Modeling and Compensation for Five-Axis Machine Tool Errors

We replicated and extended a project by Dickinson and Burke (1996) that concerned human causal judgement. In a medical diagnostic setting, college students' ratings of the causal efficacy of target cues showed retrospective revaluation: relative to a proper control condition, ratings of target cues both increased ("recovery from overshadowing") and decreased ("backward blocking") during a second stage of training in which competing cues, but not target cues, were presented. These changes in causal judgements were exhibited only by subjects who had learned which target and competing cues were paired with one another during the first stage of training. These results cannot be explained by the Rescorla-Wagner (1972) model of associative learning, but they can be explained by the revised model of Van Hamme and Wasserman (1994); the revised model assigns non-zero salience to non-presented target stimuli whose memories or representations are retrieved by competing stimuli that had previously been paired with those target stimuli.

The five-axis machine tools are increasingly popular for meeting the demand of machining the workpiece with growing geometric complexity and high accuracy. This paper studies the volumetric error modeling and its sensitivity analysis for the purpose of machine design. The volumetric error model of a five-axis machine tool with the configuration of RTTTR is established based on rigid body kinematics and homogeneous transformation matrix, in which 37 error components are involved. The sensitivity analysis of volumetric error regarding 37 error components is carried out respectively. The analysis results are successfully used for the accuracy design and manufacture of a five-axis ultra-precision machine tool. The preliminary experiment of machining sine grid surface testifies the high accuracy and effectiveness of the designed five-axis machine tool.

In the manufacturing industry, computer numerical control (CNC) machine tool has been applied widely due to their high adaptability and precision in the machining of diversity shape, especially five-axis CNC machine tools. However, more error terms related to rotary axes, in which location errors are always considered as one of the most fundamental error sources, affect directly to machine tool performance. Thus, ensuring that a five-axis machine tool is machining within tolerance is a crucial demand in the market. This study investigates an efficient strategy using a specific 3D artifact to identify and characterize the location errors in rotary axes of a five-axis machine tool. The intended 3D artifact consists of a base plate, 2 standards ball with high roundness embedded on the top surface of the base. A Touch Trigger Probe will be utilized as the calibration instrument to capture the coordinate of 6 arbitrary contact points of the balls, then an optimization algorithm is applied to calculate the coordinate of the centers of the balls. By comparisons the relative position of the imaginary circles made of those centers with the ideal ones, all location errors will be measured individually.

Enhancing the accuracy of machine tool is a key goal of machine tool manufactures and users. To characterize the Quasi-static errors and then use software compensation is an important step for accuracy enhancement. The effectiveness of an error compensation scheme relies heavily on the error model. The model must be concise and roust which can be applied to any machine tool. The total Quasi-static errors within the workspace of a five-axis gantry machine tool is composed of geometric error, kinematic error, thermal error. This paper presents an error model which can be used for practical compensation scheme. Homogeneous transformation matrix, rigid body kinematic and small angle approximations are used in this paper for error modeling.

The simultaneous measurement and identification of position-independent geometric errors (PIGEs) and position-dependent geometric errors (PDGEs) of dual rotary axes in five-axis machine tools remain a significant challenge in precision machining. To address this issue, this study develops a geometric error measurement system for the dual rotary axes of a five-axis machine tool by integrating a scanning probe, a block, and a calibration sphere. Unlike conventional touch-trigger probes, the scanning probe acquires continuous measurement signals along the surfaces of the block and the calibration sphere, enabling the extraction of richer geometric features and providing sufficient data for the simultaneous analysis of multiple geometric error components. The proposed measurement system is implemented on a five-axis machine tool, Uni5X-400, manufactured by FALCON Machine Tools Co., Ltd., to measure the geometric information of the calibration sphere and the block and thereby identify 20 PIGEs and PDGEs associated with the dual rotary axes. A kinematic model of the measurement system is established using homogeneous coordinate transformation matrices. By applying forward and inverse kinematics, the spatial positions of the calibration sphere and block are derived, forming an explicit relationship between volumetric errors and geometric errors. The resulting error identification equations are solved using the least squares method. Experimental results demonstrate that the proposed approach can efficiently and accurately identify the geometric errors of the dual rotary axes and maintain robust reliability under real-world operating conditions, making it suitable for practical calibration and accuracy improvement of five-axis machine tools.

The relative position and orientation deviation between grinding wheel and workpiece caused by geometric errors affect the machining accuracy of five-axis CNC gear profile grinding machine tools directly. Therefore, geometric error modeling and compensation are presented according to homogeneous transformation and differential motion matrix based on the multi-body system theory for the accuracy enhancement of the machine tools. Firstly, the open kinematic chain and the ideal homogeneous transformation matrix from workpiece to grinding wheel are established according to the topological structure of the CFXZAY-type, five-axis gear profile grinding machine tools and the basic homogeneous transformation matrix between coordinate frames. Secondly, the homogenous transformation matrices of linear pairs and rotary pairs with geometric errors are calculated, and the relationships of the error propagation from workpiece to grinding wheel are acquired as well on the basis of 37 geometric error components analysis. The geometric error models including position and orientation errors of grinding wheel in workpiece coordinate system are obtained with matrix multiplication by using small-angle approximation and ignoring the second-order and high-order error terms. Then, the Jacobian is obtained by using transforming differential motion matrix of each motion axis to compensate the integrated error components of the grinding wheel, which can make the compensation effective and convenient with using the corresponding homogeneous transformation matrix. Finally, error measurement, error compensation, and machining experiments are carried out on a five-axis CNC gear profile grinding machine tool SKMC-1200W/10 to verify the applicability and effectiveness of the proposed error modeling, error compensation, and research approach.

