Configuration Tool Research Articles

Control parameter optimisation using the evidence framework for the ant colony optimisation algorithm

The ant colony optimization (ACO) algorithm is a metaheuristic initially designed to solve the travelling salesman problem (TSP). The design of experiments, finding the suitable ACO algorithm configuration, and calibrating the adaptive control parameters are exhaustive and time-consuming exercises, especially for TSPs where the number of cities can exceed 1000. This paper presents an evidence framework driven control parameter optimisation (EFCPO) algorithm for an ACO algorithm solving TSPs. EFCPO performs auto-tuning of the adaptive control parameters and makes recommendations about the ACO algorithms that are best suited for the TSPs in question using the log evidence. In addition, with this ability, the algorithm can take a solution provided by an ACO algorithm and improve the results. The EFCPO accomplishes this over a number of cycles through auto-tuning of the control parameters and re-running the ACO until the process is completed. The capabilities of EFCPO are compared to another configuration tool, irace, using benchmark ACO algorithms to test the efficiency of the framework. The benchmark algorithms make use of a local search strategy to solve TSPs. The results show that ACO algorithms are able to find new and improved solution tours within reasonable times. The improvements are also significant. In addition, ACO algorithms that are best suited for the TSP in question are preferred, making the EFCPO an effective tool for real-time configuration of ACO algorithms for solving TSPs.

The vibrational circular dichroism exciton coupling in single-bond C–O stretching vibrations of p-menthene derivatives

The exciton coupling (EC) phenomenon becomes a valuable tool for the absolute configuration (AC) determination of organic molecules when an interaction between two strategically located chromophores in a molecule is evidenced by circularly polarized radiation. Besides the widely used exciton chirality method in electronic circular dichroism, the technique has been also developed for vibrational circular dichroism (VCD). Notably, the information provided by CO stretching vibrations (1680−1800 cm−1) in VCD have demonstrated high applicability. However, reported theoretical approaches have warned about the adequate interpretations of the EC phenomenon since analyzed bisignate bands could be associated with other vibrations. The current paper experimentally addresses the problem to expand the criteria for using EC data in AC determinations. Herein, the VCD exciton coupling (VCDEC) methodology is extended to the analysis of C–O stretching vibrations (1100–1300 cm−1) to uncover their possible use in AC determinations, as well as to reveal the specific contribution of the EC related to C–O vibrations when experimental parameters are considered. To this aim, naturally occurring p-menthene alcohols 1–4 isolated from Ageratina glabrata were previously used to prepare acetates 5 and 6, and acetonides 7 and 8, and now, to prepare carbonates 9–11. Density functional theory (DFT) calculations, including the IR and VCD spectra of the seven derivatives (5–11), were carried out to reinforce the conformational and configurational analysis, while the experimental VCD spectra of 5–7, 9, and 11 demonstrated the presence of C–O couplets. A systematic analysis of these bands, including the Δε value at alpha and beta state (Δεα, Δεβ, respectively), the amplitude of EC (A = ∑ǀΔεǀ) value, the couplet period (L =v‾Δεβ − v‾Δεα) value, the specific area of C–O at couplet (S = Σf(x)(u2)), and the percentage of area related to EC from C–O (S%) were considered. These experimental results were directly related to dihedral angles (θ) from simplistic models to establish the AC. The results suggested that A, Cp, S, and S% parameters are deeply related to the nature of the chromophores. Thereby, adequate molecular structural moieties should be chosen for AC determination when using the EC methodology.

Non-planar 5-axis directional additive manufacturing of plastics: Machine, process, and tool path

Additive manufacturing (AM) of plastics is a rising research and application area for light weighting. The conventional way of AM is conducted layer-by-layer, whereas recently non-planar AM is proposed with several advantages such as better use of the material through decreased weight-to-strength ratio, reuse of support material and increased conformity for AM of complex shapes. As for all the tool path-based manufacturing processes, generation of the tool path, selection of process parameters and the machine tool configuration significantly affects the product quality. In this study, non-planar AM of plastics is handled in a holistic manner covering tool path, process, and machine perspectives. Tool path generation for non-planar and directional AM is discussed in accordance with the CAD-CAM software, where the tool path is generated using a novel approach which works with the bead directions obtained from FEM based mechanical analysis. The effects of critical process parameters on the geometrical conformity for non-planar AM are discussed through visual inspection. The proposed cluster-based tool path planning is successfully shown demonstrated from the geometrical perspective.

