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This research paper aims to optimize the calibration process for an ABB IRB 120 robot, specifically for robotic orbital milling applications, by introducing and validating the use of the observability index and telescopic ballbar for accuracy enhancement.

The study uses the telescopic ballbar and an observability index for the calibration of an ABB IRB 120 robot, focusing on robotic orbital milling. Comparative simulation analysis selects the O₃ index. Experimental tests, both static and dynamic, evaluate the proposed calibration approach within the robot’s workspace.

The proposed calibration approach significantly reduces circularity errors, particularly in robotic orbital milling, showcasing effectiveness in both static and dynamic modes at various tool center point speeds.

The study focuses on a specific robot model and application (robotic orbital milling), limiting generalizability. Further research could explore diverse robot models and applications.

The findings offer practical benefits by enhancing the accuracy of robotic systems, particularly in precision tasks like orbital milling, providing a valuable calibration method.

While primarily technological, improved robotic precision can have social implications, potentially influencing fields where robotic applications are crucial, such as manufacturing and automation.

This study’s distinctiveness lies in advancing the accuracy and precision of industrial robots during circular motions, specifically tailored for orbital milling applications. The innovative approach synergistically uses the observability index and telescopic ballbar to achieve these objectives.

Because of the key role of joint torque in industrial robots (IRs) motion performance control and energy consumption calculation and efficiency optimization, the purpose of this paper is to propose a deep learning torque prediction method based on long short-term memory (LSTM) recurrent neural networks optimized by particle swarm optimization (PSO), which can accurately predict the the joint torque.

The proposed model optimized the LSTM with PSO algorithm to accurately predict the IRs joint torque. The authors design an excitation trajectory for ABB 1600–10/145 experimental robot and collect its relative dynamic data. The LSTM model was trained with the experimental data, and PSO was used to find optimal number of LSTM nodes and learning rate, then a torque prediction model is established based on PSO-LSTM deep learning method. The novel model is used to predict the robot’s six joint torque and the root mean error squares of the predicted data together with least squares (LS) method were comparably studied.

The predicted joint torque value by PSO-LSTM deep learning approach is highly overlapped with those from real experiment robot, and the error is quite small. The average square error between the predicted joint torque data and experiment data is 2.31 N.m smaller than that with the LS method. The accuracy of the novel PSO-LSTM learning method for joint torque prediction of IR is proved.

PSO and LSTM model are deeply integrated for the first time to predict the joint torque of IR and the prediction accuracy is verified.

The purpose of this paper aims to design a robust wind turbine emulator (WTE) based on a three-phase induction motor (3PIM).

The 3PIM is driven by a soft voltage source inverter (VSI) controlled by a specific space vector modulation. By adjusting the appropriate vector sequence selection, the desired VSI output voltage allows a real wind turbine speed emulation in the laboratory, taking into account the wind profile, static and dynamic behaviors and parametric variations for theoretical and then experimental analysis. A Mexican hat profile and a sinusoidal profile are therefore used as the wind speed system input to highlight the electrical, mechanical and electromagnetic system response.

The simulation results, based on relative error data, show that the proposed reactive power control method effectively estimates the flux and the rotor time constant, thus ensuring an accurate trajectory tracking of the wind speed for the wind emulation application.

The proposed architecture achieves its results through the use of mathematical theory and WTE topology combine with an online adaptive estimator and Lyapunov stability adaptation control methods. These approaches are particularly relevant for low-cost or low-power alternative current (AC) motor drives in the field of renewable energy emulation. It has the advantage of eliminating the need for expensive and unreliable position transducers, thereby increasing the emulator drive life. A comparative analysis was also carried out to highlight the online adaptive estimator fast response time and accuracy.

The research aims to investigate the impact of thermo-mechanical aging on SBR under cyclic-loading. By conducting experimental analyses and developing a 3D finite element analysis (FEA) model, it seeks to understand chemical and physical changes during aging processes. This research provides insights into nonlinear mechanical behavior, stress softening and microstructural alterations in SBR compounds, improving material performance and guiding future strategies.

This study combines experimental analyses, including cyclic tensile loading, attenuated total reflection (ATR), spectroscopy and energy-dispersive X-ray spectroscopy (EDS) line scans, to investigate the effects of thermo-mechanical aging (TMA) on carbon-black (CB) reinforced styrene-butadiene rubber (SBR). It employs a 3D FEA model using the Abaqus/Implicit code to comprehend the nonlinear behavior and stress softening response, offering a holistic understanding of aging processes and mechanical behavior under cyclic-loading.

