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Piezoelectric actuators, widely used in micro-positioning and active control systems, show important hysteresis characteristics. In particular, the hysteresis contribution is a complex phenomenon that is difficult to model when the input amplitude and frequency are time-dependent. Existing dynamic physical models poorly describe the hysteresis influence of industrial mechatronic devices. This paper proposes a novel hybrid data-driven model based on the Bouc–Wen and backlash hysteresis formulations to appraise and compensate for the nonlinear effects. Firstly, the performance of the piezoelectric actuator was simulated and then tested in a complete representative domain, and then using the committee machine approach. Experimental campaigns were conducted to develop an algorithm that incorporated Bouc–Wen and backlash hysteresis parameters derived via genetic algorithm (GA) and particle swarm optimization (PSO) approaches for identification. These parameters were combined in a committee machine using a set of frequency clusters. The results obtained demonstrated an error reduction of 23.54% for the committee machine approach compared with the complete approach. The root mean square error (RMSE) was 0.42 µm, and the maximum absolute error (MAE) appraisal was close to 0.86 µm in the 150–250 Hz domain via the Bouc–Wen sub-model tuned with the genetic algorithm (GA).

This article aims to give an overall review of research status in hand rehabilitation robotic technology, evaluating a number of devices. The main scope is to explore the current state of art to help and support designers and clinicians make better choices among varied devices and components. The review also focuses on both mechanical design, usability and training paradigms since these parts are interconnected for an effective hand recovery. In order to study the rehabilitation robotic technology status, the devices have been divided in two categories: end-effector robots and exoskeleton devices. The end-effector robots are more flexible than exoskeleton devices in fitting the different size of hands, reducing the setup time and increasing the usability for new patients. They suffer from the control of distal joints and haptic aspects of object manipulation. In this way, exoskeleton devices may represent a new opportunity. Nevertheless their design is complex and a deep investigation of hand biomechanics and physical human–robot interaction is required. The main hand exoskeletons have been developed in the last decade and the results are promising demonstrated by the growth of the commercialized devices. Finally, a discussion on the complexity to define which design is better and more effective than the other one is summarized for future investigations.

This study provides a structured literature review of the recent COllaborative roBOT (COBOT) applications in industrial and service contexts. Several papers and research studies were selected and analyzed, observing the collaborative robot interactions, the control technologies and the market impact. This review focuses on stationary COBOTs that may guarantee flexible applications, resource efficiency, and worker safety from a fixed location. COBOTs offer new opportunities to develop and integrate control techniques, environmental recognition of time-variant object location, and user-friendly programming to interact safely with humans. Artificial Intelligence (AI) and machine learning systems enable and boost the COBOT’s ability to perceive its surroundings. A deep analysis of different applications of COBOTs and their properties, from industrial assembly, material handling, service personal assistance, security and inspection, Medicare, and supernumerary tasks, was carried out. Among the observations, the analysis outlined the importance and the dependencies of the control interfaces, the intention recognition, the programming techniques, and virtual reality solutions. A market analysis of 195 models was developed, focusing on the physical characteristics and key features to demonstrate the relevance and growing interest in this field, highlighting the potential of COBOT adoption based on (i) degrees of freedom, (ii) reach and payload, (iii) accuracy, and (iv) energy consumption vs. tool center point velocity. Finally, a discussion on the advantages and limits is summarized, considering anthropomorphic robot applications for further investigations.

This study aims to present an overall review of the recent research status regarding Machine Learning (ML) applications in machining processes. In the current industrial systems, processes require the capacity to adapt to manufacturing conditions continuously, guaranteeing high performance in terms of production quality and equipment availability. Artificial Intelligence (AI) offers new opportunities to develop and integrate innovative solutions in conventional machine tools to reduce undesirable effects during operational activities. In particular, the significant increase of the computational capacity may permit the application of complex algorithms to big data volumes in a short time, expanding the potentialities of ML techniques. ML applications are present in several contexts of machining processes, from roughness quality prediction to tool condition monitoring. This review focuses on recent applications and implications, classifying the main problems that may be solved using ML related to the machining quality, energy consumption and conditional monitoring. Finally, a discussion on the advantages and limits of ML algorithms is summarized for future investigations.

Robot acceptance is rapidly increasing in many different industrial applications. The advancement of production systems and machines requires addressing the productivity complexity and flexibility of current manufacturing processes in quasi-real time. Nowadays, robot placement is still achieved via industrial practices based on the expertise of the workers and technicians, with the adoption of offline expensive software that demands time-consuming simulations, detailed time-and-motion mapping activities, and high competencies. Current challenges have been addressed mainly via path planning or robot-to-workpiece location optimization. Numerous solutions, from analytical to physical-based and data-driven formulation, have been discussed in the literature to solve these challenges. In this context, the machine learning approach has proven its superior performance. Nevertheless, the industrial environment is complex to model, generating extra training effort and making the learning procedure, in some cases, inefficient. The industrial problems concern workstation productivity; path-constrained minimal-time motions, considering the actuator’s torque limits; followed by robot vibration and the reduction in its accuracy and lifetime. This paper presents a procedure to find the robot base location for a prescribed task within the robot’s workspace, complying with multiple criteria. The proposed hybrid procedure includes analytical, physical-based, and data-driven modeling to solve the optimization problem. The contribution of the algorithm, for a given user-defined task, is the search for the best robot base location that enables the target points, maximizing the manipulability, avoiding singularities, and minimizing energy consumption. Firstly, the established method was verified using an anthropomorphic robot that considers different levels of a priori kinematics and system dynamics knowledge. The feasibility of the proposed method was evaluated through various simulations for small- and medium-sized robots. Then, a commercial offline program was compared, considering three scenarios and fourteen robots demonstrating an energy reduction in the 7.6–13.2% range. Moreover, the unknown joint dependency in real robot applications was investigated. From 11 robot positions for each active joint, a direct kinematic was appraised with an automatic DH scheme that generates the 3D workspace with an RMSE lower than 65.0 µm. Then, the inverse kinematic was computed using an ANN technique tuned with a genetic algorithm showing an RMSE in an S-shape task close to 702.0 µm. Finally, three experimental campaigns were performed with a set of tasks, repetitions, end-effector velocity, and payloads. The energy consumption reduction was observed in the 12.7–22.9% range. Consequently, the proposed procedure supports the reduction in workstation setup time and energy saving during industrial operations.

