Abstract: Robotic palletizing requires the rapid generation of optimal packing plans and the corresponding robot programs within the tight time constraints imposed by mass customization and factory intralogistics. In this context, while the bin packing problem has been extensively studied in the literature, practical engineering tools that support flexible batch palletizing, from optimal packing definition to validated robot execution, remain limited. In this regard, the present paper proposes an integrated simulation framework that guides engineers in defining an optimal robotic palletizing process through a structured sequence of steps. Starting from the 3D model of an existing palletizing station and its input product mix, the framework, implemented as a set of interconnected Python modules within the RoboDK simulation platform, supports automated generation and assessment of actionable packing layouts, simulation-based verification of the robotic process, and generation of executable robot code that can be automatically transferred to the robotic palletizing cell within a Digital Twin oriented approach. An efficient heuristic method is introduced to solve 3D packing instances for box-type items on standard Europallets. The problem is initially formulated as a sequence of 2D packing problems solved through a Guillotine plus Best Fit strategy, iterating across layers to maximize pallet filling while improving load balancing. The framework is validated across multiple scenarios on an industrially representative case study involving a KUKA palletizing robot, a conveyor feeding system, and two auxiliary buffer pallets. Finally, an instruction streaming module enabling online execution of the validated code on the KUKA controller is presented. Overall, the results confirm the effectiveness of the proposed framework in accelerating process planning, virtual validation and programming activities while maintaining packing quality in realistic industrial scenarios.

Keywords: Bin packing | Engineering method | Instruction streaming | RoboDK | Robot simulation framework | Robotic palletizing

Abstract: Industry X.0 robotic manufacturing demands higher accuracy and flexibility, enabling continuously adaptive processes designed and optimized through simulations and Digital Twins. To achieve this level of flexibility and productivity in high value-added processes, where limited robot position accuracy becomes a critical constraint, advanced engineering methods and digital tools are required. These solutions must predictively compensate for inevitable robot positional accuracy errors, eliminating the need for manual pose refinement and enabling the generation of “first-time-right” robot code. This work aims to address these challenges by introducing an engineering tool capable of predictively correcting robot positioning inaccuracies across the workspace, enabling accurate point-to-point motion generation. It is intended for tasks with limited process interaction forces, where positioning errors are dominated by geometric, compliance, and joint-related effects. The tool leverages a multi-parameter Machine Learning (ML) error predictor trained on a reduced experimental dataset, minimizing data acquisition time and production downtime. Realized as a Python-based framework, it can be seamlessly integrated into commercial offline programming environments to automatically generate validated robot programs. The paper details the framework structure, focusing on the definition of the ML-based position error predictor, and its implementation on a robotic cell equipped with a high-payload KUKA robot and a FARO laser tracker. A preliminary experimental analysis identified payload, approach direction, and point location as the key operational parameters, accessible at the code level, that influence positioning accuracy. These insights guided feature selection and the design of reduced training datasets. In particular, a uniform spatial grid of only 64 points, corresponding to about one hour of measurement time, was sufficient to achieve near-optimal model accuracy. Several ML algorithms were compared, with the Tabular Prior-data Fitted Network achieving superior generalization on small datasets. Experimental validation on the KUKA robot showed up to a 98.4 % reduction in positioning error and consistent performance across all tested points, confirming the tool robustness and suitability for deployment across different industrial environments. All datasets, source code, and implementation scripts are openly released to enable reproducibility and facilitate industrial deployment.

Keywords: Engineering tool | Industrial robot | Machine learning | Offline compensation | Position accuracy | Robot simulation

Abstract: Collaborative robotics is increasingly considered in manufacturing to improve efficiency while reducing operators physical and cognitive workloads. However, the lack of comprehensive methodologies has limited the consistent implementation of human–robot collaborative workstations across industries. Existing approaches are often fragmented, require robotics expertise, and pose challenges for non-experts, leading to suboptimal station designs and inefficient task allocation. This study presents a structured design framework to transition traditional assembly processes into collaborative ones. The framework provides a practical, scalable solution for optimizing collaborative workstations, balancing performance, ergonomics, and industrial applicability. It starts from the analysis of the assembly tasks, followed by classification and allocation between human operators and robots, and concludes with virtual prototyping and performance optimization through simulation using a commercial tool. The adopted methodology integrates task analysis, ergonomic assessment, and workspace design to ensure accessible and efficient implementation. Validated through two industrial case studies involving a gear pump and a worm gearbox, the approach demonstrated significant reductions in cycle time and notable improvements in the ergonomic working conditions. Additionally, physical prototyping and testing conducted within a research collaborative cell further confirmed the achieved results.

