Testing the effect of bioturbation and species abundance upon discrete-depth individual foraminifera analysis

We use a single foraminifera enabled, holistic hydroclimate-to-sediment transient modelling approach to fundamentally evaluate the efficacy of discrete-depth individual foraminifera analysis (IFA) for reconstructing past sea surface temperature (SST) variability from deep-sea sediment archives, a method that has been used for, amongst other applications, reconstructing El Niño Southern Oscillation (ENSO). The computer model environment allows us to strictly control for variables such as sea surface temperature (SST), foraminifera species abundance response to SST, as well as depositional processes such as sediment accumulation rate (SAR) and bioturbation depth (BD), and subsequent laboratory processes such as sample size and machine error. Examining a number of best-case scenarios, we find that IFA-derived reconstructions of past SST variability are sensitive to all of the aforementioned variables. Running 100 ensembles for each scenario, we find that the influence of bioturbation upon IFA-derived SST reconstructions, combined with typical samples sizes employed in the field, produces noisy SST reconstructions with poor correlation to the original SST distribution in the water. This noise is especially apparent for values near the edge of the SST distribution, which is the distribution region of particular interest for, e.g., ENSO. The noise is further increased in the case of increasing machine error, decreasing SAR and decreasing sample size. We also find poor agreement between ensembles, underscoring the need for replication studies in the field to confirm findings at particular sites and time periods. Furthermore, we show that a species’ abundance response to SST could in theory bias IFA-derived SST reconstructions, which can have consequences when comparing IFA-derived SST from markedly different mean climate states. We provide a number of idealised simulations spanning a number of SAR, sample size, machine error and species abundance scenarios, which can help assist researchers in the field to determine under which conditions they could expect to retrieve significant results.

Five-axis machine tool volumetric and geometric error reduction by indirect geometric calibration and lookup tables

Modern CNC machine tools provide lookup tables to enhance the machine tool's precision but the generation of table entries can be a demanding task. In this paper, the coefficients of the 25 cubic polynomial functions used to generate the LUTs entries for a five-axis machine tool are obtained by solving a linear system incorporating a Vandermonde expansion of the nominal control jacobian. The necessary volumetric errors within the working volume are predicted from machine's geometric errors estimated by the indirect error identification method based on the on-machine touch probing measurement of a reconfigurable uncalibrated master ball artefact (RUMBA). The proposed scheme is applied to a small Mitsubishi M730 CNC machine. Two different error models are used for modeling the erroneous machine tool, one estimating mainly inter-axis errors and the other including numerous intra-axis errors. The table-based compensation is validated through additional on-machine measurements. Experimental tests demonstrate a significant reduction in volumetric errors and in the effective machine error parameters. The LUTs reduce most of the dominant machine error parameters. It is concluded that although being effective in correcting some geometric errors, the generated LUTs cannot compensate some axis misalignments such as EB(OX)A and EB(OX)Z. The Root Mean Square of the translational volumetric errors are improved from 87.3, 75.4 and 71.5 µm down to 24.8, 18.8 and 22.1 µm in the X, Y and Z directions, respectively.

Smart Machinery Monitoring System with Reduced Information Transmission and Fault Prediction Methods Using Industrial Internet of Things

A monitoring system for smart machinery has been considered to be one of the most important goals in recent enterprises. This monitoring system will encounter huge difficulties, such as more data uploaded by smart machines, and the available internet bandwidth will influence the transmission speed of data and the reliability of the equipment monitoring platform. This paper proposes reducing the periodical information that has been uploaded to the monitoring platform by setting an upload event through the traits of production data from machines. The proposed methods reduce bandwidth and power consumption. The monitoring information is reconstructed by the proposed methods, so history data will not reduce storage in the cloud server database. In order to reduce the halt time caused by machine error, the proposed system uses machine-learning technology to model the operating status of machinery for fault prediction. In the experimental results, the smart machinery monitoring system using the Industrial Internet of Things reduces the volume of information uploaded by 54.57% and obtains a 98% prediction accuracy.

Analysis of the Effect of Machining Sequence on Machining Error Considering Workpiece Rigidity Change

The objective of this research is to investigate relations between individual physical phenomena and machining error from the measured machined error. Small lot production using numerical control machine tools is widely applied to high quality and high value-added products. In such production, agile and flexible machining is required. Thus, there have been many researches which investigate the effect of specific phenomena such as cutting force, thermal expansion, tool wears, chattering vibration and so on, which is to realize high precision machining. However, there have been some unsolved problems. The first problem is focused phenomena are mostly cutting force and/or machine tool deflection. Accordingly, other effects such as the results by workpiece rigidity change have not been investigated enough. The second problem is that the generation process of machining error is complicated and there is no proper method to compensate. Because of those complicate process, it is difficult to determine the dominant error factor of a new machining case in advance. Therefore, on-machine error measurement and estimation of error factors are essential technologies in order to achieve accuracy assurance. Recently, machining for rib-structured and thin-walled workpiece becomes important because of their higher structure efficiency and light weight characteristic. In this paper, the effect of workpiece rigidity to the machining error is investigated. Depend on the machining sequence, workpiece rigidities change differently during the machining process. Two different machining cases with different machining sequences are conducted and difference between the cases are investigated.

