed to facilitate the choice of the optimum process parameters for the desired density ratio and surface roughness. [ABSTRACT FROM AUTHOR]

Engineering and manufacturing processes and systems designs involve many challenges, such as dynamism, chaotic behaviours, and complexity. Of late, the arrival of big data, high computational speed, cloud computing and artificial intelligence techniques (like machine learning and deep learning) has reformed how many engineering and manufacturing professionals approach their work. These technologies offer thrilling innovative ways for engineers and manufacturers to tackle real-life challenges. On the other hand, the field of Artificial Intelligence (AI) is extensive. Several diverse theories, algorithms, and methods are available, which presents a challenge and a barrier in choosing the right AI technique for the appropriate engineering process or manufacturing process and environments. Besides, the pertinent literature is disseminated over various journals, conference proceedings, and research communities. Hence, conducting a systematic survey to scrutinise and classify the existing literature is worthwhile. However, it is challenging, but previous review studies have not adequately addressed AI's use and advancement in engineering and manufacturing (EM). Besides, some concentrated on single AI models, and others focused on a specific area in EM. This paper presents a comprehensive systematic review of studies on AI and its application in EM. To limit the scope of the current study, we conducted a keyword search in official publisher websites and academic databases, such as Springer, Elsevier, Scopus, Science Publication, Taylor & Francis, Directory of Open Access Journals (DOAJ), Association for Computing Machinery (ACM), Wiley online library, Inderscience and Google scholar. The search results (173 articles) were filtered according to a proposed framework, which resulted in ninety-one (91) relevant research articles. We reviewed the articles based on a proposed taxonomy (the year of publication, the AI algorithm and machine learning task adopted, the application area in EM, the train and test split of data, the error, and accuracy metrics used, the potential benefits). Our assessment using the proposed taxonomy gave a helpful insight into the literature's anatomy on various AI applications in engineering and manufacturing. Also, we identified opportunities for future research in AI application in the field of EM. [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]

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]

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]

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]

Advanced analytics are fundamental to transform large manufacturing data into resourceful knowledge for various purposes. In its very nature, such "industrial big data" can relay its usefulness to reach further utilitarian applications. In this context, Machine Learning (ML) is among the major predictive modeling approaches that can enable manufacturing researchers and practitioners to improve the product quality and achieve resource efficiency by exploiting large amounts of data (which is collected during manufacturing process). However, disposing ML algorithms is a challenging task for manufacturing industrial actors due to the prior specification of one or more algorithms hyperparameters (HPs) and their values. Moreover, manufacturing industrial actors often lack the technical expertise to apply advanced analytics. Consequently, it necessitates frequent consultations with data scientists; but such collaborations tends to cost the delays, which can generate the risks such as human-resource bottlenecks. As the complexity of these tasks increases, so does the demand for support solutions. In response, the field of automated ML (AutoML) is a data mining-based formalism that aims to reduce human effort and speedup the development cycle through automation. In this regard, existing approaches include evolutionary algorithms, Bayesian optimization, and reinforcement learning. These approaches mainly focus on providing the user assistance by automating the partial or entire data analysis process, but they provide very limited details concerning their impact on the analysis. The major goal of these conventional approaches has been generally focused on the performance factors, while the other important and even crucial aspects such as computational complexity are rather omitted. Therefore, in this paper, we present a novel meta-learning based approach to automate ML predictive models built over the industrial big data. The approach is leveraged with development of, AMLBID, an Automated ML tool for Big Industrial Data analyses. It attempts to support the manufacturing engineers and researchers who presumably have meager skills to carry out the advanced analytics. The empirical results show that AMLBID surpasses the state-of-the-art approaches and could retrieve the usefulness of large manufacturing data to prosper the research in manufacturing domain and improve the use of predictive models instead of precluding their outcomes. [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]

