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This paper aims to propose a novel model and calibration method to improve the absolute positioning accuracy of a robotic drilling system with secondary encoders and additional axis.

The enhanced rigid-flexible coupling model is developed by considering both kinematic parameters and link flexibility. The kinematic errors of the robot and the additional axis are considered with a model containing 27 parameters. The elastic deformation errors of the robot under self-weight of links and end-effector are estimated with a flexible link model. For calibration, an effective comprehensive calibration method is developed by further considering the coordinate systems parameters of the drilling system and using a two-step process constrained Levenberg–Marquardt identification method.

Experiments are performed on the robotic drilling system that contains a KUKA KR500 R2830 industrial robot and an additional lifting axis with a laser tracker. The results show that the maximum error and mean error are reduced to 0.311 and 0.136 mm, respectively, which verify the effectiveness of the model and the calibration method.

A novel enhanced rigid-flexible coupling model and a practical comprehensive calibration method are proposed and verified. The experiments results indicate that the absolute positioning accuracy of the system in a large workspace is greatly improved, which is conducive to the application of industrial robots in the field of aerospace assembly.

The learning ability on critical bargaining information contributes to accelerating construction claim negotiations in the win-win situation. The purpose of this paper is to study how to apply Zeuthen strategy and Bayesian learning to simulate the dynamic bargaining process of claim negotiations with the consideration of discount factor and risk attitude.

The authors first adopted certainty equivalent method and curve fitting to build a party’s own curve utility function. Taking the opponent’s bottom line as the learning goal, the authors introduced Bayesian learning to refine former predicted linear utility function of the opponent according to every new counteroffer. Both parties’ utility functions were revised by taking discount factors into consideration. Accordingly, the authors developed a bilateral learning model in construction claim negotiations based on Zeuthen strategy.

The consistency of Zeuthen strategy and the Nash bargaining solution model guarantees the effectiveness of the bilateral learning model. Moreover, the illustrative example verifies the feasibility of this model.

As the authors developed the bilateral learning model by mathematical deduction, scholars are expected to collect empirical cases and compare actual solutions and model solutions in order to modify the model in future studies.

Negotiators could refer to this model to make offers dynamically, which is favorable for the parties to reach an agreement quickly and to avoid the escalation of claims into disputes.

The proposed model provides a supplement to the existing studies on dynamic construction claim negotiations.

The purpose of this paper is to develop nitrogen-enriched carbon (NC) with high conductivity and specific capacitance as electrode materials for supercapacitors.

Graphene oxide (GO) was synthesized by the modified Hummers–Offeman method. NC was synthesized by carbonization of melamine formaldehyde resin/graphene oxide (MF/GO) composites. Supercapacitors based on Ni(OH)2/Co(OH)2 composites as the positive electrode and NC as the negative electrode were assembled. The electrochemical performances of NC and supercapacitors are studied.

The results show that obtained NC has high nitrogen content. Compared to NC-GO0 without GO, high conductivity and specific capacitance were obtained for NC with GO due to the introduction of layered GO. The presence of pseudocapacitive interactions between potassium cations and the nitrogen atoms of NC was also proposed. When the weight ratio of GO to MF is 0.013:1, the obtained NC-GO3 has the highest specific capacitance of 154.07 F/g due to GO and its highest content of N-6. When the P of the asymmetric supercapacitor with NC-GO3 as the negative electrode is 1,326.70 W/kg, its Cps and Ep are still 23.84 F/g and 8.48 Wh/Kg, respectively. There is only 4.4 per cent decay in Cps of the supercapacitor over 1,000 cycles.

NC is a suitable electrode material for supercapacitors. The supercapacitors can be used in the field of automobiles and can solve the problems of energy shortage and environmental pollutions.

NC based on MF/GO composites with high nitrogen content and conductivity was novel and its electrochemical properties were excellent.

Wire and arc additive manufacturing (WAAM) technology-based cold metal transfer (CMT) to produce large aluminum alloy parts has become more and more popular. In WAAM, wire is the only raw material. The purpose of this paper is to study the effect of wire composition on the microstructure and properties of the ZAlCu5MnCdVA alloy deposited by WAAM.