This study reveals significant insights into SBR behavior during thermo-mechanical aging. Findings include surface roughness variations, chemical alterations and microstructural changes. Notably, a partial recovery of stiffness was observed as a function of CB volume fraction. The developed 3D FEA model accurately depicts nonlinear behavior, stress softening and strain fields around CB particles in unstressed states, predicting hysteresis and energy dissipation in aged SBRs.

This research offers novel insights by comprehensively investigating the impact of thermo-mechanical aging on CB-reinforced-SBR. The fusion of experimental techniques with FEA simulations reveals time-dependent mechanical behavior and microstructural changes in SBR materials. The model serves as a valuable tool for predicting material responses under various conditions, advancing the design and engineering of SBR-based products across industries.

Estimation of solar cell parameters, mathematical modeling and the actual performance analysis of photovoltaic (PV) cells at various ecological conditions are very important in the design and analysis of maximum power point trackers and power converters. This study aims to propose the analysis and modeling of a simplified three-diode model based on the manufacturer’s performance data.

A novel technique is presented to evaluate the PV cell constraints and simplify the existing equation using analytical and iterative methods. To examine the current equation, this study focuses on three crucial operational points: open circuit, short circuit and maximum operating points. The number of parameters needed to estimate these built-in models is decreased from nine to five by an effective iteration method, considerably reducing computational requirements.

The proposed model, in contrast to the previous complex nine-parameter three-diode model, simplifies the modeling and analysis process by requiring only five parameters. To ensure the reliability and accuracy of this proposed model, its results were carefully compared with datasheet values under standard test conditions (STC). This model was implemented using MATLAB/Simulink and validated using a polycrystalline solar cell under STC conditions.

The proposed three-diode model clearly outperforms the earlier existing two-diode model in terms of accuracy and performance, especially in lower irradiance settings, according to the results and comparison analysis.

The purpose of the paper is to propose an efficient and accurate force/torque (F/T) sensing method for the robotic wrist-mounted six-dimensional F/T sensor based on an excitation trajectory.

This paper presents an efficient and accurate F/T sensing method based on an excitation trajectory. First, the dynamic identification model is established by comprehensively considering inertial forces/torques, sensor zero-drift values, robot base inclination errors and forces/torques caused by load gravity. Therefore, the sensing accuracy is improved. Then, the excitation trajectory with optimized poses is used for robot following and data acquisition. The data acquisition is not limited by poses and its time can be significantly shortened. Finally, the least squares method is used to identify parameters and sense contact forces/torques.

Experiments have been carried out on the self-developed robot manipulator. The results strongly demonstrate that the proposed approach is more efficient and accurate than the existing widely-adopted method. Furthermore, the data acquisition time can be shortened from more than 60 s to 3 s/20 s. Thus, the proposed approach is effective and suitable for fast-paced industrial applications.

The main contributions of this paper are as follows: the dynamic identification model is established by comprehensively considering inertial forces/torques, sensor zero-drift values, robot base inclination errors and forces/torques caused by load gravity; and the excitation trajectory with optimized poses is used for robot following and data acquisition.

This study aims to further develop the CLC stage/path’s identification model that distinguishes between path’s emergence (emergence stage), path’s development (growth stage), path’s sustainment (maturity stage), path’s decline (decline stage) and path’s transformation (renewal stage), and by applying it, define the current stage/path of the Demarcated Douro Region (DDR) cluster. The Port wine industry, which is the dominant industry of the DDR cluster, is at the maturity/decline stage – is the same for the cluster itself?

It is a case study with a longitudinal perspective based on the analysis of the dynamics of the parameters of cluster evolution using available secondary sources (cluster identity/brand; number of firms; number of employees; network; innovation; policies and regulations; and external markets – exports), especially addressing the past decade, that represent the stage of maturity/decline of the cluster’s dominant Port wine industry.

The conclusion is that since the 1990s the Demarcated Douro Region has gone through a “path transformation” where during the following 20 years new “anchors” for the cluster were gradually introduced, such as Doc Douro Wines, new forms of consumption of Port wine, tourism and olive oil. Since 2010 the cluster has entered a growth stage/(new) path’s development, where these “anchors” are in steady growth. The Douro brand is becoming more internationally recognized and established, the number of firms and employees is increasing, the network is restructuring with the creation of cluster-specific official institutions, innovation is especially reflected with increasing heterogeneity through diversification of the clusters into new activities and regulations and policies are supportive for expansion – all these parameters are indicating the rise of the new cycle for the cluster. Thus, the DDR cluster represents an attractive business environment and requires attention from regional policymakers to support the cluster’s development. Especially institutions have been highlighted as internal factors driving clusters growth, European integration as an external factor and firms’ strategies of diversification and internationalization as an appropriate de-locking mechanism for new path’s development.