Keywords: Collaborative assembly | Collaborative robotics | Human–robot simulation | Integrated design framework | Task allocation | Virtual prototyping

Abstract: The design and management of Industry X.0 manufacturing systems is based on several simulation tools and digital models, used to predict and optimize the final performance. The lack of a seamless interoperability between such tools requires the development of a Digital Thread, a communication framework able to connect and synchronize digital models with the physical assets throughout their lifecycles. This study presents a novel Digital Thread based framework for the simulation and Virtual Commissioning of robotic systems, built on a Docker infrastructure integrated with Hadoop 3.2.0. It leverages AutomationML (AML) as a data exchange standard and the Robot Operating System (ROS) for distributed robotic control. The proposed architecture supports offline programming and facilitates the deployment of custom, multi-brand robotic systems, while enabling seamless integration of AML-based Digital Twins. The proposed methodology is validated on a large scale robotic manufacturing cell for aircraft fuselage assembly, demonstrating its capability in terms of performance validation, operational coordination, and optimization of production sequences.

Keywords: AutomationML | Engineering Methods | ROS | Simulation Framework | Virtual Prototyping

Abstract: Purpose – This paper aims to present a novel method and tool for evaluating the motion resolution of industrial robots, defined as the minimum effective motion increment, to enable behavior-informed design and optimization of high-precision industrial robotic systems. Design/methodology/approach – A high-precision laser interferometer is used to measure the joint motion resolution of a KUKA KR210 R2700 Prime robot, following an improved test procedure derived from the ISO 230 standard and an objective error-based identification method. The resulting joint maps are then used to build a prediction tool, implemented in Python and integrated with RoboDK to propagate joint-level resolution to pose- and direction-dependent end-effector Cartesian resolution estimates across the robot workspace. Findings – Results show that ISO 230 tests can mask intrinsic joint resolution in the micro-step range due to backlash-driven response flattening. The proposed test procedure highlights marked joint-dependent performance, with the first three joints generally exhibiting finer micro-step resolution and more stable step responses than the wrist joints. The Cartesian predictor reproduces measured micro-displacement trends with uncertainty envelopes. Originality/value – The paper provides an objective and repeatable approach that decouples intrinsic resolution from backlash, and introduces an efficient prediction tool for Cartesian resolution estimation, supporting improved system design as well as robot motion planning and compensation for precision manufacturing tasks.

Keywords: Backlash | Industrial robots | Laser interferometer | Least increment step | Motion resolution | Prediction tool

Abstract: Industrial Robots (IRs) are typically employed as flexible machines to perform many types of repetitive and intensive tasks within fenced safe areas, ensuring high productivity and cost efficiency. However, their rigid programming approaches often pose challenges during cell commissioning and reset, hindering the implementation of self-reconfigurable systems. In addition, several production lines still need the presence of skilled operators to conduct assisted assembly operations and inspections. This motivates the growing interest in the development of innovative solutions for supporting safe and efficient human–robot collaborative applications. The manual guidance of the IR end-effector is a representative functionality of such collaboration, as it simplifies heavy-part manipulation and allows intuitive robot teaching and programming. The present study reports a sensor-based approach for enabling manual guidance operations with high-payload IRs and discusses its practical implementation on a production cell with an extended workspace. The setup features a KUKA robot mounted on a custom linear track actuated via Beckhoff technology to enable flexible assembly and machining operations. The developed logic and its software configuration, split into multiple control units to allow the manual guiding of both the 6-axis IR and the linear track unit, are described in detail. Finally, an experimental demonstration involving two users with different levels of expertise was conducted to evaluate the approach during target teaching on a physical cell. The results showed that the proposed manual guidance method significantly reduced task completion time by more than 55% compared with the conventional teach pendant, demonstrating the effectiveness and practical advantages of the developed framework.

