Robotic Cell Research Articles

AUTOMATIC PATH PROGRAMMING FOR THE MANUFACTURING OF SHEET METAL COMPONENTS USING INCREMENTAL FORMING TECHNOLOGY

Asymmetric Incremental Sheet Forming (AISF) is an unconventional forming technology that involves localized and progressive plastic deformation of a sheet using a tool, typically a punch, following a continuous and numerically controlled path. Due to its specific characteristics and advantages, such as flexibility and relatively low implementation cost, it is considered a technology with great potential for competitively producing small series of sheet metal components. However, there are still some challenges, such as the lack of specific CAM tools for this technology, which has hindered its industrialization. Although the process may appear simple, with a 3D path sufficient to transform the original flat sheet into the final part, it has been observed that the manufacturing of real designs requires extensive knowledge of the technology to choose the appropriate path based on the target geometry. In this regard, this study presents a new solution developed by Tecnalia that incorporates expert process knowledge and enables automatic programming of Incremental Forming paths. Specifically, the solution allows programming paths aimed at manufacturing different geometric features commonly found in sheet metal components. This development has been validated through the manufacturing of several parts in a robotic cell implemented at Southeast University in Nanjing.

Smart automation in luxury leather shoe polishing: a human centric robotic approach

ABSTRACT The polishing of luxury leather shoes is a delicate, labor-intensive process traditionally performed by skilled craftsmen. Footwear companies aim to automate parts of this process to enhance quality, productivity and operator well-being, but the unique nature of luxury shoe production presents challenges. This paper introduces a solution involving a collaborative robotic cell to assist in shoe polishing. A collaborative robotic manipulator, equipped with a specialized tool and governed by force control, executes the polishing tasks. Key factors such as trajectory design, applied force, polishing speed and polish amount were analyzed. Polishing trajectories are designed using CAM software and transferred to the robot’s control system. Human operators design the process, supervise the robot and perform final finishing, ensuring their expertise is integral to achieving quality. Extensive testing on various shoe models showed significant improvements in quality and reliability, leading to successful implementation on an industrial production line.

Robotic manipulation of cardiomyocytes to identify gap junction modifiers for arrhythmogenic cardiomyopathy.

Arrhythmogenic cardiomyopathy (ACM) is a leading cause of sudden cardiac death among young adults. Aberrant gap junction remodeling has been linked to disease-causative mutations in plakophilin-2 (PKP2). Although gap junctions are a key therapeutic target, measurement of gap junction function in preclinical disease models is technically challenging. To quantify gap junction function with high precision and high consistency, we developed a robotic cell manipulation system with visual feedback from digital holographic microscopy for three-dimensional and label-free imaging of human induced pluripotent stem cell-derived cardiomyocytes (iPSC-CMs). The robotic system can accurately determine the dynamic height changes in the cells' contraction and resting phases, microinject drug-treated healthy and diseased iPSC-CMs in their resting phase with constant injection depth across all cells, and deposit a membrane-impermeable dye that solely diffuses between cells through gap junctions for measuring the gap junction diffusion function. The robotic system was applied toward a targeted drug screen to identify gap junction modulators and potential therapeutics for ACM. Five compounds were found to dose-dependently enhance gap junction permeability in cardiomyocytes with PKP2 knockdown. In addition, PCO 400 (pinacidil) reduced beating irregularity in a mouse model of ACM expressing mutant PKP2 (R735X). These results highlight the utility of the robotic cell manipulation system to efficiently assess gap junction function in a relevant preclinical disease model, thus providing a technique to advance drug discovery for ACM and other gap junction-mediated diseases.

Vision Systems for a UR5 Cobot on a Quality Control Robotic Station

This paper addresses the development of a vision system for the UR5 cobot and the corresponding operating algorithm of the robotic quality control station. The hardware–software architecture of the developed robotic station consisting of a UR5 cobot equipped with a web camera and a stationary industrial camera with a lighting system is presented. Image processing and analysis algorithms are described, the method of control and communication between the station components is discussed, and operating scenarios are presented as a single robotic station and as a part of the robotic line. Based on the results which were obtained, the level of measurement noise, accuracy, and repeatability of the developed vision system were estimated. A novel complete vision system architecture with hardware–software modules for the UR5 cobot quality control station is shown and discussed. The software part based on Python 3.12 language, OpenCV 4.7.0.68 libraries, and the PolyScope 1.8 environment incorporates modules for system calibration, image acquisition, preprocessing and image analysis (for objects’ location and geometric measurements) and for robotic cell communication and control. The hardware part of the UR5 cobot vision system is based on a PC with two independent and distinct cameras: one permanently affixed and the other mounted to the cobot’s flange. This innovative setup, combined with software architecture, broadens the scope of existing robotic quality control applications.

