Hybrid Tool Research Articles

Unscented Kalman Filtering for Prognostics Under Varying Operational and Environmental Conditions

Prognostics gained a lot of research attention over the last decade, not the least due to the rise of data-driven prediction models. Also hybrid approaches are being developed that combine physics-based and data-driven models for better performance. However, limited attention is given to prognostics for varying operational and environmental conditions. In fact, varying operational and environmental conditions can significantly influence the remaining useful life of assets. A powerful hybrid tool for prognostics is Bayesian filtering, where a physical degradation model is updated based on realtime data. Although these types of filters are widely studied for prognostics, application for assets in varying conditions is rarely considered in literature. In this paper, it is proposed to apply an unscented Kalman filter for prognostics under varying operational conditions. Four scenarios are described in which a distinction is made between the level in which real-time and future loads are known and between short-term and long-term prognostics. The method is demonstrated on an artificial crack growth case study with frequently changing stress ranges in two different stress profiles. After this specific case, the generic application of the method is discussed. A positioning diagram is presented, indicating in which situations the proposed filter is useful and feasible. It is demonstrated that incorporation of physical knowledge can lead to highly accurate prognostics due to a degradation model in which uncertainty in model parameters is reduced. It is also demonstrated that in case of limited physical knowledge, data can compensate for missing physics to yield reasonable predictions.

Influence of hybrid pin profile on microstructural and mechanical properties of Al/SiC graded composites produced by friction stir processing

Friction stir processing (FSP) was employed to fabricate a surface graded composite by embedding SiC reinforcement particles in an AA6082-T6 matrix. Conical blind holes were drilled on the surface of the plate with varying inter-hole distances. The processing was performed with the different number of passes by keeping rotational and traverse speed constant. A new hybrid tool with a combination of conventional conical threaded tool and the triangular cross-sectioned tool was used in processing. The microstructural features of the processed samples were examined by a 3D microscope and scanning electron microscope (SEM). Mechanical properties such as microhardness, tensile strength was thoroughly evaluated. It is reported that the number of passes played an essential role in the distribution of reinforcement particles and grain refinement. The hardness value improved by applying multiple passes. The fractured tensile samples showed ductile failure. The sample treated with double passes gave better results with the homogenous distribution of reinforcement particles compared to samples processed with a single pass.

Hybrid tools of generating unit simulation for proper tuning of automatic voltage regulators: concept, development and validation

Hybrid tools of generating unit simulation for proper tuning of automatic voltage regulators: concept, development and validation

Assessing current concerns and goals idiographically: A review of the Motivational Structure Questionnaire family of instruments.

There have been two kinds of methods for assessing individuals' motivation and their goal-striving behavior. The idiographic method obtains respondents' individual descriptions of their behavior or inner experiences. The nomothetic approach uses a standardized questionnaire in which respondents select from a set of alternatives. Idiographic responses provide rich, individualized information, but they make comparisons across different individuals difficult. By contrast, the nomothetic approach loses valuable individualized information, but it readily lends itself to cross-individual comparisons. The present authors have developed a family of motivational assessment instruments within the framework of the Goal Theory of Current Concerns and individuals' goal pursuits. Each of these instruments is a hybrid version of the idiographic and nomothetic methods. Each one obtains individualized information about each respondent at the start of the assessment, but it then utilizes rating scales that allow comparisons across different individuals to be made. The objective of the present article is to present this family of hybridized instruments for potential use in routine outcome monitoring. The method used in this article was to review the development of this family of hybrid assessments instruments over the preceding decades and the research on their psychometric properties and clinical applications. These hybrid tools include the Interview Questionnaire, Work Concerns Inventory, Motivational Structure Questionnaire, and Personal Concerns Inventory and their variants. The review includes only the idiographic-nomothetic approaches that are based on the Goal Theory of Current Concerns. The review reveals that for each instrument, motivational indices are calculated, which have been shown to be valid and reliable. Analyses have also revealed adaptive and maladaptive motivational factors. The measures discussed here have proven useful in clinical applications, when, for example, they are used as components of Systematic Motivational Counseling and the Life Enhancement and Advancement Programme for helping individuals improve their motivational structure. Similarly, the measures hold promise for use in routine outcome monitoring.