In the present study, deep learning neural network model has been employed in many engineering problems including heat transfer prediction. The main consideration of this document is to predict the performance of the boiling heat transfer in helical coils under terrestrial gravity conditions and compare with actual experimental data. Total of 877 data sample has been used in the present neural model. Artificial new Neural Network (ANN) model developed in Python environment with Multi-layer Perceptron (MLP) using four parameters (helical coils dimensions, mass flow rate, heating power, inlet temperature) and one parameter (outlet temperature) has been used in the input layer and output layer in order. Levenberg-Marquardt (LM) algorithm using L2 Regularization to find out the optimal model. A typical feed-forward neural network model composed of three layers, with 30 numbers of neurons in each hidden layer, has been found as optimal based on statistical error analysis. The 4-30-30-1 neural model predicts the characteristics of the helical coil with the accuracy of 98.16 percent in the training stage and 96.68 percent in the testing stage. The result indicated that the proposed ANN model successfully predicts the heat transfer performance in helical coils and can be applied for others operation concerned with heat transfer prediction for future works

Training and credentialing in laparoscopic surgery — the need of the day

A machine learningsemi-supervised method was developed for the classification analysis of defects on the surface of the sewer pipe based on CCTV video inspection images. The aim of the research is the process of defect detection on the surface of sewage pipes. The subject of the research is a machine learning method for the classification analysis of sewage pipe defects on video inspection images under conditions of a limited and unbalanced set of labeled training data. A five-stage algorithm for classifier training is proposed. In the first stage, contrast training occurs using the instance-prototype contrast loss function, where the normalized Euclidean distance is used to measure the similarity of the encoded samples. The second step considers two variants of regularized loss functions – a triplet NCA function and a contrast-center loss function. The regularizing component in the second stage of training is used to penalize the rounding error of the output feature vector to a discrete form and ensures that the principle of information bottlenecking is implemented. The next step is to calculate the binary code of each class to implement error-correcting codes, but considering the structure of the classes and the relationships between their features. The resulting prototype vector of each class is used as a label of image for training using the cross-entropy loss function. The last stage of training conducts an optimization of the parameters of the decision rules using the information criterion to consider the variance of the class distribution in Hamming binary space. A micro-averaged metric F1, which is calculated on test data, is used to compare learning outcomes at different stages and within different approaches. The results obtained on the Sewer-ML open dataset confirm the suitability of the training method for practical use, with an F1 metric value of 0.977. The proposed method provides a 9 % increase in the value of the micro-averaged F1 metric compared to the results obtained using the traditional method.

A new methodology was used to train two rats on a conditional discriminationtask that used multi-modal stimuli that differed along a number of sensory dimensions (i.e., visual, tactile and olfactory). Rats were trained across two stages to move from a multi-modal sample to a multi-modal comparison for food reward (i.e., given Wood as sample. move to the Rubber comparison [A1-B1], and given Foam as sample. move to the Carpet comparison [A2-B2]). Having reached criterion in the second stage of training. rats were then exposed to blocks of trials where symmetry probe test trials (B1-A1 and B2- A2) were interspersed with reinforced and non-reinforced baseline trials. The advantages of the methodology for facilitating multi-modal conditional discrimination responding in rats is discussed.

1. In this study, neural network (NN) and response surface (RS) models were developed to investigate the response [average daily gain (ADG) and feed efficiency (FE)] of young broiler chickens to dietary protein and lysine. For this purpose, data on their responses to dietary protein and lysine were extracted from the literature and separate NN and RS models were constructed. 2. Comparison between the NN and RS models revealed higher accuracy of prediction with the NN models compared to the RS models. In terms of R 2 values, the NN models developed for both ADG (R 2 = 0.923) and FE (R 2 = 0.904) were far superior to the RS models (R 2 for ADG = 0.511; R 2 for FE = 0.67). This suggests that the NN models can serve as an alternative option to conventional regression approaches including use of RS models. 3. Optimisation of the NN models developed for response to protein and lysine showed that diets containing 220.7 (g/kg of diet) protein and 12.85 (g/kg of diet) lysine maximise ADG, whereas maximum FE is achieved with diets containing 241.3 and 13.12 (g/kg) protein and lysine, respectively. Based on the optimisation results, optimal dietary protein and lysine concentrations for maximum FE in broiler chickens during the starting period are higher than for ADG.