Keywords: collaborative manufacturing | human–robot interaction | industrial robot | linear track | manual guiding

Abstract: Purpose – This paper aims to present a methodology for evaluating the path accuracy of industrial robots using the telescoping ballbar measurement technology. The goal is to improve accuracy assessments in precision-driven manufacturing processes. Design/methodology/approach – A single telescoping ballbar is used to assess the circle contouring performance of a KUKA KR210 R2700 prime robot. Experiments involve system setup, data collection and analysis in Matlab to derive performance metrics such as radial deviation, circularity and path accuracy error. This study investigates the impact of varying the operational conditions, including speed, payload and robot configuration, on these indexes through statistical analysis, and examines the relationship between joint errors and path deviations. Findings – The results indicate that the robot behavior is influenced by the operating conditions, with notable error spikes at joint reversal positions due to factors such as joint backlash and transmission errors. This study evaluates various performance indexes from different standards, ISO 230 and ISO 9283, and identifies key operating parameters influencing each index. The findings suggest effective strategies for error compensation and performance enhancement. Originality/value – This paper offers a novel approach to path accuracy verification and error source identification in industrial robots. It proposes methods to rapidly assess the correlation between performance and operating conditions, offering insights for better calibration and control strategies, especially in high-precision tasks.

Keywords: Backlash | Ballbar | Experimental assessment | Industrial robots | Path accuracy

Abstract: Traditional industrial robots offer significant operational flexibility and adapt well to reconfigurable production systems, although they face limitations in applications demanding high motion performance and spatial positional accuracy. While novel manufacturing solutions supporting small batch productions of custom products are widely researched, they are not yet fully available at industrial level. With the aim to advance in this domain, the present work, conducted in the context of the EU project OPeraTIC, reports the development of a novel manipulator for advanced three-dimensional laser surface treatment of large industrial components. The proposed robotic platform presents a decoupled kinematic architecture, with direct drive actuation in all axes. Its open control ensures adaptability to diverse manufacturing scenarios, making it a versatile tool for modern production lines. Starting from the description of its embodiment design and mechanical layout, the paper delves into robot virtual prototyping focusing on kinematic and dynamics aspects. In particular, a detailed behavioral model covering direct and inverse kinematic calculations, also allowing the precise evaluation of all actuation forces/torques, has been developed using analytical approaches. The model is validated with a commercial solver imposing different spatial motions. The generated performance maps illustrate the robot operational capabilities across a range of work scenarios.

Keywords: dynamics | kinematics | multibody model | robotic design | simulation | virtual prototyping

Abstract: This paper addresses the crucial aspect of position error modeling and compensation in industrial servomechanisms with the aim to achieve accurate control and high-performance operation in industrial robots and automated production systems. The inherent complexity and nonlinear behavior of these modules, usually consisting of a servomotor and a speed reducer, often challenge traditional analytical modeling approaches. In response, the study extensively explores the design and implementation of Machine Learning (ML) algorithms to obtain a comprehensive model of the Transmission Error (TE) in rotating vector reducers, which is a main source of robot motion accuracy errors. The ML models are trained with experimental data obtained from a special purpose test rig, where the reducer is tested under different combinations of input speed, applied load and oil temperature. In the second part of the work, the resulting predictive model, tailored to capture the intricate dynamics of the analyzed reducer, is imported into a programmable logic controller to enable online compensation strategies during the execution of custom motion profiles. Experimental tests are conducted using two distinct motion profiles: one generated with a cycloidal law, typical of industrial machinery, and the other extrapolated from the joints of an industrial robot during a pick-and-place task. The results demonstrate the effectiveness of the proposed approach, enabling accurate prediction and substantial reductions (over 90%) in the overall reducer TE through the implemented predictive model.