Improvement of Mechanical and Corrosion Properties of Al/SiC Functionally Graded Material Using a Novel Hybrid Tool in Friction Stir Processing

Improvement of Mechanical and Corrosion Properties of Al/SiC Functionally Graded Material Using a Novel Hybrid Tool in Friction Stir Processing

Electronic search programs are effective in identifying patients with minimal trauma fractures.

Most patients who suffer a minimal trauma fracture remain undiagnosed, placing them at high risk of refracture. Case finding can be improved by electronic search tools that screen medical records for documented fractures. Here, we assessed the efficacy of two new programs, AES and XRAIT, in identifying patients with minimal trauma fracture. Each tool was applied to search the electronic medical record and/or radiology reports at two tertiary hospitals in Sydney, Australia, from 1 July to 31 December 2018. Samples of the extracted reports were then manually reviewed to determine the sensitivity of each program in detecting minimal trauma fractures. At the two centers, AES detected 872 and 1364 cases, whereas XRAIT identified 1414 and 2180 patients with fractures, respectively. The true positive rate for "any fracture" was similar for both instruments (77-88%). However, the ability to detect "minimal trauma fractures" differed between programs and centers (53-75% accuracy), with each tool identifying separate subsets of patients. Concordance between both tools was less than half of the combined total number of minimal trauma fractures (43-45%). Considering the total number of minimal trauma fractures detected by both tools combined, AES correctly identified 52-55% of cases while XRAIT identified 88-93% of cases. Both programs reliably identified patients with any fracture but missed individuals with minimal trauma fracture to different degrees. Hybrid tools combining the methodology of XRAIT and AES are likely to improve the identification of patients who require investigation and treatment for osteoporosis.

Attitudes towards Participation in a Passive Data Collection Experiment.

In this paper, we present the results of an exploratory study conducted in Hungary using a factorial design-based online survey to explore the willingness to participate in a future research project based on active and passive data collection via smartphones. Recently, the improvement of smart devices has enabled the collection of behavioural data on a previously unimaginable scale. However, the willingness to share this data is a key issue for the social sciences and often proves to be the biggest obstacle to conducting research. In this paper we use vignettes to test different (hypothetical) study settings that involve sensor data collection but differ in the organizer of the research, the purpose of the study and the type of collected data, the duration of data sharing, the number of incentives and the ability to suspend and review the collection of data. Besides the demographic profile of respondents, we also include behavioural and attitudinal variables to the models. Our results show that the content and context of the data collection significantly changes people’s willingness to participate, however their basic demographic characteristics (apart from age) and general level of trust seem to have no significant effect. This study is a first step in a larger project that involves the development of a complex smartphone-based research tool for hybrid (active and passive) data collection. The results presented in this paper help improve our experimental design to encourage participation by minimizing data sharing concerns and maximizing user participation and motivation.

Comparative transcriptome analysis reveals differential gene expression in sterile and fertile rubber tree varieties during flower bud differentiation

Plant male sterility (MS) is an important agronomic trait that provides an efficient tool for hybridization and heterosis utilization of crops. Based on phenotypic and cytological observations, our study performed a multi-comparison transcriptome analysis strategy on multiple sterile and fertile rubber tree varieties using RNA-seq. Compared with the male-fertile varieties, a total of 1590 differentially expressed genes (DEGs) were detected in male-sterile varieties, including 970 up-regulated and 620 down-regulated transcripts in sterile varieties. Key DEGs were further assessed focusing on anther development, microsporogenesis and plant hormone metabolism. Twenty DEGs were selected randomly to validate transcriptome data using quantitative real-time PCR (qRT-PCR). Eleven key genes were subjected to expression pattern analysis using qRT-PCR and fluorescence in situ hybridization. Among them, nine genes, i.e., A6, GAI1, ACA7, TKPR1, CYP704B1, XTH26, MS1, MS35 and MYB33, that regulate callose metabolism, pollen wall formation, tapetum and microspores development were identified as candidate male-sterile genes. These findings provide insights into the molecular mechanism of male sterility in rubber tree.

DigiGlyc: A hybrid tool for reactive scheduling in cell culture systems

Chinese hamster ovary (CHO) cell culture systems are the most widely used platform for the industrial production of monoclonal antibodies (mAbs). The optimisation of manufacturing conditions for these high-value products is largely conducted off-line with little or no monitoring of mAb quality in-process. Here, we propose DigiGlyc, a hybrid model of these systems that predicts the critical quality attribute of mAb galactosylation. Having shown that DigiGlyc describes a wide range of experimental data well, we demonstrate that it can be used for the design of reactive optimisation studies. This hybrid formulation offers considerable gains in computational speed compared to mechanistic models with no loss in fidelity and can therefore underpin future online control and optimisation studies.