A unified geometric error model applicable to all configurations of three-axis machine tools

A unified geometric error model applicable to all configurations of three-axis machine tools

Ergonomic assessment of mid-air interaction and device-assisted interactions under vibration environments based on task performance, muscle activity and user perceptions

Mid-air interaction has been increasingly introduced for human-computer interaction (HCI) tasks in vibration environments, but it has seldom been assessed from ergonomic aspects, especially in comparison with device-assisted interactions. This study aimed to provide a comprehensive ergonomic assessment of mid-air interaction and device-assisted interactions under vibration environments based on task performance, muscle activity in the upper limb and shoulder, and user perceptions. A within-subjects design was implemented in this study, where participants were required to perform basic pointing and dragging tasks with four interaction modes (i.e., one mid-air interaction and three device-assisted interactions) under static, low and high vibration environments, respectively. Both small and large target sizes were examined. Muscle activity was recorded with surface electromyography for five muscles from participants’ dominant arm. Results showed that mid-air interaction yielded longer task completion time, more errors, higher perceived workload, lower usability ratings, and larger muscle activities in the forearm, upper arm and shoulder compared with device-assisted interactions. There were significant interaction effects between vibration and interaction mode. Specifically, compared with device-assisted interactions, mid-air interaction was associated with greater susceptibility to the detrimental effects of vibration (poorer task performance and larger muscle activities). Target size significantly affected task performance, but the effects varied by tasks. Overall, our results suggest that mid-air interaction presents a higher ergonomic risk compared with device-assisted interactions, especially in vibration environments. These findings provide implications for better use, configuration and ergonomic assessment of interaction tools in vibration environments, and are useful in developing evidence-based interventions to control ergonomic risk in HCI tasks in vibration environments.

Federated Learning for Weld Quality Prediction

This study introduces a monitoring system that can accurately predict the quality of friction stir welds. The study involved four different sets of welding experiments using varying materials and tool configurations. The goal was to create a universal monitoring system capable of predicting weld quality across these diverse experimental sets. Real-time welding data was collected using instruments such as load cells and a power sensor. Signal processing methods were then used to analyze this data and extract essential information about welding quality. Subsequently, Federated Learning (FL) was used to develop the universal monitoring system. This method involves collective learning and model training, leading to a global model trained on decentralized data from the different experimental sets. The approach proved to be effective in forecasting weld quality, achieving a mean absolute error (MAE) of 10.44 for all welding experiments. Additionally, it offered benefits such as reduced latency and enhanced user data protection, while maintaining the accuracy of the global model. An artificial neural network (ANN) model was also developed for comparison with FL, achieving a MAE of 13.85 for the welding experiments. Overall, this study demonstrates the effectiveness of training global and more reliable models, with multiple devices sharing their knowledgebases to train the global model effectively. Conflict of Interest Statement The authors declare no conflicts of interest.

Implementation of automation configuration of enterprise networks as software defined network

Software defined network (SDN) is a new computer network configuration concept in which the data plane and control plane are separated. In Cisco system, the SDN concept is implemented in Cisco Application Centric Infrastructure (Cisco ACI), which by default can be configured through the main controller, namely the Application Policy Infrastructure Controller (APIC). Conventional configuration on Cisco ACI creates problems, i.e.: the large number of required configurations causes the increase of time required for configuration and the risk of misconfiguration due to repetitive works. This problem reduces the productivity of network engineers in managing Cisco system. In overcoming these problems, this research work proposes an automation tool for Cisco ACI configuration using Ansible and Python as an SDN implementation for optimizing enterprise network configuration. The SDN is implemented and experimented at PT. NTT Indonesia Technology network, as a case study. The experimental result shows the proposed SDN successfully performs multiple routers configurations accurately and automatically. Observations on manual configuration takes 50 minutes and automatic configuration takes 6 minutes, thus, the proposed SDN achieves 833.33% improvement.

