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The purpose of this paper is to search the optimal arrangement scheme of random motion accuracy of joints for optimal synthesis of pose repeatability which can make robot design more reasonable and reduce the development cost of robots.

In this paper, a mathematical model of pose repeatability, which includes positioning repeatability and orientation repeatability of robots, is established. According to the ISO 9283 standard, an optimal synthesis method of pose repeatability for collaborative robots is introduced, and three optimization objective functions are proposed. The optimization model is solved by using numerical analysis software, and the optimal arrangement scheme of random motion accuracy of joints is obtained which meets the requirements of pose repeatability of robot.

It is found that, in three optimization objective functions, the single-objective evaluation function of maximization of joint motion error is more suitable for optimal synthesis of pose repeatability. In practice, due to the safety factor, the test results of pose repeatability are better than the results of optimal synthesis of pose repeatability.

This method makes robot design more reasonable and reduces the development cost of robots.

This work is the first time to optimize the orientation repeatability of collaborative robots. Because the pose repeatability of most robots is tested by the ISO 9283 standard, so this method which is based on this standard is more suitable for the performance requirements of robot products.

This purpose of this paper is to present an optimization‐based approach to support the design of attribute sampling plans for lot acceptance purposes, with the fraction of non‐conforming items being modeled by a Poisson probability distribution function.

The paper approach stands upon the minimization of the error of the probability of acceptance equalities in the controlled points of the operating curve (OC) with respect to sample size and acceptance number. It was applied to simple and double sampling plans, including several combinations of quality levels required by the producer and the consumer. Formulation of the design of acceptance sampling plans as an optimization problem, having as a goal the minimization of the squared error at the controlled points of the OC curve, and its subsequent solution employing GAMS.

The results are in strong agreement with acceptance sampling plans available in the open literature. The papers approach in some scenarios outperforms classical sampling plans and allows one to identify the lack of feasible solutions.

An optimization‐based approach to support the design of acceptance sampling plans for attributes was conceived and tested. It allows for a general treatment of these problems, including the identification of a lack of feasible solutions, as well as making possible the determination of feasible alternatives by relaxing some model constraints.

The purpose of this paper is to report experimental investigations performed to analyze the effect of process parameters on the shape accuracy of selective laser sintered (SLS) parts.

The effect of process parameters, namely build orientation, laser power, scan speed, cylinder diameter and build chamber temperature has been studied on shape accuracy by using geometric tolerances such as cylindricity and flatness. Central composite design (CCD) is used to plan the experiments and a second order regression model has been developed to predict flatness and cylindricity. The significance of process variables on flatness and cylindricity has been evaluated using analysis of variance technique.

It is observed that interaction effects are more dominant than individual effects. In case of cylindricity, it is found that the interaction between the scan speed and orientation is the dominant factor next to the orientation and quadratic effect of the geometry. In case of flatness, the interaction between build chamber temperature and scan speed is the dominant factor.

The empirical models presented in this paper work within the range of values used for the experiments and most of these models need to be redeveloped for use with other materials.

The empirical models developed in this work would be useful in deciding the process parameters for parts with improved geometrical tolerances. The optimum parameters identified from the empirical model are found to yield accurate parts with minimum shape error.

The paper establishes the interactions between this build orientation, geometry and process parameters on the shape accuracy of SLS process.

Data mining plays a major role in forecasting the open price details of the stock market. However, it fails to address the dimensionality and expectancy of a naive investor. Hence, this paper aims to study a future prediction model named time series model is implemented.

In this model, the stock market data are fed to the proposed deep neural networks (DBN), and the number of hidden neurons is optimized by the modified JAYA Algorithm (JA), based on the fitness function. Hence, the algorithm is termed as fitness-oriented JA (FJA), and the proposed model is termed as FJA-DBN. The primary objective of this open price forecasting model is the minimization of the error function between the modeled and actual output.

The performance analysis demonstrates that the deviation of FJA–DBN in predicting the open price details of the Tata Motors, Reliance Power and Infosys data shows better performance in terms of mean error percentage, symmetric mean absolute percentage error, mean absolute scaled error, mean absolute error, root mean square error, L1-norm, L2-Norm and Infinity-Norm (least infinity error).

