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This study aims to explore how individual personal growth initiative (PGI) mediates the relationship between a positive error orientation and job crafting. Furthermore, it explores the moderating role of the feedback from the leader in this relationship.

Data was collected through a survey conducted on 209 international employees from multiple occupations.

A positive error orientation is indirectly related to job crafting through its relationship with PGI. Also, feedback from leadership has a negative effect on the relationship between a positive error orientation and PGI. Indeed, the mediation effect of PGI on the relationship between a positive error orientation and job crafting loses significance when the leadership feedback is high.

As far as job crafting is concerned, it is essential to develop an error management culture to promote proactive behaviors among individuals.

Although the literature tends to highlight the positive effects of receiving feedback from the leader on employee’s professional development, this paper highlights the potential detrimental effects of leader feedback on PGI, therefore opening a new interesting area that demands attention.

This paper investigates whether error management orientation (EMO) of hospitality employees influence their service recovery performance (SRP) through self-efficacy.

In Study 1, data was collected from 161 hotel managers in the USA. In Study 2, data was collected from 215 restaurant employees in Turkey. Partial least squares (PLS) method using SmartPLS 3.3.3 was used for data analysis.

The results indicated that EMO of hospitality employees increases their self-efficacy beliefs which in turn enhance their SRP. The findings were consistent in both studies.

Hospitality organizations should consider assessing EMO of individuals when making selection decisions. These organizations should also consider providing error management training to employees to develop their EMO, improve error management skills and performance.

To the best of the author’s knowledge, this is the first study that focuses on EMO of hospitality managers and employees. Error orientation refers to how individuals cope with and how they think about errors at work. Errors are part of our work lives, and a positive orientation toward errors (i.e. EMO) can have a significant impact on individuals’ work attitudes, behaviors and performances. This is the first study that examines EMO as an important predictor of SRP. This study also makes a contribution by studying the mediating effect of self-efficacy to understand the underlying mechanism that links EMO with SRP.

The purpose of this paper is to develop a methodology to analyze the total sintering energy (TSE) required for manufacturing a part in metal powder-based additive manufacturing (AM) processes and optimize AM processes for minimizing total energy and form errors of AM parts while maximizing part strength.

The paper uses a computational geometry approach to determine the TSE expended for manufacturing a metal AM part. The stereolithography (STL) file of a part is converted into a voxel data structure and the total sintering volume (TSV) is computed from the voxel representation. The TSE is then calculated from the TSV using the material property information of the metal powder.

The TSE of an AM part is calculated for different slice thickness and part orientations, and the correlation of the total energy to these parameters is calculated. Using these correlations, the AM process is optimized to calculate the optimal values of slice thickness and part orientation which would result in lower process energy, lower part form errors and higher part strength.

The methodology presented in this paper provides AM users a roadmap to predict the energy required for manufacturing a part. In addition, the optimization model will allow engineers to manufacture precision parts which satisfy their design specifications with minimal energy expenditure.

The purpose of this paper is to propose an on-line iterative compensation method combining with a feed-forward compensation method to enhance the assembly accuracy of a metrology-integrated robot system (MIRS).

By the integration of a six degrees of freedom (6DoF) measurement system (T-Mac), the robot’ movement can be tracked with real-time measurement. With the on-line measured data, the proposed iterative compensation for absolute positioning and the feed-forward compensation for relative linear motion are integrated into the assembly process to improve the assembly accuracy.

It is found that the MIRS exhibits good performance in both accuracy and efficiency with the application of the proposed compensation method. With the proposed assembly process, a component can be automatically aligned to the target in seconds, and the assembly error can be decreased to 0.021 mm for position and 0.008° for orientation on average.

This paper presents a 6DoF MIRS for high-precision assembly. Based on the system, a novel on-line compensation method is proposed to enhance the assembly accuracy. In this paper, the assembly accuracy and the corresponding distance parameter are given by a series of experiments as reference for assembly applications.

The purpose of this paper is to propose an error model for serial robot kinematic calibration based on dual quaternions.

The dual quaternions are the combination of dual-number theory and quaternion algebra, which means that they can represent spatial transformation. The dual quaternions can represent the screw displacement in a compact and efficient way, so that they are used for the kinematic analysis of serial robot. The error model proposed in this paper is derived from the forward kinematic equations via using dual quaternion algebra. The full pose measurements are considered to apply the error model to the serial robot by using Leica Geosystems Absolute Tracker (AT960) and tracker machine control (T-MAC) probe.

Two kinematic-parameter identification algorithms are derived from the proposed error model based on dual quaternions, and they can be used for serial robot calibration. The error model uses Denavit–Hartenberg (DH) notation in the kinematic analysis, so that it gives the intuitive geometrical meaning of the kinematic parameters. The absolute tracker system can measure the position and orientation of the end-effector (EE) simultaneously via using T-MAC.

The error model formulated by dual quaternion algebra contains all the basic geometrical parameters of serial robot during the kinematic calibration process. The vector of dual quaternion error can be used as an indicator to represent the trend of error change of robot’s EE between the nominal value and the actual value. The accuracy of the EE is improved after nearly 20 measurements in the experiment conduct on robot SDA5F. The simulation and experiment verify the effectiveness of the error model and the calibration algorithms.