Impact of Model Complexity in the Monitoring of Machine Tools Condition Using Volumetric Errors

The scale and master ball artefact (SAMBA) method allows estimating the inter- and intra-axis error parameters as well as volumetric errors (VEs) of a five-axis machine tool by using simple ball artefacts and the machine tool’s own touch-trigger probe. The SAMBA method can use two different machine error models named after the number of model parameters, i.e., the “13” and “84” machine error models, to estimate the VEs. In this study, we compare these two machine error models when using VE vector directions and values for monitoring the machine tool condition for three cases of machine malfunctions: 1) a C-axis encoder fault, 2) an induced X-axis linear positioning error, and 3) an induced straightness error simulated fault. The results show that the “13” machine error model produces more focused concentrated VE directions but smaller VE values when compared with the “84” machine error model; furthermore, although both models can recognize the three faults and are effective in monitoring the machine tool condition, the “13” machine error model achieves a better recognition rate of the machine condition. This paper provides guidelines for selecting machine error models for the SAMBA method when using VEs to monitor the machine tool condition.

An Integrated Geometric and Hysteretic Error Model of a Three Axis Machine Tool and Its Identification With a 3D Telescoping Ball-Bar

The ball-bar instrument is used to estimate a maximum number of hysteretic error sources. Machine error parameters include inter- and intra-axis errors as well as hysteresis effects. An error model containing cubic polynomial functions and modified qualitative variables, for hysteresis modeling, is proposed to identify such errors of the three nominally orthogonal linear axes machine. Such model has a total of 90 coefficients, not all of which being necessary. A numerical analysis is conducted to select a minimal but complete non-confounded set of error coefficients. Four different ball-bar test strategies to estimate the model coefficients are simulated and compared. The first one consists of circular trajectories on the primary planes XY, YZ, and XZ and the others use the XY plane, as an equator, and either four, five, or nine meridians. It is concluded that the five-meridian strategy can estimate the additional eight error coefficients: ECZ1, ECZ2, ECZ3, ECZb, EZY3, EZX3, ECX3, and ECXb. The Jacobian condition number is improved by increasing the number of meridians to 5. Further increasing the number of meridians from five to nine improves neither the number of estimable coefficients nor the conditioning, and so as it increases, the test time it was dismissed.

Intelli-NGS: Intelligent NGS, a deep neural network-based artificial intelligence to delineate good and bad variant calls from IonTorrent sequencer data

BackgroundIonTorrent is a second-generation sequencing platform with smaller capital costs than Illumina but is also prone to higher machine error than later. Given its lower costs, the platform is generally preferred in developing countries where next-generation sequencing is still a very exclusive technique. There are many software tools available for other platforms but IonTorrent. This makes the already tricky analysis part more error-prone.MotivationWe have been using the IonTorrent platform in our hospital setting for aiding diagnosis or treatment for the past couple of years. Given to our experience, analysis part of IonTorrent data takes the longest time and still, we used to get stuck with certain variants which seemed fine on looking at their metrics but were found to be negative in Sanger sequencing verification. This made us determined to develop a tool that could aid us in reducing false positive and negative rates while still retaining good recall. The artificial intelligence-based technique was our final choice after developing pipelines with less success.MethodologyThe artificial intelligence was developed from scratch in Python 3 using TensorFlow fully connected dense layers. The model takes VCF files as input and solves each variant based on the thirty-five parameters given by the IonTorrent platform, including the flow-space information which is missed by variant callers other than the default torrent variant caller.ResultsThe final trained model was able to achieve an accuracy of 93.08% and a ROC-AUC of 0.95 with GIAB validation data. The additional program that was written to run the model annotates each variant using online databases such as dbSNP, ClinVar and others. A probability score for each outcome for each variant is also provided to aid in decision making.AvailabilityThe model and running code are available for free only for non-commercial users at https://www.github.com/aditya-88/intelli-ngs.

Error modeling and compensation for FDM machines

Purpose The purpose of this paper is to improve the accuracy of fused deposition modeling (FDM) machines. Design/methodology/approach An integrated error model and compensation methods are developed to improve the accuracy of FDM machines. The effects of machine-dependent and process-dependent errors are included in this integrated model. The error model is then used to obtain compensated values for the printed object. A three-dimensional artifact is designed for the FDM machine characterization. This process takes place only once and an error model for the machine is then developed. An artifact is designed that is feature rich and its coordinates are measured by the coordinate measuring machine (CMM). The CMM digitized values for the three-dimensional artifact are used to calculate the coefficients of the model. The integrated error model of the machine can be used to obtain the compensated values for any given part models. The coefficients of the integrated error model are machine-dependent and represent machine error estimation. To demonstrate this, two test examples are used and modified based on the machine model to verify the effectiveness of the proposed method. Findings The errors from machine mechanical structure and process are evaluated. The variation trend of each error is analyzed. The uncompensated and compensated models are compared, and the effectiveness of the integrated error model and compensation method is analyzed and validated. Originality/value An effective integrated error model with compensation is developed, which can be used to improve the FDM machines accuracy.