Decision support tools, systems and indices for sustainable coastal planning and management: A review

Coasts worldwide are facing enormous challenges relating to extreme water levels, inundation and coastal erosion. These challenges need to be addressed with consideration given to the need for infrastructure such as for ports and other socio-economic developments, especially for coastal tourism. Choosing the optimal decision support tools (DSTs) for coastal vulnerability and resilience assessment is a major challenge for decision-makers and coastal planners. The robustness and flexibility of coastal decision-making can be improved by using effective DSTs, particularly for the management of coastal hazards. This study provides an overview of the construction and use of decision support systems (DSSs) as combinations of DSTs, such as the commonly used multi-criteria decision analysis (MCDA) methods and an artificial neural network (ANN), integrated with a geographical information system (GIS). The experience of many researchers is that the combination of MCDA techniques based on fuzzy logic, analytical hierarchy process (AHP) and weighted linear combination (WLC), with GIS, and possibly also incorporating ANN, provides decision-makers with a comprehensive tool for efficiently calculating decision support indices (DSIs). Hybrid tools are becoming more popular and relevant among experts due to their multiple functionalities that facilitate decision-making. An integration of DSTs in a DSS and further development of DSIs provides a path for the integration of quantitative and qualitative parameters into the decision-making process, and providing materials to be used in consultation processes. An integrated DSS is more likely to produce high-quality results for decision-makers, handle the uncertainty of analysis, and extend the long-term applicability of tools employed by coastal managers.

Natural evolutionary strategies for variational quantum computation

Natural evolutionary strategies (NES) are a family of gradient-free black-box optimization algorithms. This study illustrates their use for the optimization of randomly initialized parameterized quantum circuits (PQCs) in the region of vanishing gradients. We show that using the NES gradient estimator the exponential decrease in variance can be alleviated. We implement two specific approaches, the exponential and separable NES, for parameter optimization of PQCs and compare them against standard gradient descent. We apply them to two different problems of ground state energy estimation using variational quantum eigensolver and state preparation with circuits of varying depth and length. We also introduce batch optimization for circuits with larger depth to extend the use of ES to a larger number of parameters. We achieve accuracy comparable to state-of-the-art optimization techniques in all the above cases with a lower number of circuit evaluations. Our empirical results indicate that one can use NES as a hybrid tool in tandem with other gradient-based methods for optimization of deep quantum circuits in regions with vanishing gradients.

Hybrid tool servo diamond turning of multiscale optical surface based on spectral separation of tool path

Hybrid tool servo diamond turning of multiscale optical surface based on spectral separation of tool path

From Hybrids to New Scaffolds: The Latest Medicinal Chemistry Goals in Multi-target Directed Ligands for Alzheimer's Disease.

Alzheimer’s disease (AD) is a chronic, progressive, and fatal neurodegenerative disorder affecting cognition, behavior, and function, being one of the most common causes of mental deterioration in elderly people. Once thought as being just developed because of β amyloid depositions or neurofibrillary Tau tangles, during the last decades, numerous AD-related targets have been established, the multifactorial nature of AD became evident. In this context, the one drug-one target paradigm has resulted in being inefficient in facing AD and other disorders with complex etiology, opening the field for the emergence of the multitarget approach. In this review, we highlight the recent advances within this area, emphasizing in hybridization tools of well-known chemical scaffolds endowed with pharmacological properties concerning AD, such as curcumin-, resveratrol-, chromone- and indole-. We focus mainly on well established and incipient AD therapeutic targets, AChE, BuChE, MAOs, β-amyloid deposition, 5-HT4 and Serotonin transporter, with the aim to shed light about new insights in the AD multitarget therapy.