Saccharomyces cerevisiae is widely employed as a cell factory for the production of biofuels. However, product toxicity has hindered improvements in biofuel production. Here, we engineer the actin cytoskeleton in S. cerevisiae to increase both the cell growth and production of n-butanol and medium-chain fatty acids. Actin cable tortuosity is regulated using an n-butanol responsive promoter-based autonomous bidirectional signal conditioner in S. cerevisiae. The budding index is increased by 14.0%, resulting in the highest n-butanol titer of 1674.3 mg L−1. Moreover, actin patch density is fine-tuned using a medium-chain fatty acid responsive promoter-based autonomous bidirectional signal conditioner. The intracellular pH is stabilized at 6.4, yielding the highest medium-chain fatty acids titer of 692.3 mg L−1 in yeast extract peptone dextrose medium. Engineering the actin cytoskeleton in S. cerevisiae can efficiently alleviate biofuels toxicity and enhance biofuels production. Product toxicity is one of the factors that hinder biofuel production. Here, the authors engineer the actin cytoskeleton to increase cell growth and production of n-butanol and medium-chain fatty acids in Saccharomyces cerevisiae. [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]

This document is a correction notice for an article titled "Evaluation of physical parameters and spectral characterization of the quality of soaps containing by-products from the food industry." The original article was published in Scientific Reports and can be found online. The correction addresses the omission of the Funding section in the original version of the article. The corrected version now includes information about the funding received from the Ministry of Education and Science in Poland. The authors of the article are Patrycja Łusiak, Renata Różyło, Jacek Mazur, Paweł Sobczak, and Arkadiusz Matwijczuk. [Extracted from the article]

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]

We examine whether the probability of innovating a company's business model towards the Industry 4.0 paradigm is affected by external institutional support and family leadership. Industry 4.0 is the information-intensive transformation of global manufacturing enabled by Internet technologies aimed at reinventing products and services from design and engineering to manufacturing. Using a sample of 3000 firms from a corporate survey on the manufacturing industry in Italy, our results showed that family leadership has a significant positive influence on the adoption of Industry 4.0 business models, but only in terms of family ownership. By contrast, family management has a negative influence on the probability of adopting a new business model. However, this negative influence is almost totally offset by the presence of the Triple Helix, i.e. the external support by public institutions and universities, which counterbalances the lower propensity of family managers to adopt Industry 4.0 business models. This supporting role only occurs when institutions and universities act together. [ABSTRACT FROM AUTHOR]

The purposes of this research are to analyse studies which include "Engineering Design Process" by using meta-synthesis method and to present the type of tendency and differences of the processes used in the current literature. With this purpose, 46 research articles were selected and analysed. Qualitative data were collected under three categories based on the literature and the selected 46 articles. The results demonstrate that researchers mostly use open-ended questions and problem scenarios while presenting a design brief and use different teaching methods—mostly brainstorms, laboratory activities, and writing activities—while utilizing an engineering design process. And also while creating design artefacts, researchers use four different steps, namely, drawing, make, test, redesign. It is believed that this research will guide subsequent researchers and science teachers in planning and implementing engineering design process in K-12 classrooms. [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]

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]

In this study, polyamide 66 (PA 66) filaments were prepared for fused filament fabrication (FFF). The effects of the process parameters on the mechanical properties, initial microstructures, dynamic mechanical behavior, and crystallinity of the samples were investigated. The samples obtained at high processing temperatures exhibited high crystallinity, high tensile strength, and low porosity. Almost fully dense samples with excellent mechanical properties were obtained under optimal conditions. The tensile strength of the samples improved by 29.5% (from 68.07 to 88.17 MPa) with an increase in the nozzle temperature from 270 to 290 °C. The elongation at break abruptly increased (from 2.38 to 13.17%), because of the plastic behavior of the material and strain hardening. X-ray diffraction results demonstrated that the crystallinity of PA 66, significantly improved (from 47.3 to 65.6%). In addition, the dynamic mechanical performance of the samples was significantly related to the raster angle. The samples fabricated at a raster angle of 0° exhibited the best dynamic mechanical properties, followed by the 45° and 90° samples. The successful fabrication of PA 66 samples demonstrates the potential use of PA 66 for producing parts using FFF, and provides options for utilizing materials with improved performance for additive manufacturing applications in engineering. [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]