The article raises the problem of training cadets of the CIS countries who receive higher education in military universities of our country. Russian language proficiency is the main problem, as the training is exclusively in Russian, and as part of a Russian group. Language acquisition occurs gradually during interpersonal communication, and most of all, at classes. One of the first subjects that cadets start studying is mathematics. The main feature of mathematics is the universality of formulas for communication in its language both in the CIS countries and in the world. It greatly simplifies the process of transmitting information to students, allows the teacher to minimize the number of Russian words at the first stages of training (first year of training) gradually raising the volume of definitions and theorems. The great majority of students from the CIS countries freely perceive the lecture and fully assimilate the information even in such a section of «Higher mathematics»as probability theory by the end of the second year. The article shows the transition from the overwhelming number of formulas in the lectures to the prevalence of definitions and theorems. The number of Russian words also increases at practical classes. Their introduction is based on drawings (topic «Analytical geometry»), graphs («Introduction to mathematical analysis», «Definite integral »). The laboratory works are of the greatest interest, of course, where students must draw conclusions about the suitability of the formula obtained as the experiment result. The article details the methodological features of organizing various types of classes using multimedia tools and electronic textbooks in groups with cadets from the CIS countries.

Research on the way first‐year students attempt to solve problems in the first stages of training in calculus has been focused chiefly on the relation between knowledge and heuristic methods. Sometimes an inadequate command of the concepts and characteristics of the specific subject is the main cause of failure to solve ‘new’ problems. Often the approach to problem‐solving is so inadequate that success in coping with ‘new’ problems seems impossible. The role of heuristics should be stressed in the teaching of mathematics to improve the procedures novices follow in solving problems.

BackgroundThis study evaluated whether two selection tests previously validated for primary care General Practice (GP) trainee selection could provide a valid shortlisting selection method for entry into specialty training for the secondary care specialty of radiology.MethodsWe conducted a retrospective analysis of data from radiology applicants who also applied to UK GP specialty training or Core Medical Training. The psychometric properties of the two selection tests, a clinical problem solving (CPS) test and situational judgement test (SJT), were analysed to evaluate their reliability. Predictive validity of the tests was analysed by comparing them with the current radiology selection assessments, and the licensure examination results taken after the first stage of training (Fellowship of the Royal College of Radiologists (FRCR) Part 1).ResultsThe internal reliability of the two selection tests in the radiology applicant sample was good (α ≥ 0.80). The average correlation with radiology shortlisting selection scores was r = 0.26 for the CPS (with p < 0.05 in 5 of 11 shortlisting centres), r = 0.15 for the SJT (with p < 0.05 in 2 of 11 shortlisting centres) and r = 0.25 (with p < 0.05 in 5 of 11 shortlisting centres) for the two tests combined. The CPS test scores significantly correlated with performance in both components of the FRCR Part 1 examinations (r = 0.5 anatomy; r = 0.4 physics; p < 0.05 for both). The SJT did not correlate with either component of the examination.ConclusionsThe current CPS test may be an appropriate selection method for shortlisting in radiology but would benefit from further refinement for use in radiology to ensure that the test specification is relevant. The evidence on whether the SJT may be appropriate for shortlisting in radiology is limited. However, these results may be expected to some extent since the SJT is designed to measure non-academic attributes. Further validation work (e.g. with non-academic outcome variables) is required to evaluate whether an SJT will add value in recruitment for radiology specialty training and will further inform construct validity of SJTs as a selection methodology.

Background: Data mining (DM) is an approach used in extracting valuable information from environmental processes. This research depicts a DM approach used in extracting some information from influent and effluent wastewater characteristic data of a waste stabilization pond (WSP) in Birjand, a city in Eastern Iran. Methods: Multiple regression (MR) and neural network (NN) models were examined using influent characteristics (pH, Biochemical oxygen demand [BOD 5 ], temperature, chemical oxygen demand [COD], total suspended solids [TSS], total dissolved solid [TDS], electrical conductivity [EC] and turbidity) as the regression input vectors. Models were adjusted to input attributes, effluent BOD 5 (BODout) and COD (CODout). The models performances were estimated by 10-fold external cross-validation. An internal 5-fold cross-validation was also used for the training data set in NN model. The models were compared using regression error characteristic (REC) plot and other statistical measures such as relative absolute error (RAE). Sensitivity analysis was also applied to extract useful knowledge from NN model. Results: NN models (with RAE = 78.71 ± 1.16 for BODout and 83.67 ± 1.35 for CODout) and MR models (with RAE = 84.40% ± 1.07 for BODout and 88.07 ± 0.80 for CODout) indicate different performances and the former was better (P < 0.05) for the prediction of both effluent BOD 5 and COD parameters. For the prediction of CODout the NN model with hidden layer size (H) = 4 and decay factor = 0.75 ± 0.03 presented the best predictive results. For BODout the H and decay factor were found to be 4 and 0.73 ± 0.03, respectively. TDS was found as the most descriptive influent wastewater characteristics for the prediction of the WSP performance. The REC plots confirmed the NN model performance superiority for both BOD and COD effluent prediction. Conclusion: Modeling the performance of WSP systems using NN models along with sensitivity analysis can offer better understanding on exploring the most significant parameters for the prediction of system performance. The findings of this study could build the foundation for prospective work on the characterization of WSP operations and optimization of their performances with a view to conducting statistical approaches.