A New Approach to Optimize the Relative Clearance for Cylindrical Joints Manufactured by FDM 3D Printing Using a Hybrid Genetic Algorithm Artificial Neural Network and Rational Function

Nowadays, FDM technology permits obtaining functional prototypes or even end parts. The process parameters, such as layer thickness, building orientation, fill density, type of support, etc., have great influence on the quality, functionality and behavior of the obtained parts during their lifetime. In this paper, we present a study concerning the possibilities of obtaining certain values for clearance in revolute joints of non-assembly mechanisms manufactured by FDM 3D Printing. To ensure the functioning of the assembly, one must know the relationship between the imposed and measured clearances by taking into account the significant input data. One way is to use the automat learning method with an artificial neuronal network (ANN). The data necessary for the training, testing, and validation of ANN were experimentally obtained, using a complete L 27 Taguchi experimental plan. A total of 27 samples were printed with different values of the following parameters: the infill density, the imposed clearance between the shaft and the hole, and the layer thickness. ANN architecture corresponds to the Hecht–Kolmogorov theorem. Genetic algorithms (GA) were used for the optimization of the output. The Neural Network Toolbox from MATLAB was used for training the network and a hybrid tool genetic algorithm artificial neural network (GA-ANN) was used to minimize the value of the absolute relative clearance (arc). The minimum value of the absolute relative clearance established by GA-ANN was 0.0385788. This value was validated experimentally, with a relative difference of 4%. We also introduced a rational function to approximate the correlation between the input and output parameters. This function fulfills some frontier conditions resulted from practice. In addition, the function may be used to establish the designed clearance in order to obtain an imposed one.

Investment Leverage for Adaptive Reuse of Cultural Heritage

This article tracks the design of a panoptic toolkit of complementary financial (grant and endowment, tax, debt and equity) and non-financial (regulation, real estate, risk mitigation and performance, capacity building, impact metric and digital network) instruments, designed to leverage capital investment and engender collaborative partnerships, to encourage investment capital to flow to cultural heritage adaptive reuse activities. Cultural heritage activities encompass adaptive reuse and energy retrofit of built heritage structures, protecting natural eco-systems and enabling local community enterprise activities. These activities embody circular economy dimensions, that stimulate social, cultural, environmental and economic regeneration, within the global value chain. Many cultural heritage investments entail long-term time horizons, requiring patient investment strategies. Consideration of the financial landscape, with regard to capital investment leverage is as much about understanding the motivations of participants to engage in the capital markets, as about innovations in financial instruments to safeguard cultural heritage values. Individual financial instruments, within the toolkit, such as debt and equity tools, are not new and some have a long association within traditional capital markets. What is new, is a framework for the engagement of blended complementary instruments, pooled within diverse multidisciplinary collaborative social enterprise fund structures, to achieve intentional and measurable impact investment returns. Risk adjusted investment return metrics include the analysis of socio-cultural and environmental impact returns in unison with market based financial returns, including below market returns in some instances. A case study of a revolving social impact fund is provided to give a practical example of combined complementary hybrid financial instruments within a collaborative funding structure. The ultimate choice and design of blended and pooled hybrid tool combinations and hybrid fund structures will change from building to building, and community to community, but must always prioritize the need to protect people and ecosystems in parallel with saving vulnerable cultural heritage resources. The selection of tailored hybrid financial instruments, to enhance circular economy transitionary ambitions, must remain flexible within a long-term collaborative investment strategy. The key change in mindset, central to cultural heritage financial toolkit development, is the enablement of capital leverage investment strategies that prioritize people and the ecosystem over pure profit motivation.

Study of milling of low thickness thermoplastic carbon fiber composites in function of tool geometry and cutting conditions

The application of carbon fiber reinforced thermoplastic matrix composites (CFRTP) is constantly increasing in various industrial sectors due to their mechanical properties and advantages compared to thermoset matrix composite. Nevertheless, CFRTP machining generates a current problem due to the anisotropy of these materials, the difficulty of impregnation of the reinforcement in the matrix, and its low melting temperature. For this reason, the study of conventional operations such as milling to achieve geometries with a good surface quality and reduced cosmetic defects is a line of research of great interest. In this article, a comparison of five cutting tools with different geometries has been made in CFRTP milling. The surface quality and the formation of visual defects such as fiber pull-out have been evaluated for each tool and combination of cutting parameters. In this sense, 16.42 μm in terms of Rz is the minimum average value for the complete experiment obtained for a conventional tool (tool A). However, surface qualities in terms of Rz close to 20 μm and minimum cosmetic defects have been obtained with a hybrid tool (tool C) with −10° helix angle and 8 teeth with a combination of cutting parameters of 0.07 mm/tooth and 3000 rpm, being the most complete tool of the experiment.

