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Existing precision design methods cannot directly guide the tolerance design. Therefore, in this study, an optimization design method of machine tool static geometric accuracy based on tolerance modeling is proposed. In this methodology, the mapping relationship between the geometric error of machine tools and tolerance design is established using the small displacement torsor to represent the tolerance information and the Monte Carlo simulation method is used to establish the response model of the torsor parameters and the tolerance variation bandwidths. An assembly accuracy model is then established by combining a machine tool topology analysis and the forming mechanism of the joint surface error. To calculate the tolerances of the component joint surface, a tolerance response model related to the component joint surface tolerance and torsor parameters is developed. Finally, according to the state function of assembly accuracy reliability, a function response model of the assembly accuracy, reliability, and tolerance is developed. Combining the assembly’s processing cost model with the accuracy, reliability, and tolerance principles, a tolerance optimization model of the static geometric accuracy of a CNC machine tool, a linear axis motion guide, is constructed as a case study. Using a simulated annealing genetic algorithm to solve the tolerance optimization model, the tolerance optimization value is obtained, thereby verifying the effectiveness of the proposed method.

Based on the topology analysis of a screw grinder mapping the dressing error of the grinding wheel (DEGW) to the geometric errors of the virtual axis, an improved accuracy model of the screw grinder with 36 geometric errors is established, and an error model of ball screw profile parameters is established according to the forming principle. Then, the Sobol method is performed to analyze the error sensitivity and obtain the crucial geometric errors affecting the profile parameters by considering the individual and intercoupling effect. It is worth mentioning that the overall change of the global sensitivity sum of the crucial geometric errors for lead error (EL) and pitch diameter error (EPD) is 22.9% and 30.2%, respectively, which indicates that the model considering the DEGW has a stronger ability to identify the geometric errors. Lastly, the grinding experiments of ball screw under the adjustment of four grinder geometric errors are conducted. The results show that the crucial geometric error corresponding to the EL and EPD is consistent with the calculation results of the Sobol method, which verifies the effectiveness of the proposed method, and provides a way to trace the machining errors.

Remotely piloted aerial systems (RPAS) have been recognized as an effective low-cost tool to acquire photogrammetric data of low accessible areas reducing collection and processing time. Data processing techniques like structure from motion (SfM) and multiview stereo (MVS) techniques, can nowadays provide detailed 3D models with an accuracy comparable to the one generated by other conventional approaches. Accuracy of RPAS-based measures is strongly dependent on the type of adopted sensors. Nevertheless, up to now, no investigation was done about relationships between camera calibration parameters and final accuracy of measures. In this work, authors tried to fill this gap by exploring those dependencies with the aim of proposing a prediction function able to quantify the potential final error in respect of camera parameters. Predictive functions were estimated by combining multivariate and linear statistical techniques. Four photogrammetric RPAS acquisitions were considered, supported by ground surveys, to calibrate the predictive model while a further acquisition was used to test and validate it. Results are preliminary, but promising. The calibrated predictive functions relating camera internal orientation (I.O.) parameters with final accuracy of measures (root mean squared error) showed high reliability and accuracy.

Assembly accuracy is an important index for measuring the assembly quality of a rudder system, which determines the attitude control accuracy of aircraft to a certain extent. In addition, the fit clearance between parts is an important factor affecting assembly accuracy. The manufacturing errors and assembly deformations of parts in the assembly process are the main factors that affect the fitting clearances. To reveal the effect of the fitting clearances on the assembly accuracy of a rudder system, this study focuses on a method for establishing an assembly accuracy prediction model that comprehensively considers the fitting clearances of the components. First, based on the small displacement torsor theory and homogeneous coordinate transformation theory, the error characterisation method and the system error transfer accumulation model construction method are studied. Second, the error models of plane and cylindrical features are constructed according to the error characterisation method. Third, the method of obtaining the actual fitting clearance of the fit surface by the least square method is studied, and the system assembly accuracy prediction model considering the fitting clearance factors of parts is constructed. Finally, taking a certain type of rudder system as an example, the assembly accuracy prediction model is analysed and verified. The results show that the existence of a fitting clearance in the assembly process leads to a lag in the deflection phase of the rudder system, and the deflection angle error at the end of the system can be 0.0139°. Therefore, the influence of fitting clearance on the assembly accuracy of the rudder system cannot be ignored. The method presented provides theoretical and technical support for the optimisation design and performance prediction of rudder systems.

In view of the differences in the applicability and prediction ability of different creep rupture life prediction models, we propose a creep rupture life prediction method in this paper. Various time–temperature parametric models, machine learning models, and a new method combining time–temperature parametric models with machine learning models are used to predict the creep rupture life of a small-sample material. The prediction accuracy of each model is quantitatively compared using model evaluation indicators (RMSE, MAPE, R2), and the output values of the most accurate model are used as the output values of the prediction method. The prediction method not only improves the applicability and accuracy of creep rupture life predictions but also quantifies the influence of each input variable on creep rupture life through the machine learning model. A new method is proposed in order to effectively take advantage of both advanced machine learning models and classical time–temperature parametric models. Parametric equations of creep rupture life, stress, and temperature are obtained using different time–temperature parametric models; then, creep rupture life data, obtained via equations under other temperature and stress conditions, are used to expand the training set data of different machine learning models. By expanding the data of different intervals, the problem of the low accuracy of the machine learning model for the small-sample material is solved.