This research contributes to the CLC theory by further developing and applying a CLC stage/path identification model. It provides a better understanding of the dynamics of the DDR cluster that diverge from its dominant industry life cycle, which is relevant for regional policies and firms’ strategies. This study has its limitations. It provides an exploratory application of the theoretical framework proposed, and consequently, no general conclusions are possible yet. More empirical studies with different clusters in different stages are necessary to test the framework.

These findings are useful to policymakers when designing their policies for cluster development but also for clusters’ entities and actors when making their strategic decisions as it allows based on the verification of the established parameter of CLC to identify its current stage/path of development.

The paper presents a theoretically grounded model for CLC identification and for the first time to the best of the authors’ knowledge applies it to a cluster case – the DDR cluster. This case applies the proposed model and illustrates its usefulness. The model provides the tools for a better understanding of cluster dynamics.

Globally, the governance has shifted from positivist to the regulatory-centric approach, necessitating accurate contouring of regulatory governance framework. The study proposes a novel approach to unravel the regulatory governance framework in the context of the Indian electronics industry – extendable to other sectors in India and other emerging economies.

The research objective has been operationalized through document analysis and thematic analysis of semi-structured interview transcripts in three steps: (1) arrive at parameters of the regulatory governance framework, (2) identify instruments against each parameter and (3) characterize parameters in terms of dominant instruments and their underlying modalities. The authors have adopted a set of 6 Cs modalities (control, communications, competition, consensus, code and collaboration) and regulatory space theory to analyze existing modalities mix in the dominant instruments.

In summary, the study has (1) identified eight macro and twenty micro regulatory governance parameters, (2) mapped regulatory governance parameters with instruments and institutions (3) revealed the top two dominant modalities for each regulatory governance parameter.

The existing modality characteristics of regulatory governance parameters can be used by manufacturers, investors and other stakeholders to make a realistic assessment of regulatory governance and reduce regulatory risk and regulatory burden.

The multidimensional use of parameters, instruments and modalities broadens the understanding of the existing regulatory governance framework and may assist the regulators in optimizing it to meet market requirements.

The dispute resolution process in the construction industry is known for delays in settlement, with some cases even escalating to complex arbitration and litigation. To avoid conflicts turning into disputes, the parties need to be proactive in identifying and resolving conflicts in their nascent stages. It is here that innovative lean construction practices can potentially act as a game-changer to avoid disputes, and this study aims to attempt to understand this phenomenon empirically.

A questionnaire-based empirical study, followed by semi-structured interviews, is conducted to understand the relevance of key tenets of lean principles in dispute avoidance.

Although stakeholders agree on the usefulness and practicality of lean principles in dispute avoidance, the extent of agreement is lesser when it comes to its implementation practicality. Moreover, there is a demographic influence observed on lean tenets such as “open communication”, “stakeholder collaboration” and “constraint identification”.

The results point towards an approach that combines contractual mandate, training and awareness creation to iron out the differences in the usefulness and practicality of lean approaches to avoid disputes.

Lean implementation is widely discussed in many construction contexts, such as sustainability, productivity improvement and planning. However, a discussion on lean philosophy’s role in dispute avoidance is muted. Therefore, this study assumes significance.

The aim of this study is to investigate and clarify how the triple helix actors can effectively implement the concepts of Kaizen to navigate and overcome the complex obstacles brought on by the global COVID-19 pandemic.

Through broad literature reviews, nine common parameters under triple helix actor have been recognized. A regression analysis has been done to study how the triple helix actors’ common parameters impact Kaizen implementation in business operations.

The results of this study revealed insightful patterns in the relationships between the common parameters of triple helix actor and the dependent variables. Notably, the results also showed that leadership commitment (LC) emerges as a very significant component, having a big impact on employee engagement as well as organizational performance.

In addition to offering valuable insights, this study has limitations including the potential for response bias in survey data and the focus on a specific set of common parameters, which may not encompass the entirety of factors influencing Kaizen implementation within the triple helix framework during the pandemic.

The originality of this study lies in its comprehensive exploration of the interplay between triple helix actors and Kaizen principles in addressing COVID-19 challenges. By identifying and analyzing nine specific common parameters, the study provides a novel framework for understanding how triple helix actors collaboratively enhance organizational performance and employee engagement during challenging times.