Keywords: Compensation approach | Machine learning | Predictive modeling | Servomechanism | Test rig | Transmission error

Abstract: The design of delta robots poses significant challenges as their mechanical behavior depends on a high number of dimensional parameters and dynamic factors. This is further compounded by the presence of demanding performance requirements, particularly in terms of position accuracy during high-dynamics motion tasks. By leveraging theoretical models, dynamic optimization techniques and advanced simulations, the present paper aims to streamline the design process, providing a structured engineering method and tool to address the dimensional synthesis of delta robots, encompassing kinematics, dynamics, link flexibility, and ball joint clearance. The systematic design process incorporates user requirements, including bounding box specifications, cycles per minute for pick-and-place operations, end-effector accuracy tolerance, maximum static payload, and cost minimization. The methodology involves an initial dynamic optimization phase employing a genetic algorithm to derive optimal dimensional parameters. Analytical models implemented in Matlab expedite the iterative optimization process. Then, the optimized design is virtually prototyped in RecurDyn flexible multibody simulation tool for validation by including the link flexibility and the effect of ball joint clearances. The iterative approach ensures that the final design aligns with user expectations. Additionally, the paper addresses motor selection based on torque requirements and proposes an approach for evaluating the robot performance in terms of maximum end-effector acceleration and payload. Finally, the efficacy of the tool is evaluated through a case study focused on designing a manipulator as an integral part of a collaborative research project with an industrial partner.

Keywords: Delta robot | Design tool | Industrial robotics | Multibody simulation | Optimization

Abstract: Position-controlled servomechanisms are the core elements of flexible manufacturing plants, primarily utilized to actuate robotic systems and automated machines. To match specific torque and costs requirements, typical servomechanism arrangements comprise precision reducers, which introduce motion errors that heavily limit the final performance achievable. Such errors are complex to model and depend from speed, dynamic loading conditions and temperature. Accurate characterization is fundamental to develop digital twins and advanced control strategies aimed at their active prediction and compensation. To properly assess the servomechanisms behavior and elaborate high-fidelity virtual models, instrumented test rigs have been proposed which can replicate the time-varying working conditions encountered in real industrial environments. In this context, the present paper reports about a novel engineering method for developing an active loading apparatus, namely a programmable mechatronic device that can deliver custom loads in a highly dynamic manner. The proposed system, consisting of a secondary servomotor and related rotating vector reducer, is integrated and synchronized within an existing instrumented test rig and is controlled in torque mode via a programmable logic controller. The paper mainly focuses on the description of the implemented closed-loop control and on the related tuning and calibration processes, demonstrating that the proposed solutions avoid important measurement errors that could compromise the final effectiveness of the system. The study finally explores the potential benefits of introducing a filter to further enhance system performance. At last, to prove the importance of stabilizing the rig and demonstrate the influence of the control parameters on its measurements, a standard test aimed at assessing the reducer transmission error is conducted adopting different parameter settings.

Keywords: Loading system | Mechanical transmission | Servomechanisms | Stabilization | Test rig | Torque control

Abstract: Earth-moving machine builders require innovative design methods and tool to optimize structural performance while reducing production and design costs, particularly in crucial phases like undercarriage frame design and structural verification. After an in-depth description of the design flow normally followed in industry, the paper presents a computationally efficient method and tool to aid designers in dimensioning extendable tracked undercarriages, aiming to drastically reduce design time and efforts to optimize resources. The proposed tool is based on an analytical model established from in-depth analyses of the undercarriage Computer Aided Design (CAD) assembly and the expertise of the industrial partner. To address the 3D structural problem, a planar system is employed with proper corrective coefficients. These coefficients are meticulously evaluated through direct comparison with Finite Element Method (FEM) models by seamlessly integrating SolidWorks and ANSYS Workbench. The tool accepts as inputs geometric and material data, as well as specific user-defined load scenarios, providing outputs in the form of the deflected configuration of the undercarriage and stress levels. Direct comparison with the results obtained from FEM for three industrial undercarriage models demonstrates the validity of the approach, with errors consistently within the 10% range in almost all cases. This enables designers with no advanced skills in FEM to efficiently validate diverse design variants with minimal effort. Once validated, the tool is integrated with an optimizer in Matlab to conduct computationally efficient design optimization studies. The optimization problem, focused on minimizing the beam’s vertical size while maintaining structural integrity and limiting deflections, has been successfully resolved within a limited computational time, showcasing the benefits of the proposed approach for undercarriage design.