The proposed model can be used to forecast the open price details.

The investors are constantly reviewing past pricing history and using it to influence their future investment decisions. There are some basic assumptions used in this analysis, first being that everything significant about a company is already priced into the stock, other being that the price moves in trends

This paper presents a technique for time series modeling using JA. This is the first work that uses FJA-based optimization for stock market open price prediction.

A local optimisation method for obtaining material parameters in finite element simulations has been developed. The method is based on the minimisation of an error function which reflects the accuracy of a numerical prediction with respect to the results of simple specimen tests. The experimental data were obtained from high strain rate tensile tests on the alloy 90 per cent titanium – 6 per cent aluminium – 4 per cent vanadium (Ti6Al4V) using the tensile split‐Hopkinson pressure bar. The behaviour of the tensile specimen was monitored during the test using high‐speed photography and transient recorders. Finite element simulations were performed using ABAQUS/Explicit employing the Zerilli‐Armstrong material model for bcc metals).

The purpose of this paper is to propose an optimal predictive model for the short-term forecast of real-time non-stationary machine variables by combining time series prediction with adaptive algorithms to minimize the error and to improve the prediction accuracy.

The proposed model is applied for prediction of speed and controller set point of three-phase induction motor operating on closed loop speed control with AC drive and PI controller. At Stage 1, the trend of the machine variables has been extracted and added to auto-regressive moving average (ARMA) time series prediction. ARMA prediction has been carried out using different combinations of AR and MA methods in order to make prediction with less Mean Squared Error (MSE).

The prediction error indicates the inadequacy of the model to estimate the data characteristics, which has been resolved at the subsequent stage by cascading an adaptive least mean square finite impulse response filter to the time series model. The adaptive filter receives the predicted output including training data and iteratively adjusts its coefficients for zero error convergence.

The componentized data prediction based on time series and cascade adaptive filter algorithm decomposes the non-stationary data characteristics for predictive maintenance. Evaluation of the model with different combination of time series algorithms and parameter settings of adaptive filter has been carried out to illustrate the performance of the prediction model. This prediction accuracy is compared with existing linear adaptive filter prediction using MSE as comparison index. The wide margin in the MSE values substantiates the prediction efficiency of the proposed model for machine data.

This model predicts the dynamic machine data with component decomposition at high accuracy, which enables to interpret the system response under dynamic conditions efficiently.

The industrial robot has high repeatability but low accuracy. With the industrial robot being widely used in complicated tasks, e.g. arc welding, offline programming and surgery, accuracy of the robot is more and more important. Robot calibration is an efficient way to improve the accuracy. Previous methods such as using coordinate measurement machines, laser trackers or cameras are limited by the cost, complex operation or the resolution. The purpose of this paper is to propose an approach and calibration equipment to address these issues.

The proposed method relies mainly upon a laser pointer attached on the end‐effector and single position‐sensitive devices (PSD) arbitrarily located on the workcell. The automated calibration procedure (about three minutes) involves aiming the laser lines loaded by the robot towards the center of the PSD surface from various robot positions and orientations. The localization is guaranteed by precise PSD feedback servoing control, which means physically the intersections of each pair of laser lines (virtual lines) are on the same point. Based on the untouched single‐point constraint, the robot joint offset calibration is implemented. Using the authors' proposed approach, a portable, low‐cost, battery‐powered, wireless and automated calibration system was implemented. Error analysis was conducted on the system.

The localization error of the developed calibration system is within 2 μm. The errors in joint space are magnified in PSD plane, and consequently the resolution in the joint space is improved. The standard deviation of the identified parameters was small (10‐2), indicating the stability of the calibration method. Both simulation and experimental results verify the feasibility of the proposed method and demonstrate the developed calibration system can identify joint offset with uncalibrated laser tool parameters.

The paper shows how a portable calibration system for joint offset of industrial robots was developed and how the goal of fast, automated, low‐cost, portable, and high precision calibration methods for joint offset was achieved.