Implementation of automation configuration of enterprise networks as software defined network

Software defined network (SDN) is a new computer network configuration concept in which the data plane and control plane are separated. In Cisco system, the SDN concept is implemented in Cisco Application Centric Infrastructure (Cisco ACI), which by default can be configured through the main controller, namely the Application Policy Infrastructure Controller (APIC). Conventional configuration on Cisco ACI creates problems, i.e.: the large number of required configurations causes the increase of time required for configuration and the risk of misconfiguration due to repetitive works. This problem reduces the productivity of network engineers in managing Cisco system. In overcoming these problems, this research work proposes an automation tool for Cisco ACI configuration using Ansible and Python as an SDN implementation for optimizing enterprise network configuration. The SDN is implemented and experimented at PT. NTT Indonesia Technology network, as a case study. The experimental result shows the proposed SDN successfully performs multiple routers configurations accurately and automatically. Observations on manual configuration takes 50 minutes and automatic configuration takes 6 minutes, thus, the proposed SDN achieves 833.33% improvement.

Towards a standard benchmark for variant and gene prioritisation algorithms: PhEval - Phenotypic inference Evaluation framework.

Computational approaches to support rare disease diagnosis are challenging to build, requiring the integration of complex data types such as ontologies, gene-to-phenotype associations, and cross-species data into variant and gene prioritisation algorithms (VGPAs). However, the performance of VGPAs has been difficult to measure and is impacted by many factors, for example, ontology structure, annotation completeness or changes to the underlying algorithm. Assertions of the capabilities of VGPAs are often not reproducible, in part because there is no standardised, empirical framework and openly available patient data to assess the efficacy of VGPAs - ultimately hindering the development of effective prioritisation tools. In this paper, we present our benchmarking tool, PhEval, which aims to provide a standardised and empirical framework to evaluate phenotype-driven VGPAs. The inclusion of standardised test corpora and test corpus generation tools in the PhEval suite of tools allows open benchmarking and comparison of methods on standardised data sets. PhEval and the standardised test corpora solve the issues of patient data availability and experimental tooling configuration when benchmarking and comparing rare disease VGPAs. By providing standardised data on patient cohorts from real-world case-reports and controlling the configuration of evaluated VGPAs, PhEval enables transparent, portable, comparable and reproducible benchmarking of VGPAs. As these tools are often a key component of many rare disease diagnostic pipelines, a thorough and standardised method of assessment is essential for improving patient diagnosis and care.

GenoFig: a user-friendly application for the visualization and comparison of genomic regions.

Understanding the molecular evolutionary history of organisms usually requires visual comparison of genomic regions from related species or strains. Although several applications already exist to achieve this task, they are either too old, too limited, or too complex for most user's needs. GenoFig is a graphical application for the visualization of prokaryotic genomic regions, intended to be as easy to use as possible and flexible enough to adapt to a variety of needs. GenoFig allows the personalized representation of annotations extracted from GenBank files in a consistent way across sequences, using regular expressions. It also provides several unique options to optimize the display of homologous regions between sequences, as well as other more classical features such as sequence GC percent or GC-skew representations. In summary, GenoFig is a simple, free, and highly configurable tool to explore the evolution of specific genomic regions in prokaryotes and to produce publication-ready figures. Genofig is fully available at https://forgemia.inra.fr/public-pgba/genofig under a GPL 3.0 license.

A Python Project Template for Healthy Scientific Software

The creation of “healthy” scientific software is vital for its successful long-term adoption in scientific research. Here healthy code is defined to mean software that is usable, maintainable, and proffers consistently reproducible results. Incorporating tooling and practices to achieve these goals often leads to short-term, yet significant, overhead for new projects. We introduce the LINCC Frameworks Python Project Template, a configurable code template designed for scientific software projects that greatly simplifies adopting best practices by automating the setup and configuration of important tools locally and via a suite of GitHub workflows. Notably, the template does not include any application-specific code, thereby enabling users to focus on their scientific code rather than building or maintaining code infrastructure.