Keywords: CAD/CAE integration | Earth-moving machinery | Engineering design tool | Structural design | Tracked undercarriage

Abstract: Industrial Robots (IR) are currently employed in several production areas as they enable flexible automation and high productivity on a wide range of operations. The IR low positioning performance, however, has limited their use in high precision applications, namely where positioning errors assume importance for the process and directly affect the quality of the final products. Common approaches to increase the IR accuracy rely on empirical relations which are valid for a single IR model. Also, existing works show no uniformity regarding the experimental procedures followed during the IR performance assessment and identification phases. With the aim to overcome these restrictions and further extend the IR usability, this paper presents a general method for the evaluation of IR pose and path accuracy, primarily focusing on instrumentation and testing procedures. After a detailed description of the experimental campaign carried out on a KUKA KR210 R2700 Prime robot under different operating conditions (speed, payload and temperature state), a novel online compensation approach is presented and validated. The position corrections are processed with an industrial PC by means of a purposely developed application which receives as input the position feedback from a laser tracker. Experiments conducted on straight paths confirmed the validity of the proposed approach, which allows remarkable reductions (in the order of 90%) of the orthogonal deviations and in-line errors during the robot movements.

Keywords: Error compensation | Experimental approaches | Industrial robots | Laser tracker | Path accuracy | Pose accuracy

Abstract: In the modern manufacturing industry, virtual models are normally used in machine and robot design for behavioral analysis and optimization. However, the literature reveals a notable gap in methodologies for simulating and conducting virtual commissioning multi-brand robotic systems with distributed control in a unified software environment before their physical installation. In this context, the present works leverages the open-source robotic libraries within the Robot Operating System (ROS) environment to extend the simulation capabilities of RoboDK software, aiming to support the validation and coordination of complex automated systems. This methodology is validated through procedures for conducting virtual commissioning of a robotic cell for an aircraft fuselage assembly whose process coordinator is defined in ROS. The obtained results demonstrate the effectiveness of the proposed simulation framework.

Keywords: Industrial Robot | RoboDK | ROS | Simulation | Virtual Commissioning

Abstract: The study of an automated system for intralogistics requires an important use of time and resources, starting from the input data analysis up to the definition of the technical solution. While many commercial tools are available for testing and optimizing the plant performance during the advanced design stages, little work has been done concerning the workflow to be followed at the pre-sales design phase. In this context, the present paper focuses on the definition of the best practices for the correct preliminary definition of a robotic cell for palletization. To simplify and speed up the pre-sales feasibility study and estimate the performance of the proposed robotic system, an engineering approach based on a simplified theoretical model is reported and integrated within a dynamic calculation table. As the main output, the proposed tool calculates the robot saturation which is a key index for the plant preliminary definition.

Keywords: Design Tool | Industry 4.0 | Palletizing Robotic System | Performance Definition | Pre-Sales Design

Abstract: Robot-based manufacturing cells exhibit strong capability in performing high customizable working cycles involving operations like machining, assembly, painting, welding and gluing. Designing and optimizing such systems require a holistic approach across domains like device layout, logical connections, control systems, and tasks. The AutomationML file format (AML) within the Reference Architectural Model Industrie (RAMI 4.0) framework enhances interconnection and interoperability among engineering tools but lacks established practices. This paper introduces AML for representing flexible manufacturing cells within the Product-Process-Resource paradigm. AML serves as a comprehensive representation of equipment, processed products, and manufacturing operations, aiding design, configuration, virtual simulation, and optimization before physical implementation. A software tool for robotic systems simulation and programming, i.e. RoboDK, is employed to generate customizable virtual prototypes based on AML content for streamlined simulation and assessment. The same AML file facilitates code transfer to physical cell control units. Demonstrated on a robotic deburring cell, this approach highlights AML’s efficacy in data representation between virtual and physical environments, paving the way for digital twin mapping and enhanced manufacturing integration.