Two thin-walled ZAlCu5MnCdVA alloys with different wire compositions were prepared by WAAM. The copper contents were 4.7% (Al-4.7Cu) and 5.0% (Al-5.0Cu), respectively. The microstructure, element distribution and evolution of precipitated phases of the two samples were characterized and analyzed by optical microscopy, scanning electron microscopy and transmission electron microscopy. Hardness and tensile properties of samples were tested, and strengthening mechanism was analyzed in detail.

The results show that grain sizes of Al-4.7Cu and Al-5.0Cu are less than 40 μm. The average mass fraction of Cu in Al matrix and the number of nanometer scale θ'' and θ' phases are the main factors affecting the tensile properties of Al-Cu alloy. Tensile properties of two materials show different characteristics at room temperature and high temperature. Al-5.0Cu is better at room temperature and Al-4.7Cu is better at high temperature. The yield strength (YS), ultimate tensile strength (UTS) and elongation in the x direction of Al-5.0Cu at room temperature are 451 ± 10.2 MPa, 486 ± 10.2 MPa and 9 ± 0.5%, respectively. The YS, UTS and elongation in the x direction of Al-4.7Cu at high temperature are 290 ± 4.5 MPa, 356 ± 7.0 MPa and 13% ± 0.2%, respectively.

Experiments show that the increase of Cu element can improve the properties at room temperature of the ZAlCu5MnCdVA alloy by WAAM, but its properties at high temperature decrease.

The purpose of this paper is to use He's Exp‐function method (EFM) to construct solitary and soliton solutions of the nonlinear evolution equation.

This technique is straightforward and simple to use and is a powerful method to overcome some difficulties in the nonlinear problems.

This method is developed for searching exact traveling wave solutions of the nonlinear partial differential equations. The EFM presents a wider applicability for handling nonlinear wave equations.

The paper shows that EFM, with the help of symbolic computation, provides a straightforward and powerful mathematical tool for solving nonlinear evolution equations. Application of EFM to Fitzhugh‐Nagumo equation illustrates its effectiveness.

The purpose of this study is to improve the accuracy of industrial robot kinematic parameter identification and position accuracy by solving the problem of insufficient consideration of error sources in the kinematic parameter identification model and optimizing the selection of measurement pose set.

In this study, a kinematic calibration method for industrial robots considering multiple error sources is proposed. Based on the Modified Denavit Hartenberg (MD-H) model, a robot kinematics identification model including joint reduction ratio error, target ball installation error and coordinate system transformation error is established. Taking the optimal observability index O1 and the minimum flexible deformation as the optimization objectives, a measurement pose set optimization method based on Non-dominated Sorting Genetic Algorithm II (NSGA-II) is proposed to obtain a measurement pose set with higher identification accuracy.

Through experiments conducted with the Nantong Zhenkang ZK1400-6 robot as the test subject, the kinematic parameters identified by the optimized measurement pose set are more accurate than the randomly selected measurement pose set, and the positioning accuracy of the robot is improved from 2.11 to 0.31 mm, an increase of 85.3%.

This study introduces a position error model that comprehensively accounts for the error sources causing positioning inaccuracies. Building on this foundation, a novel flexible deformation index is proposed to quantify the flexible deformation in the measurement pose set, thereby reducing the impact of such deformation on the position error in the model. To the best of the authors’ knowledge, for the first time, this study presents an optimization method for the measurement pose set based on NSGA-II, using the flexible deformation index and observability index as objectives for multi-objective optimization, simultaneously optimizing the pose error and Jacobian matrix in the error model.

In typical model-based calibration, linearization errors are derived inevitably, and non-negligible negative impact will be induced on the identification results if the rotational kinematic errors are not small enough or the lengths of links are too long, which is common in the industrial cases. Thus, an accurate two-step kinematic calibration method minimizing the linearization errors is presented for a six-DoF serial robot to improve the calibration accuracy.

The negative impact of linearization on identification accuracy is minimized by removing the responsible linearized kinematic errors from the complete kinematic error model. Accordingly, the identification results of the dimension-reduced new model are accurate but not complete, so the complete kinematic error model, which achieves high identification accuracy of the rest of the error parameters, is combined with this new model to create a two-step calibration procedure capable of highly accurate identification of all the kinematic errors.

The proportions of linearization errors in measured pose errors are quantified and found to be non-negligible with the increase of rotational kinematic errors. Thus, negative impacts of linearization errors are analyzed quantitatively in different cases, providing the basis for allowed kinematic errors in the new model. Much more accurate results were obtained by using the new two-step calibration method, according to a comparison with the typical methods.