A concise introduction to the normalized quadratic expenditure or cost function is provided so that the interested reader will have the necessary information to understand and use this functional form. The normalized quadratic is an attractive functional form for use in empirical applications as correct curvature can be imposed in a parsimonious way without losing the desirable property of flexibility. We believe it is unique in this regard. Topics covered include the problem of cardinalizing utility, the modeling of nonhomothetic preferences, the use of spline functions to achieve greater flexibility, and the use of a “semiflexible” approach to make it feasible to estimate systems of equations with a large number of commodities.

Advanced heavy water reactor (AHWR) is a pressure tube type of heavy water reactor. It eliminates high-pressure heavy water coolant resulting in a reduction of heavy water leakage losses and eliminating heavy water recovery system. It recovers the heat generated in the moderator for feed water heating. However, it requires a satisfactory technological response to develop an effective controller that attains the challenges of the very high-level safety system. Hence, they require application-specific improvement for better controlling performance.

The purpose of this study intends to propose a system for controlling state vectors v₁ and v₂and in AHWR using Grey Wolf second-order sliding mode control (GW-SoSMC) technique. The main aim of the paper is to minimize the errors between the predicted and desired azimuthal angles of the system. With this proposed method, it is possible to mitigate both the chattering phenomenon and controlling performance of AHWR system. It implements a SoSMC controller based on GWO algorithm for the purpose of controlling the state vectors in the AHWR system. It aims to accomplish a controller for improving the performance of the AHWR system.

Through the performance analysis, the efficiency of the proposed GW-SoSMC technique was verified by comparing it with various conventional algorithms, such as GW-SMC, FF-SoSMC, ABC-SoSMC, GS-SoSMC and GA-SoSMC. From the analysis, it was obtained that the implemented GW-SoSMC technique was 65.3 per cent superior to GW-SMC, 65.32 per cent superior to both FF-SoSMC and 65 per cent superior to ABC-SoSMC, 65.8 per cent superior to the GS-SoSMC and 58 per cent superior to the GA-SoSMC methods. Thus, the effectiveness of the proposed method in controlling the state vectors in AHWR was obtained.

This paper presents a technique for controlling the state vectors in the AHWR system using GWO algorithm. This is the first work that uses GWO-based optimization for controlling state vectors in the AHWR system.

This study aims to dissect the multifaceted impact of ISO/IEC 17025 accreditation, specifically within civil engineering testing and calibration laboratories. To achieve this, it intends to explore several key objectives: identifying the prominent benefits of accreditation to laboratory performance, understanding the advantages conferred through participation in proficiency testing schemes, assessing the role of accreditation in enhancing laboratory competitiveness, examining the primary challenges encountered during the accreditation process, investigating any discernible adverse effects of accreditation on laboratory performance and evaluating whether the financial cost of accreditation justifies the resultant profitability.

This study employs a qualitative approach through semi-structured interviews with 23 industry professionals—including technical managers, quality managers, external auditors and clients. Thematic analysis, guided by Braun and Clarke’s six-stage paradigm, was utilized to interpret the data, ensuring a comprehensive understanding of the accreditation’s impact.

Findings reveal that accreditation significantly enhances operational processes, fosters quality awareness and facilitates continuous improvement, contributing to greater client satisfaction. In addition, standardized operations and rigorous quality controls further result in enhanced performance metrics, such as staff capability and measurement accuracy. However, the study also uncovers the challenges of accreditation, including high resource costs and bureaucratic hurdles that can inhibit innovation and slow routine operations. Importantly, the research underscores that the impact of accreditation on profitability is not universal, but contingent upon various factors like sector-specific regulations and market demand. The study also highlights sector-specific variations in the role of accreditation as a marketing tool and differing perceptions of its value among clients. It further emphasizes the psychological stress of high-stakes evaluations during audits.

This study represents the first in-depth investigation into the impact of ISO/IEC 17025 accreditation on civil engineering testing and calibration laboratories, directly contributing to the enhancement of their quality and operational standards. Providing actionable insights for laboratories, it underscores the importance of weighing accreditation costs and benefits and the necessity for a tailored approach to the unique market and regulatory landscapes they operate in.