Keywords: AutomationML | Interoperability | Offline Programming | RAMI 4.0 | Robotics | Simulation

Abstract: Virtual Training (VT) is a recently available modality that uses Virtual Reality (VR) technologies to train people within simulated environments. Companies can use VT to leverage the skills of their staff by avoiding risks related to real production thanks to the digital simulation possibilities and anticipating the training phases to reduce downtime of productive systems [1]. However, the use of VR-based immersive training is still limited in industry due to the cost of equipment and the lack of skilled people able to use VR platforms to effectively implement this type of simulations. This paper deals with the application of low-cost VR equipment to develop virtual training applications. It defines a methodology to create suitable applications for smartphones to be displayed by low-cost, highly portable Google Cardboard. Such equipment could be easily used also by small and medium-sized enterprises (SMEs) that do not have large capitals to invest in traditional VR viewers but are still interested in exploring the adoption of digital tools for training. A case study is presented related to assembly of a 3D printer.

Keywords: Digital Simulation | Ergonomics | Human-centered de-sign | Virtual training | X-reality

Abstract: In the context of Industry 4.0, industrial robots are experiencing wider application fields due to improved capability of executing flexible and diversified manufacturing cycles. The implementation of mechatronic automation systems remains a critical task, since it must cope with many heterogeneous domains, from layout definition to design of mechanical, actuating, and sensing devices, control logic coding, testing and optimization of the whole system. This paper leverages a Python-based connection between a simulation software for robotic cells, i.e. RoboDK, and a PLC system, i.e. Beckhoff TwinCAT, to realize a holistic virtual prototyping environment able to support the design and virtual commissioning of automation systems. The proposed approach is demonstrated with a case study comprising a robotic deburring cell. The resulting application shows the ability to effectively debug logic code, optimize the sequence of manufacturing tasks, and monitor the primary kinematic quantities.

Keywords: RoboDK | Robotic cell | TwinCAT | Virtual Commissioning | Virtual prototyping

Abstract: Wire Arc Additive Manufacturing is based on a welding torch usually attached to a robotic arm with multiple degrees of freedom. Robot-based additive manufacturing allows non-planar and non-uniform thickness layers to be deposited where the slices have non-constant thickness. Thus, in addition to the motion settings, fine regulations of the welding parameters become necessary to obtain variable bead heights in the same slice. This paper aims to evaluate the user-accessible welding parameters’ influence on the deposited material’s dimensions during continuous Cold Metal Transfer (CMT) and its variant named CMT Cycle Step. In particular, the height and width of beads are investigated by varying the travel speed and the wire-feed rate (continuous CMT), as well as the size of the droplets by varying the number of CMT cycles and the wire-feed rate (CMT Cycle Step). In particular, the characterization of the material deposited during the CMT Cycle Step is not deeply studied in the literature. The experimental specimens are measured and the obtained values are numerically processed to yield empirical formulas that link the dimensions of the deposited material with the selected process parameters. The results show that CMT Cycle Step is more stable than continuous CMT, which confirms its higher suitability for accurate manufacturing.

Keywords: Bead Modeling | Cold Metal Transfer | Experimental characterization | Wire and Arc Additive Manufacturing

Abstract: Due to the rising digitalization in the past few years, even more data can be collected from smart products and sensors to describe the real world, goods, environments, and newly humans including those mutual interactions. Digital twin (DT) has become a key word in engineering, society, and medicine, which is also a hot topic in research for creating virtual data-driven replicas of real objects and simulating their behaviors to predict and optimize the entire system functioning. DTs can mirror the physical entities throughout their lifecycle and create real-time connections between the physical and virtual worlds to monitor and control physical objects from any location. Physical objects can be any living or non-living object, such as humans, machines, robots, cars, buildings, plants, food, or economy. Numerous papers related to DT in various industries have been presented, but very few are focusing on the human-related aspects and the quality of the human machine interaction. In this context, how to shape a human-centric digital twin (HCDT)? The paper states the needs of a human-centric approach in the design and development of DT and presents a set of significant applications of HCDT in different fields, from industry to medicine, from economics to society, discussing the positioning of the HCDT concept in the landscape of transdisciplinary engineering, which is also subject of a workshop during the conference.