Optimizing Micro Friction Stir Spot Welding (mFSSW) of Aluminum Alloy AA1100 Using Neural Network Model

This study explores the optimization of Micro Friction Stir Spot Welding (mFSSW) by investigating the influence of tool profiles on welding outcomes, using aluminum alloy AA1100 with a 0.42 mm thickness as the specimen material. Monitoring temperature and RPM during welding with thermocouples and tachometers, mechanical properties are assessed through tensile shear tests, microhardness measurements, and macrostructural observations. The findings serve as the basis for developing Neural Network models using Rapidminer software, marking a transformative development that positions Neural Networks as potent tools for optimizing welding processes, potentially leading to achieving optimal weld quality. The investigation also delves into three welding tool configurations – the two-stage pin, one-stage pin, and pinless mFSSW probes – highlighting their distinct impacts on tensile shear test values and overall welding quality. Notably, the two-stage pin configuration emphasizes the significance of larger pin diameters and controlled heat generation for enhanced weld strength, while the one-stage pin configuration underscores the pivotal role of pin diameter and elevated temperatures in improving weld quality. The pinless mFSSW probe configuration, on the other hand, emphasizes the importance of shoulder diameter and temperature control for superior tensile shear test results. Leveraging Neural Network modeling for optimization, this study advances our understanding of parameter interactions and underscores the efficacy of Neural Networks in achieving superior tensile shear test values and welding quality in mFSSW, offering valuable insights for future endeavors in the field..

Traversing Situations and Contexts in Networked Learning

To meet the goals of business education and develop responsible business leaders, educators need to design networked learning environments that nurture and leverage connections. This includes connecting students to each other, their teachers, to industry and to the community. This paper aims to understand factors that support or inhibit students’ participation in networked learning in a business education context. Productive participation in learning networks is essential if students are to connect meaningfully with people and resources in ways that support their learning and experience. As the physical and digital learning environments students engage in often include complex configurations of tools, tasks, and epistemic resources, they require the skills and dispositions to effectively transition between these environments. Using a situated readiness framework by Hachmann and Dohn (2018), this study presents an analysis of focus group data from business students in higher education to identify ways in which students’ level of readiness impacts on their ability to participate effectively in learning networks. Students in the present study highlighted a range of challenges they faced when attempting to traverse between face-to-face and online learning environments. However, our study also revealed numerous other situations or contexts that business students needed to traverse as part of their networked learning experience. These were grouped into five overarching categories: domain; space and configuration; format, structure and resources; people or groups; and purpose and responsibility. Overlayed onto the situated readiness framework, these categories can be used by other researchers to gain a more nuanced understanding of how students navigate the complexities of networked learning environments. Future research conducted across a range of learning contexts would allow the categories to be refined and built upon. The study also identified skills and dispositions that contributed to students' level of readiness to navigate situations and contexts within networked learning environments, and some preliminary design considerations based on the findings. To support business students in higher education on their journey to becoming responsible business leaders, it is imperative that they are able to participate productively in complex networked learning environments. As educators, this requires us to understand their level of readiness to do so and equip them with the skills and dispositions they need to participate effectively.

Assembly procedure and tooling development plan for in-situ winding of ITER in-vessel vertical stability coils

The ITER in-vessel Vertical Stabilization (VS) coils, composed by the upper and lower VS coils, are designed to provide fast vertical stabilization of the plasma through a fast-current noise profile to compensate small plasma oscillations and to recover vertical displacement events.The VS coils are made by 4 continuous turns of Mineral Insulated Conductor (MIC) joined by Inconel ® 625 welded brackets, installed on rails on the inner wall of ITER Vacuum Vessel (VV). Thus, the coil turns winding/forming and the entire coils assembly have to be performed inside ITER VV, forcing the assembly procedure and tooling design to cope with the very limited space and strict constraints of ITER VV environment. The proposed procedure foresees that MIC conductor is un-spooled in the Neutral Beam (NB) port area and inserted into the ITER VV through a NB equatorial port, then a set of tooling is installed on a reinforced staging platform to wind, form and seal the conductor turns and finally to weld brackets under pre-compression. For this purpose, the tooling design including the reinforcement staging has been developed to meet the required tooling function, configuration optimization, assembly procedure and environmental constraints. The dedicated tools to be used for the assembly procedure of the VS coils are categorized into ex-vessel tools for unspooling and cleaning, in-vessel tools for winding and bump forming, bracket assembly tools for pre-compression and welding, termination and sealing tools, ancillary lifting tools and reinforcement staging platforms. This paper provides the overall assembly procedure and tooling development for the in-situ winding of VS coils.