The paper aims to present a mechatronic device able to micro-position the workpiece and to reject disturbances due to machining operation. A decoupling method is proposed for a parallel kinematic machine (PKM) fixturing platform composed by a 3-DoF flexure-based piezo-actuated mechanism. The parallel platform, with a vertical motion and two rotations, is described and its kinematics and dynamics are studied. The coupling undesirable effect is investigated based on a set of poses. To improve the quasi-static regulator model for a set-point following system, a bump less switching controller and a fine-tuning procedure, to estimate the parameter uncertainty and enable the external disturbance containment in an extended broadband frequency range, are presented. The platform and the piezo-actuator controllers are modelled based on a gain scheduling, standard ISA form method, to guarantee the stability. The accuracy is demonstrated through a set of simulations and experimental comparisons. A sensitivity analysis that evaluates the tracking performance and the disturbance rejection based on the number of signal amplitudes, frequencies, and phases is discussed. A validation phase has shown that the developed architecture presents a steady state error lower than 1.2 µm, a vibration reduction of 96% at 1130 Hz with a maximum resolving time of 6.60 ms.

The application of robots is growing in most countries, occupying a relevant place in everyday environments. Robots are still affected by errors due to their limitations, which may compromise the final performance. Accurate trajectories and positionings are strict requirements that robots have to satisfy and may be studied by the inverse kinematic (IK) formulation. The IK conventional numerical techniques are computationally intensive procedures, focusing on the robot joint values simultaneously and increasing the complexity of the solution identification. In this scenario, Machine Learning strategies may be adopted to achieve effective and robust manipulator’s IK formulation due to their computational efficiency and learning ability. This work proposes a machine learning (ML) sequential methodology for robot inverse kinematics modeling, iterating the model prediction at each joint. The method implements an automatic Denavit-Hartenberg (D-H) parameters formulation code to obtain the forward kinematic (FK) equations required to produce the robot dataset. Moreover, the artificial neural network (ANN) architecture is selected as a structure and the number of layers in combination with the hidden neurons per layer is defined by an offline optimization phase based on the genetic algorithm (GA) technique for each joint. The ANN is implemented with the following settings: scaled conjugate gradient as training function and the mean squared error as the loss function. Different network architectures are examined to validate the IK procedure, ranging from global to sequential and considering the computation direction (from end-effector or from basement). The method is validated in the simulated and experimental laboratory environment, considering articulated robots. The sequential method exhibits a reduction of the mean squared error index of 42.7–56.7%, compared to the global scheme. Results show the outstanding performance of the IK model in robot joint space prediction, with a residual mean absolute error of 0.370–0.699 mm in trajectory following 150.0–200.0 mm paths applied to a real robot.

This paper aims to investigate the impact of industrial robotics, examining the process integration in a sample of +600 companies located in the Province of Brescia, an intensive industrial area in the North of Italy. Through a detailed economic investigation, this study analyses the adoption of robotic solutions in companies of varying sizes, using a survey and financial databases to investigate the most used types of robots, their applications, the impacts at the operational and personnel level, and the companies’ growth (sales, employees, other). The results highlight a significant presence of robotic solutions, particularly articulated robots, in the large companies involved. Robotics diffusion positively correlates with significant improvements in terms of productivity and quality. The introduction of robots is associated with increased corporate growth indicators, including staff expansion. Large companies demonstrate a superior ability to adapt to these technologies, supported by more significant financial resources and a wide range of internal competencies for managing robots. Furthermore, large companies proactively hire qualified personnel or initiate internal training courses. Small and medium-sized enterprises (SMEs), although currently less equipped with robotic technologies, exhibit a significant interest in future adoption, highlighting the opportunity for growth and innovation. The results suggest that integrating robotics in the manufacturing sector not only constitutes an effective means to enhance operational performance but also acts as a catalyst for developing human capital and strengthening the local economy.

This paper proposes an innovative mechatronic piezo-actuated module to control vibrations in modern machine tools. Vibrations represent one of the main issues that seriously compromise the quality of the workpiece. The active vibration control (AVC) device is composed of a host part integrated with sensors and actuators synchronized by a regulator; it is able to make a self-assessment and adjust to alterations in the environment. In particular, an innovative smart actuator has been designed and developed to satisfy machining requirements during active vibration control. This study presents the mechatronic model based on the kinematic and dynamic analysis of the AVC device. To ensure a real time performance, a H2-LQG controller has been developed and validated by simulations involving a machine tool, PZT actuator and controller models. The Hardware in the Loop (HIL) architecture is adopted to control and attenuate the vibrations. A set of experimental tests has been performed to validate the AVC module on a commercial machine tool. The feasibility of the real time vibration damping is demonstrated and the simulation accuracy is evaluated.