The purpose of this paper is to propose a robot-assisted assembly system (RAAS) for the installation of a variety of small components in the aircraft assembly system. The RAAS is designed to improve the assembly accuracy and increase the productive efficiency.

The RAAS is a closed-loop feedback system, which is integrated with a laser tracking system and an industrial robot system. The laser tracking system is used to evaluate the deviations of the position and orientation of the small component and the industrial robot system is used to locate and re-align the small component according to the deviations.

The RAAS has exhibited considerable accuracy improvement and acceptable assembly efficiency in aircraft assembly project. With the RAAS, the maximum position deviation of the component is reduced to 0.069 mm and the maximum orientation deviation is reduced to 0.013°.

The RAAS is applied successfully in one of the aircraft final assembly projects in southwest China.

By integrating the laser tracking system, the RAAS is constructed as a closed-loop feedback system of both the position and orientation of the component. With the RAAS, the installation a variety of small components can be dealt with by a single industrial robot.

This paper aims to develop an adaptive unscented Kalman filter (AUKF) formulation for orientation estimation of aircraft and UAV utilizing low‐cost attitude and heading reference systems (AHRS).

A recursive least‐square algorithm with exponential age weighting in time is utilized for estimation of the unknown inputs. The proposed AUKF tunes its measurement covariance to yield optimal performance. Owing to nonlinear nature of the dynamic model as well as the measurement equations, an unscented Kalman filter (UKF) is chosen against the extended Kalman filter, due to its better performance characteristics. The unscented transformation of the UKF is shown to equivalently capture the effect of nonlinearities up to second order without the need for explicit calculations of the Jacobians.

In most conventional AHRS filters, severe problems can occur once the system suddenly experiences additional acceleration, resulting in erroneous orientation angles. On the contrary in the high dynamic accelerative mode of the new proposed filter the errors would not suddenly increase, since the additional to cruise accelerations are being continuously estimated resulting in substantially more accurate orientation estimation. This feature causes the associated filter errors to gradually increase, in the event of continuous vehicle acceleration, up to a point of zero additional acceleration that subsequently causes a subsidence of the error back to zero.

The proposed filtering methodology can be implemented for orientation estimation of aircraft and UAV that are equipped with low‐cost AHRSs.

Traditional AHRS algorithms utilize the accelerometers output for the computation of roll and pitch angles and magnetometer output for the heading angle. Moreover, these angles are also calculated from the gyroscopes output as well, but with errors that increase with time. Of course for some applications of AHRS system, orientation errors can be damped out with a proportional‐integral controller. In such a case, the filter cut‐off frequency is usually selected experimentally. But, for high accelerating vehicles utilizing AHRS, the system errors can become very large. A possible remedy to this problem could be to use more advanced nonlinear filter algorithms such as the one proposed.

The purpose of this paper is to describe a calibration method developed to improve the absolute accuracy of a novel three degrees‐of‐freedom planar parallel robot. The robot is designed for the precise alignment of semiconductor wafers and, even though its complete workspace is slightly larger, the accuracy improvements are performed within a target workspace, in which the positions are on a disc of 170 mm in diameter and the orientations are in the range ±17°.

The calibration method makes use of a single optimization model, based on the direct kinematic calibration approach, while the experimental data are collected from two sources. The first source is a measurement arm from FARO Technologies, and the second is a Mitutoyo coordinate measurement machine (CMM). The two sets of calibration results are compared.

Simulation confirmed that the model proposed is not sensitive to measurement noise. An experimental validation on the CMM shows that the absolute accuracy inside the target workspace was improved by reducing the maximum position and orientation errors from 1.432 mm and 0.107°, respectively, to 0.044 mm and 0.009°.

This paper presents a calibration method which makes it possible to accurately identify the actual robot's base frame (base frame calibration), at the same time as identifying and compensating for geometric errors, actuator offsets, and even screw lead errors. The proposed calibration method is applied on a novel planar robot, and its absolute accuracy was found to improve to 0.044 mm.

The purpose of this paper is to develop a means of the kinematic calibration of a parallel manipulator with full-circle rotation.

An error-mapping model based on the space vector chain is formulated and parameter identification is proposed based on double ball-bar (DBB) measurements. The measurement trajectory is determined by the motion characteristics of this mechanism and whether the error sources can be identified. Error compensation is proposed by modifying the inputs, and a two-step kinematic calibration method is implemented.

The simulation and experiment results show that this kinematic calibration method is effective. The DBB length errors and the position errors in the end-effector of the parallel manipulator with full-circle rotation are greatly reduced after error compensation.

By establishing the mapping relationship between measured error data and geometric error sources, the error parameters of this mechanism are identified; thus, the pose errors are unnecessary to be measured directly. The effectiveness of the kinematic calibration method is verified by computer simulation and experiment. This proposed calibration method can help the novel parallel manipulator with full-circle rotation and other similar parallel mechanisms to improve their accuracy.

Presents an on‐line calibration methodology for robot relative positioning inaccuracy. This methodology eliminates the need for time‐consuming off‐line calibrations relying on accurate models and complicated procedures. To realize this methodology, a vision system, a 3D force/torque sensor, and control strategies involving Neural Networks (NNs) were incorporated with an industrial robot.