Overview of in-bore pipe cutting and welding tools for the maintenance of CFETR and EU-DEMO

In the remote maintenance of fusion nuclear devices, one of the most challenging tasks is the cutting and the re-welding of large pipes that feed the breeding blanket. The narrow space around some of the pipes hinders the suitability of conventional orbital processes. For such pipes, both the cutting system and the welding one have to be compact enough to move along their interior, lock against the pipe inner wall and keep the position during the operation. The inner diameter and thickness of the pipes together with the other severe requirements related to the remote maintenance drive the choice of the processing technologies and the design of the tooling configuration. In this work, activities carried out by a joint team from EU-DEMO and CFETR on the in-bore cutting and welding operations are presented and discussed. For the in-bore cutting system a solution for the simultaneous parting and bevelling has been developed opting for a mechanical cutting with a pair of symmetrically distributed knives. A preliminary dimensioning of the motors has been made assuming the radial feed and the cutting speed of the knives. For the in-bore welding system, the TIG welding with filler wire has been considered as the most reliable and suitable technology within prescribed requirements and constraints. A multi-pass welding operation has been assumed. A conceptual design of both tools has been developed. The space constraint remains the most critical issue. Further verification and validation work is planned in close collaboration between European laboratories and the Comprehensive Research fAcility for Fusion Technology (CRAFT) at ASIPP in Hefei.

The impact of spectral basis set composition on estimated levels of cingulate glutamate and its associations with different personality traits

Background1H-MRS is increasingly used in basic and clinical research to explain brain function and alterations respectively. In psychosis research it is now one of the main tools to investigate imbalances in the glutamatergic system. Interestingly, however, the findings are extremely variable even within patients of similar disease states. One reason may be the variability in analysis strategies, despite suggestions for standardization. Therefore, our study aimed to investigate the extent to which the basis set configuration– which metabolites are included in the basis set used for analysis– would affect the spectral fit and estimated glutamate (Glu) concentrations in the anterior cingulate cortex (ACC), and whether any changes in levels of glutamate would be associated with psychotic-like experiences and autistic traits.MethodsTo ensure comparability, we utilized five different exemplar basis sets, used in research, and two different analysis tools, r-based spant applying the ABfit method and Osprey using the LCModel.ResultsOur findings revealed that the types of metabolites included in the basis set significantly affected the glutamate concentration. We observed that three basis sets led to more consistent results across different concentration types (i.e., absolute Glu in mol/kg, Glx (glutamate + glutamine), Glu/tCr), spectral fit and quality measurements. Interestingly, all three basis sets included phosphocreatine. Importantly, our findings also revealed that glutamate levels were differently associated with both schizotypal and autistic traits depending on basis set configuration and analysis tool, with the same three basis sets showing more consistent results.ConclusionsOur study highlights that scientific results may be significantly altered depending on the choices of metabolites included in the basis set, and with that emphasizes the importance of carefully selecting the configuration of the basis set to ensure accurate and consistent results, when using MR spectroscopy. Overall, our study points out the need for standardized analysis pipelines and reporting.

Experimental study on mechanical behaviour of friction stir welded aluminium alloy butt joints

The purpose of this paper is to understand the softening extent and stress-strain behavior of the normal-strength 6061-T6, 6013-T6, and high-strength 7075-T6 aluminium alloy butt joints using the novel friction stir welding (FSW) technique. A total of 107 monotonic tensile coupons and 33 Vickers hardness coupons were prepared from 8- or 12-mm thick welded plates using various tool rotation speed, welding transverse speed and tool configurations. The surface morphology, stress-strain curves and hardness distribution laws after welding were evaluated. The W-shaped hardness profiles were clearly observed, with the fracture location positively correlated at the lowest point of Vickers hardness near the edge of the weld toe. The strength reduction and softening extent of extruded aluminium alloys in T6 temper induced by FSW were superior to those of aluminium alloys using fusing welding process outlined in the Chinese and European standards, indicating the effectiveness and applicability of the FSW technique in the formation of components and connections for aluminium alloy structures. Additionally, the current widely used constitutive models generally yielded under-estimations on the strength under large strains. The developed three-stage Ramberg-Osgood model, using seven key input parameters, significantly improved the accuracy and consistent predictions in the full-range stress-strain curve of FSW aluminium alloys. Furthermore, the suggested model could still achieve a great balance between accuracy and practicality when using just three common available parameters with the employment of predictive expressions on other four parameters.