Keywords: Digital Twin | Human-centric approaches | Human-machine interaction | Transdisciplinary Engineering

Abstract: Modern automated production systems (e.g., automatic machines, assembly lines, robotic cells) are typically governed by dedicated industrial controllers, such as Programmable Logic Controllers (PLCs), which supervise and coordinate the process by exchanging I/O data, sequencing tasks or triggering actions with the involved automation modules. Different solutions have been developed to offer an intuitive Human-Machine Interface (HMI) programming to the user, based on PLC HMI editors, according to vendor-specific programming languages. However, in the current industrial practice, user interfaces (UIs) are usually generated by software specialists and far from adopting any user-centered approach. As a result, the generated UIs are poorly usable and hard to understand for end users (e.g., operators), diverging from Industry 5.0 ideas that put humans at the center of the modern factory design. In this context, the present paper aims at exploring how the adoption of User eXperience (UX) driven approaches can benefit the design of industrial PLC UIs, reflecting on advantages and limits, and transdisciplinary perspectives. A case study utilizing Beckhoff TwinCAT as PLC environment and Adobe XD as UX design tool is examined and discussed.

Keywords: Human-machine interface | Programmable Logic Controllers | User experience | User interface | User-centered design

Abstract: Nowadays, manufacturing plants are required to be flexible to respond quickly to customer demands, adapting production and processes without affecting their efficiency. In this context, Industrial Robots (IRs) are a primary resource for modern factories due to their versatility which allows the execution of flexible, reconfigurable, and zero-defect manufacturing tasks. Even so, the control and programming of the commercially available IRs are limiting factors for their effective implementation, especially for dynamic production environments or when complex applications are required. These issues have stimulated the development of new technologies that support more efficient methods for robot control and programming. The goal of this research is to identify and evaluate the main approaches proposed in scientific papers and by the robotics industry in the last decades. After a critical review of the standard IR control schematic, the paper discusses the available control alternatives and summarizes their characteristics, range of applications, and remaining limitations.

Keywords: industrial robots | instruction streaming | open controller | robot control | robot programming | trajectory streaming

Abstract: Industrial Robots (IRs) are increasingly adopted for material subtraction or deposition functions owing to their advantages over machine tools, like cost-effectiveness and versatility. Unfortunately, the development of efficient robot manufacturing processes still faces unsolved issues related to the IRs poor positioning accuracy and to the tool path generation process. Novel engineering methods and tools are needed for CAD based programming of accurate paths and continuous robot motions to obtain the required manufacturing quality and tolerances. Within this context, to achieve smoothness along the tool path formed by linear G-code segments, the IR controllers’ approximation strategies, summarily reported in the manufacturer’s manuals, must be considered. The aim of this paper is to present the preliminary work carried out to identify the approximation algorithms of a Kuka IR when executing linear moves. An experimental study is conducted by varying the controller settings and the maximum translational velocity. The robot behavior has been acquired thanks to the controller tracing function and then processed to yield relations readily employable for the interpretation of G-Code commands and the subsequent generation of proper robot motion instructions. The obtained formulas allow to accurately predict the robot geometric path and kinematics within the corner transition between two linear segments.

Keywords: Corner smoothing | G-code translation | Manufacturing robots | Path approximation | Robot programming

Abstract: The Servo-Mechanisms (SMs) mounted in industrial robots joints are a major source of positioning accuracy errors. To improve robots precision performance, researchers have been focusing on the development of novel SMs design and control strategies, which need extensive experimental analyses to tune their parameters. In this context, the scope of this paper is double: first, to present the novel experimental apparatus and methods designed to improve the accuracy of the transmission performance evaluation of high dynamics SMs and, secondly, to report and discuss the achieved experimental results. In the first part, a description of the test rig tuning operations is given, primarily focusing on the signals synchronization and on the elimination of the measuring errors caused by the mechanical transmission elasticity and the servomotor torque ripples. Then, control strategies for compensating the torque ripples and input speed errors are defined. It is shown that speed oscillations can be reduced of ≈70% when rotating the servomotor up to 2000 rpm, improving the measurement quality of the reducer performance. In the second part, a set of experiments is carried out to assess the combined effect of input speed and lubricant temperature on the reducer behavior. The system sensitivity to the variation of the input parameters is confirmed by the dynamic lost motion curves, whose mean value equals 16.8″ and 35.4″ when the reducer is operated at its minimum and maximum friction load respectively. At last, the extrapolated harmonic content is used to build a simple mathematical model of the reducer transmission error.