This new method achieves high accuracy with no compromise on completeness, is easy to operate and is consistent with the typical method because the second step with the new model is conveniently combined without changing the sensors or measurement instrument setup.

The increasing popularity of music streaming services also increases the need to customize the services for each user to attract and retain customers. Most of the music streaming services will not have explicit ratings for songs; they will have only implicit feedback data, i.e user listening history. For efficient music recommendation, the preferences of the users have to be infered, which is a challenging task.

Preferences of the users can be identified from the users' listening history. In this paper, a hybrid music recommendation system is proposed that infers features from user's implicit feedback and uses the hybrid of content-based and collaborative filtering method to recommend songs. A Content Boosted K-Nearest Neighbours (CBKNN) filtering technique was proposed, which used the users' listening history, popularity of songs, song features, and songs of similar interested users for recommending songs. The song features are taken as content features. Song Frequency–Inverse Popularity Frequency (SF-IPF) metric is proposed to find the similarity among the neighbours in collaborative filtering. Million Song Dataset and Echo Nest Taste Profile Subset are used as data sets.

The proposed CBKNN technique with SF-IPF similarity measure to identify similar interest neighbours performs better than other machine learning techniques like linear regression, decision trees, random forest, support vector machines, XGboost and Adaboost. The performance of proposed SF-IPF was tested with other similarity metrics like Pearson and Cosine similarity measures, in which SF-IPF results in better performance.

This method was devised to infer the user preferences from the implicit feedback data and it is converted as rating preferences. The importance of adding content features with collaborative information is analysed in hybrid filtering. A new similarity metric SF-IPF is formulated to identify the similarity between the users in collaborative filtering.

This study aims to evaluate cargo and eco-efficiency of global container shipping companies (CSCs) and explore the determinants of the CSCs' efficiencies. While the former is derived from the CSCs' operational perspective, the latter highlights environmental issue related to carbon emission reduction.

In the first stage, a two-stage double bootstrap approach of data envelopment analysis (DEA) is applied to derive bias-corrected cargo and eco-efficiency of the top ten global CSCs under the variable returns to scale assumption. In the second stage, ordinary least squares and truncated regression are applied to examine determinants of the CSCs' efficiencies.

The DEA results reveal that the cargo efficiency of the CSCs is higher than their eco-efficiency by about 2.6% under variable returns to scale in DEA. However, the bias-corrected results show that the difference is 2.9%. The overall average efficiencies suggest that the CSCs can improve their cargo (eco) efficiency by 6.9% (10.8%). In the second stage, the regression results show that the numbers of ship, return on assets and asset turnover ratio are significantly related to both cargo and eco-efficiencies, whereas the total fleet capacity positively affects cargo efficiency.

The results of this study can help the inefficient CSCs make strategic decisions to improve their performance. For example, their business experience and capacity may be contributing to their efficiencies. However, this study only focuses on the container market among the three main markets, namely, dry bulk, wet bulk and container.

This study highlights an environmental issue in the shipping industry. While CSCs are operating their cargo efficiently in general, they should also put green initiatives into their business operations for the long-term sustainability.

This paper aims to mainly report the impact of torch angle on the dynamic behavior of the weld pool which is recorded and monitored in real time with the aid of a high-speed camera system. The influence of depositing torch angle on the fluctuation behavior of weld pool and the quality of weld formation are compared and analyzed.

The FANUC controlled robotic manufacturing system comprised a Fronius cold metal transfer (CMT) Advanced 4000R power source, FANUC robot, water cooling system, wire feeding system and a gas shielding system. An infrared laser was used to illuminate the weld pool for high-speed imaging at 1,000 frames per second with CR600X2 high-speed camera. The high-speed camera was set up a 35 ° angle with the deposition direction to investigate the weld pool flow patterns derived from high-speed video and the effect of torch angles on the first layer of wire additive manufacture-CMT.

The experimental results demonstrated that different torch angles significantly influence on the deposited morphology, porosity formation rate and weld pool flow.

With regard to the first layer of wire arc additive manufacture of aluminum alloys, the change of torch angle is critical. It is clear that different torch angles significantly influence on the weld morphology, porosity formation and weld pool flow. Furthermore, under different torch angles, the deposited beads will produce different defects. To get well deposited beads, 0-10° torch could be made away from the vertical position of the deposition direction, in which the formation of deposited beads were well and less porosity and other defects.