Characteristics of in-situ automated fiber placement carbon-fiber-reinforced low-melt polyaryl ether ketone laminates part 1: Manufacturing influences

This study presents an investigation into mechanical and thermal properties, as well as the microstructure of Automated Fiber Placement-manufactured laminates using a novel carbon fiber-reinforced low-melt polyaryl ether ketone polymer material. The material’s lower melting temperature and lower melt viscosity as compared to established high-temperature thermoplastic materials as PEEK, promises favourable characteristics for the Automated Fiber Placement process. This work aims at in-situ consolidation and the influence of a heated tooling and a post process tempering step, which both turned out to be promising in previous investigations. Laminates were manufactured using a cold tooling, a heated tooling configuration, a cold tooling with a subsequent tempering process step and a hot-pressed reference laminate. Differential Scanning Calorimetry showed that crystallinity values more than doubled for the heated tooling and post process tempering configurations, compared to the cold tooling, reaching 24% and 30%, respectively. Mechanical strength values showed an increase in interlaminar shear strength and compression strength but did not increase to the same extent as was expected from the increase in crystallinity. With Scanning Electron Microscopy differences in the microscopic structure of the polymer matrix could be detected. While the post process tempering step leads to a mostly lamellar crystalline structure, the heated tooling configuration and the post process hot pressing induce a predominance of crystalline spherulites, which might positively affect the mechanical performance. Computed Tomography scans revealed a high amount of porosity in the in-situ-manufactured samples and unprocessed tape material, which likely mitigated the positive effect of increased crystallinity.

Modelling the material removal obtained using a pneumatically configurable polishing tool subjected to a reciprocating linear feed

A nanofinishing technique - Pneumatically Configurable Polishing (PCP) process can be used for producing surface roughness of the order of a few nanometers on surfaces. It is a deterministic polishing process which employs pneumatic pressure for precise control of finishing forces across the polishing spot. The material removal rate is an important characteristic for determining the final surface generated after polishing. The material removal mechanism of PCP process typically exhibits multidisciplinary complexity due to simultaneous contribution of various process parameters. A mathematical model has been proposed in this study to achieve an in-depth understanding of the process physics. The model is developed using the fundamental tenets of contact behaviour between the tool and workpiece, and study of the microscopic material removal caused by a single abrasive particle. The polishing tool undergoes a reciprocating linear feed to polish a linear strip of the work surface. Various process parameters such as feed rate, tool rotational speed, working gap, mean diameter of abrasive particles, slurry concentration etc have been incorporated in the model as per the physics of the process. In order to validate the model, finishing experiments and MATLAB simulations have been conducted under similar polishing conditions to estimate the prediction error. The material depth removal rate in the PC polishing of die steel was found to range between 39 nm/min and 257 nm/min. The error between the experimental and predicted values of material removal rate was found within a range of 2.7 %–15.9 %. The repeatability of final surface roughness values and MRR obtained is ±0.007 μm and ±10 nm/min respectively. The developed model helps to make the PCP process more predictable and provides valuable insights into the various parameters which predominantly affect the material removal. The simulation of the gradual evolution of the surface profile has been obtained using the developed model.

Efficient Method for Identifying Key Errors Based on 21-Geometric-Error Measurement of Three Linear Axes of Machine Tools

Key errors of machine tools have a significant impact on their accuracy, however accurately and quickly measuring the geometric errors of machine tools is essential for key error identification. Fortunately, a quick and direct laser measurement method and system for 21 geometric errors of three linear axes of machine tools were proposed previously, which enables the measurement of all 21 geometric errors via a one-step installation and a three-step automated measurement process. Based on this, to efficiently identify the key error factors, this paper first utilizes the 21 geometric errors obtained from the proposed measurement system to evaluate the contribution of each error to the volumetric errors of machine tools, leading to the building of a 21-geometric-error sensitivity analysis model. Then, experiments are carried out on the vertical machining tool TH5656, and all 21 geometric errors are obtained in 5 min. After this, the volumetric error distribution in the machining workspace is mapped according to the relationship between the geometric errors and the machining errors, and the key error factors affecting the manufacturing and machining accuracy of the TH5656 are ultimately determined. Thus, this new method provides a way to quickly identify key errors of the three linear axes of machine tools, and offers guidance for the machine tool configuration design, machining technology determination, and geometric error compensation.