Keywords: Experimental methods | Lubricant temperature | Robot reducers | Servo-mechanisms | Test rig | Torque ripples | Transmission error

Abstract: Nowadays, robot-based additive manufacturing (RBAM) is emerging as a potential solution to increase manufacturing flexibility. Such technology allows to change the orientation of the material deposition unit during printing, making it possible to fabricate complex parts with optimized material distribution. In this context, the representation of parts geometries and their subsequent processing become aspects of primary importance. In particular, part orientation, multiaxial deposition, slicing, and infill strategies must be properly evaluated so as to obtain satisfactory outputs and avoid printing failures. Some advanced features can be found in commercial slicing software (e.g., adaptive slicing, advanced path strategies, and non-planar slicing), although the procedure may result excessively constrained due to the limited number of available options. Several approaches and algorithms have been proposed for each phase and their combination must be determined accurately to achieve the best results. This paper reviews the state-of-the-art works addressing the primary methods for the representation of geometries and the subsequent geometry processing for RBAM. For each category, tools and software found in the literature and commercially available are discussed. Comparison tables are then reported to assist in the selection of the most appropriate approaches. The presented review can be helpful for designers, researchers and practitioners to identify possible future directions and open issues.

Keywords: Geometry processing | Multiaxial deposition | Robot-based additive manufacturing | Slicing strategy | Volume decomposition

Abstract: This article reports the analysis and preliminary design of a passive, wearable, upper limb exoskeleton to support workers in industrial environments in a vast range of repetitive tasks, offering an effective strategy to reduce the risk of injuries in production lines. The system primary purpose is to compensate for gravity loads acting on the human upper limb. The proposed exoskeleton is based on 6 Degrees-of-Freedom (DoFs) kinematics with 5-DoFs for the shoulder joint (two displacements plus three rotations) and 1-DoF for the elbow. Gravity compensation is implemented with passive elastic elements to minimize weight and reduce cost. A detailed analytical tool is developed to support the designer in the preliminary design stage, investigating the exoskeleton kinetic-static behaviour and deriving optimal design parameters for the springs over the human arm workspace. By defining specific functional requirements (i.e., the user\'s features and simulated movements), computationally efficient optimization studies may be carried out to determine the optimal coefficients and positions of the springs, thus, maximizing the accuracy of the gravity balancing. Two different solutions for the arrangement of the elastic elements are investigated, and obtained results are validated with a commercial multi-body tool for some relevant movements of the user\'s arm.

Keywords: design optimization | gravity balancing | Upper limb exoskeleton | virtual prototyping | wearable devices

Abstract: Intelligent robotic manufacturing cells must adapt to ever-varying operating conditions, developing autonomously optimal manufacturing strategies to achieve the best quality and overall productivity. Intelligent and cognitive behaviors are realized by using distributed controllers, in which complex control logics must interact and process a wide variety of input/output signals. In particular, programmable logic controllers (PLCs) and robot controllers must be coordinated and integrated. Then, there is the need to simulate the robotic cells’ behavior for performance verification and optimization by evaluating the effects of both PLC and robot control codes. In this context, this work proposes a method, and its implementation into an integrated tool, to exploit the potential of ABB RobotStudio software as a virtual prototyping platform for robotic cells, in which real robots control codes are executed on a virtual controller and integrated with Beckhoff PLC environment. For this purpose, a PLC Smart Component was conceived as an extension of RobotStudio functionalities to exchange signals with a TwinCAT instance. The new module allows the virtual commissioning of a complete robotic cell to be performed, assessing the control logics effects on the overall productivity. The solution is demonstrated on a robotic assembly cell, showing its feasibility and effectiveness in optimizing the final performance.

Keywords: robotic cell | RobotStudio | TwinCAT | virtual commissioning | virtual prototyping