Approaches for the On-Line Three-Dimensional Knapsack Problem with Buffering and Repacking

The rapid growth of the e-commerce sector, particularly in Latin America, has highlighted the need for more efficient automated packing and distribution systems. This study presents heuristic algorithms to solve the online three-dimensional knapsack problem (OSKP), incorporating buffering and repacking strategies to optimize space utilization in automated packing environments. These strategies enable the system to handle the stochastic nature of item arrivals and improve container utilization by temporarily storing boxes (buffering) and rearranging already packed boxes (repacking) to enhance packing efficiency. Computational experiments conducted on specialized datasets from the existing literature demonstrate that the proposed heuristics perform comparably to state-of-the-art methodologies. Moreover, physical experiments were conducted on a robotic packing cell to determine the time that buffering and repacking implicate. The contributions of this paper lie in the integration of buffering and repacking into the OSKP, the development of tailored heuristics, and the validation of these heuristics in both simulated and real-world environments. The findings indicate that including buffering and repacking strategies significantly improves space utilization in automated packing systems. However, they significantly increase the time spent packing.

Hierarchical robot learning method for industrial fluorescent penetrant inspection

Fluorescent Penetrant Inspection (FPI) is a Non-Destructive Testing (NDT) method, extensively used to evaluate components for identifying defects across a broad range of industries. FPI process remains a manual visual inspection, where the operator by means of fluorescent dye, that penetrates discontinuities on the component, aims to distinguish between indications that are relevant (i.e., can be associated with surface defects) and non-relevant (i.e., can be associated to insufficient wash-off, dust or other non-relevant factors). The FPI process can be decomposed into the following steps: (a) thorough visual examination of the component, (b) executing manual wiping-off of the fluorescent dye with a brush, of all areas that require interrogation for potential indications, and (c) disposition of the inspected components. The number of those areas on the component that require interrogation and hence to be wiped-off is unknown a priori of the inspection and varies depending on the condition of the part. As a result, replacing this manual wipe-off step by a robot requires tedious manual programming of an excessive number of robot paths to assure reach of the robot to the entire surface of the part as well as safe robot motion. In addition, these robot motions are part specific and thus not transferrable to other geometries of components, making scaling of this technology across manufacturing industry not possible. In this paper, we propose a hierarchical robot learning method to address the challenge of reducing manual robot programming and enable the scaling of this automated NDT technology. The proposed method integrates and fuses Deep Reinforcement Learning (DRL), Screw Linear Interpolation (ScLERP) and Learning from Demonstration (LfD), enabling an autonomous generation of brushing strokes with a six-degrees of freedom (DoF) industrial manipulator, and automating the wipe-off step of the FPI process. Using this approach, a robot learning policy is generated for the wipe-off motion in a simulated industrial robotic cell at first and then the policy is transferred to the real system for validation.

Implementing a Vision-Based ROS Package for Reliable Part Localization and Displacement from Conveyor Belts

The use of computer vision in the industry has become fundamental, playing an essential role in areas such as quality control and inspection, object recognition/tracking, and automation. Despite this constant growth, robotic cell systems employing computer vision encounter significant challenges, such as a lack of flexibility to adapt to different tasks or types of objects, necessitating extensive adjustments each time a change is required. This highlights the importance of developing a system that can be easily reused and reconfigured to address these challenges. This paper introduces a versatile and adaptable framework that exploits Computer Vision and the Robot Operating System (ROS) to facilitate pick-and-place operations within robotic cells, offering a comprehensive solution for handling and sorting random-flow objects on conveyor belts. Designed to be easily configured and reconfigured, it accommodates ROS-compatible robotic arms and 3D vision systems, ensuring adaptability to different technological requirements and reducing deployment costs. Experimental results demonstrate the framework’s high precision and accuracy in manipulating and sorting tested objects. Thus, this framework enhances the efficiency and flexibility of industrial robotic systems, making object manipulation more adaptable for unpredictable manufacturing environments.