Protein structure prediction is an important issue in structural bioinformatics. In this process, model quality assessment (MQA), which estimates the accuracy of the predicted structure, is also practically important. Currently, the most commonly used dataset to evaluate the performance of MQA is the critical assessment of the protein structure prediction (CASP) dataset. However, the CASP dataset does not contain enough targets with high-quality models, and thus cannot sufficiently evaluate the MQA performance in practical use. Additionally, most application studies employ homology modeling because of its reliability. However, the CASP dataset includes models generated by de novo methods, which may lead to the mis-estimation of MQA performance. In this study, we created new benchmark datasets, named a homology models dataset for model quality assessment (HMDM), that contain targets with high-quality models derived using homology modeling. We then benchmarked the performance of the MQA methods using the new datasets and compared their performance to that of the classical selection based on the sequence identity of the template proteins. The results showed that model selection by the latest MQA methods using deep learning is better than selection by template sequence identity and classical statistical potentials. Using HMDM, it is possible to verify the MQA performance for high-accuracy homology models.

The article discusses the technology capabilities of multi-purpose CNC machines, and possible options for implementing parallel multi-tool processing. It was revealed that the technological capabilities of these machines are used at best by 50% in factories. This is due to the lack of recommendations for the design and use of such adjustments for these machines. To this end, generalised lattice matrix models of the accuracy of multi-tool machining have been developed in order to fulfill the requirements of algorithmic uniformity models and their structural transparency. The use of lattice matrices greatly simplifies the error in model of multi-tool machining and makes it extremely visual. Also, full-factorial distortion models and scattering fields of the dimensions of multi-tool machining performed on modern multi-purpose CNC lathe machines have been developed to take into account the angular displacements of the workpiece when machining parts with prevailing overall dimensions. They take into account the flexibility of the technological system for all six degrees of freedom to identify the influence degree of complex of technological factors on the machining accuracy (structure of multi-tool adjustment, deformation properties of subsystems of a technological system, cutting conditions). A methodology has been developed for determining the complex characteristics of compliance of a technological system. On the basis of the developed accuracy models in spatial adjustments, it is possible to develop recommendations for the design of adjustments for modern multi-purpose machines in CNC turning group (creation of CAD of multi-tool machining). Thus, it is possible to achieve a number of ways to control multi-tool machining, including improving the structure of multi-tool adjustment, calculating the limiting cutting conditions.

Industry 4.0 is the emerging trend of the industrial automation. Millimeter-wave (mmWave) communication is a prominent technology for wireless networks to support the Industry 4.0 requirements. The availability of tractable accurate interference models would greatly facilitate performance analysis and protocol development for these networks. In this paper, we investigate the accuracy of an interference model that assumes impenetrable obstacles and neglects the sidelobes. We quantify the error of such a model in terms of statistical distribution of the signal to noise plus interference ratio and of the user rate for outdoor mmWave networks under different carrier frequencies and antenna array settings. The results show that assuming impenetrable obstacle comes at almost no accuracy penalty, and the accuracy of neglecting antenna sidelobes can be guaranteed with sufficiently large number of antenna elements. The comprehensive discussions of this paper provide useful insights for the performance analysis and protocol design of outdoor mmWave networks.

Learning exists in the context of data, yet notions of confidence typically focus on model predictions, not label quality. Confident learning (CL) is an alternative approach which focuses instead on label quality by characterizing and identifying label errors in datasets, based on the principles of pruning noisy data, counting with probabilistic thresholds to estimate noise, and ranking examples to train with confidence. Whereas numerous studies have developed these principles independently, here, we combine them, building on the assumption of a class-conditional noise process to directly estimate the joint distribution between noisy (given) labels and uncorrupted (unknown) labels. This results in a generalized CL which is provably consistent and experimentally performant. We present sufficient conditions where CL exactly finds label errors, and show CL performance exceeding seven recent competitive approaches for learning with noisy labels on the CIFAR dataset. Uniquely, the CL framework is not coupled to a specific data modality or model (e.g., we use CL to find several label errors in the presumed error-free MNIST dataset and improve sentiment classification on text data in Amazon Reviews). We also employ CL on ImageNet to quantify ontological class overlap (e.g., estimating 645 missile images are mislabeled as their parent class projectile), and moderately increase model accuracy (e.g., for ResNet) by cleaning data prior to training. These results are replicable using the open-source cleanlab release.

Estimating the fruit size is an important factor because it directly influences size-specific yield estimation, which would be useful for pricing in the market. In this paper, it was considered a method of constructing models for estimating the proportion of fruit size grades of citrus using polynomial regression. In order to construct models, curvilinear regressions were performed, utilizing the fruit diameters of a kind of citrus (Citrus junos Sieb. ex Tanaka) in the harvest. The constructed models were validated by comparison with another model, which was constructed using a combination of four datasets obtained from three orchards differing in the number of fruit sets. The estimation model’s accuracy (EMA, defined as the sum of the absolute difference between the actual and estimated proportions of each grade) was used for the evaluation of constructed models. The EMAs of 14 models applied to 28 validation data were ranging from 2.0% to 6.1%. In all validations, the proportions of fruit size grade were insignificant at a 5% level by Pearson’s chi-square test. Additionally, a comparison of EMAs differing in the number of trees by the constructed models showed that most were within EMA ≤ 10.0% in the case calculated by 10 trees. Validation of five farmers’ orchards indicated that the EMA of two was within 10.0%, and the EMA of three was at 11.3 to 12.5%. These results revealed that the constructed models could be applied to orchards for differing numbers of fruit sets. The acceptable accuracy was derived by at least over 10 trees investigated at one time.