Dynamic conformal cooling improves injection molding

To achieve a certain visual quality or acceptable surface appearance in injection-molded components, a higher mold surface temperature is needed. In order to achieve this, injection molds can be dynamically tempered by integrating an active heating and cooling process inside the mold halves. This heating and cooling of the mold halves becomes more efficient when the temperature change occurs closer to the mold surface. Complex channels that carry cold or hot liquids can be manufactured close to the mold surface by using the layer by layer principle of additive manufacturing. Laser powder bed fusion (L-PBF), as an additive manufacturing process, has special advantages; in particular, so-called hybrid tools can be manufactured. For example, complex tool inserts with conformal cooling channels can be additively built on simple, machined baseplates. This paper outlines the thermal simulation carried out to optimize the injection molding process by use of dynamic conformal cooling. Based on the results of this simulation, a mold with conformal cooling channels was designed and additively manufactured in maraging steel (1.2709) and then experimentally tested.

Improve durability and surface quality of additively manufactured molds using carbon fiber prepreg

AbstractHybrid tooling is an emerging concept introduced in the aerospace industries to reduce weight and cost of traditional tools. A hybrid tool features a skin, which provides desired surface quality, durability, and a low‐density substrate to reduce the weight of the mold. The big area additive manufacturing (BAAM) technology permits rapid production of thermoplastic polymer intensive large‐scale structures. The present study features a carbon fiber reinforced polyphenylene sulfide (CF‐PPS) substrate fabricated using Oak Ridge National Laboratory's BAAM system. Carbon fiber‐bismaleimide (CF‐BMI) prepreg skin was then bonded to the BAAM tool through high‐pressure autoclave molding. Process optimization was conducted to improve the bonding between CF‐PPS and CF‐BMI (transverse tensile strength increased from 0.54 to 4.8 MPa). Durability of the mold was demonstrated from fabricating seven (7) carbon fiber‐Huntsman epoxy hand lay‐up parts utilizing the mold.

Indentation probe with optical fibre array‐based optical coherence tomography for material deformation

We present a new optomechanical probe for mechanical testing of soft matter. The probe consists of a micromachined cantilever equipped with an indenting sphere, and an array of 16 single‐mode optical fibres, which are connected to an optical coherence tomography (OCT) system that allows subsurface analysis of the sample during the indentation stroke. To test our device and its capability, we performed indentation on a PDMS‐based phantom. Our findings demonstrate that Common Path (CP)‐OCT via lensed optical fibres can be successfully combined with a microindentation sensor to visualise the phantom's deformation profile at different indentation depths and locations in real time.Lay DescriptionThis work presents a new approach to simultaneously perform micro‐indentation experiments and OCT imaging. An optical fiber array‐based sensor is used to develop a new hybrid tool where micro‐indentation is combined with optical coherence tomography. The sensor is therefore capable of compressing a sample with a small force and simultaneously collecting OCT depth profiles underneath and around the indentation point. This method offers the opportunity to characterize the mechanical properties of soft materials and simultaneously visualize their deformation profile. The ability to integrate OCT imaging with indentation technology is promising for the non‐invasive and precise characterization of different soft materials.

Sharing sustainability through sustainability control activities. A practice-based analysis

How can sustainability control change organizational practices dominated by the business-as-usual paradigm? This paper offers a practice-based perspective on the question with the aim of approaching sustainability control tools as something organizations do rather than something they have. Using data from an ethnographic study of a gambling company, the study explores how sustainability actors put into effect sustainability control tools. It shows that actors enacted sustainability control tools in different ways, each of which carried out through distinct arrays of control activities: (a) by capturing an existing tool in another practice, (b) by adding a new hybrid tool to another practice, or (c) by capturing a tool that was already shared between several practices. Through these control activities, they attempted to interlock the sustainability practice with other practices and produced distinct types of links – respectively, (a) reassembling, (b) expanding and (c) rippling. These findings contribute to unpacking sustainability control by highlighting that sustainability tools can only become control tools when they are supported by arrays of activities tying practices together, and that these interrelations can happen in different ways. These ways of enacting control enable sustainability controlling to various extents and they affect practices differently. The paper also contributes to practice theory by specifying how particular configurations of practices emerge and alter the practices involved.