Data Envelopment Analysis-based Scenario Selection for Sequencing Pattern in a Simulated Robotic Cell

In this study, the performance of suggested scenarios for part input sequences in a 3-machine robotic cell producing different parts is determined through the application of data envelopment analysis (DEA) and the Banker–Charnes–Cooper model. A single gripper robot supports the manufacturing process by loading and unloading products and moving them inside the system. This study addresses random machine failures and repairs to minimize cycle time based on two robot move cycles in a three-machine robotic cell and overall production costs. Here, simulation assists in the modeling of uncertainty and a simulation-based optimization approach is applied to find the best scenarios for sequencing patterns in the cell through several numerical examples using DEA. The results displayed that, efficient scenarios satisfying minimum time and cost, are those, in which the percentages of operations assigned to the machines are close to each other. This enables decision-makers in manufacturing systems to make precise selections of the optimal part sequencing pattern with the lowest production cost and cycle time for robotic cells.

Scalable Hypothalamic Arcuate Neuron Differentiation from Human Pluripotent Stem Cells Suitable for Modeling Metabolic and Reproductive Disorders.

The hypothalamus, composed of several nuclei, is essential for maintaining our body's homeostasis. The arcuate nucleus (ARC), located in the mediobasal hypothalamus, contains neuronal populations with eminent roles in energy and glucose homeostasis as well as reproduction. These neuronal populations are of great interest for translational research. To fulfill this promise, we used a robotic cell culture platform to provide a scalable and chemically defined approach for differentiating human pluripotent stem cells (hPSCs) into pro-opiomelanocortin (POMC), somatostatin (SST), tyrosine hydroxylase (TH) and gonadotropin-releasing hormone (GnRH) neuronal subpopulations with an ARC-like signature. This robust approach is reproducible across several distinct hPSC lines and exhibits a stepwise induction of key ventral diencephalon and ARC markers in transcriptomic profiling experiments. This is further corroborated by direct comparison to human fetal hypothalamus, and the enriched expression of genes implicated in obesity and type 2 diabetes (T2D). Genome-wide chromatin accessibility profiling by ATAC-seq identified accessible regulatory regions that can be utilized to predict candidate enhancers related to metabolic disorders and hypothalamic development. In depth molecular, cellular, and functional experiments unveiled the responsiveness of the hPSC-derived hypothalamic neurons to hormonal stimuli, such as insulin, neuropeptides including kisspeptin, and incretin mimetic drugs such as Exendin-4, highlighting their potential utility as physiologically relevant cellular models for disease studies. In addition, differential glucose and insulin treatments uncovered adaptability within the generated ARC neurons in the dynamic regulation of POMC and insulin receptors. In summary, the establishment of this model represents a novel, chemically defined, and scalable platform for manufacturing large numbers of hypothalamic arcuate neurons and serves as a valuable resource for modeling metabolic and reproductive disorders.

An Innovative Vision-Guided Feeding System for Robotic Picking of Different-Shaped Industrial Components Randomly Arranged

Within an industrial plant, the handling of randomly arranged objects is becoming increasingly popular. The technology industry has introduced ever more powerful devices to the market, but they are often unable to meet the demands of the industry in terms of processing times. Using a multi-component feeder, which facilitates the automatic picking of objects arranged in bulk, is the ideal element to speed up the identification of objects by the vision system. The innovative designed feeder eliminates the dead time of the vision system since the feeder has two working surfaces, thus making the viewing time hidden in relation to the total handling cycle time. In addition, the step feeder integrated into the feeder structure allows for control over the number of objects that fall onto the work surface, optimizing the material flow. The feeder was designed to palletize aluminum hinge fins but can also handle other products with different shapes and sizes. A two-dimensional (2D) vision system is integrated into the robotic cell to identify the components to be palletized, obtaining a reduced cycle time. The innovative feeder is fully adaptable to industrial applications and allows for easy integration into the robotic cell in which it is installed; by testing its operation with different aluminum fins, male and female, significant results were obtained in terms of cycle times ranging from 1.44 s to 1.68 s per piece, with an average productivity level (PL) of 1175 pcs every 30 min.

Robot material processing and hardware-in-the-loop-based real-time simulations

Abstract This paper presents a cyber-physical production system that consists of a simulation, an industrial robot cell, and sensors. The industrial robot hardware, used for welding and additive manufacturing applications, is connected or “in-the-loop” with a real-time target machine on which simulations are running. These simulations are updated in real-time by the data provided by process sensors. Particular focus is given to wire-arc additive manufacturing (WAAM). Still, the cyber-physical system allows use in other robot-based material processes, such as sheet forming, (dis)assembly and material handling applications. It is also argued that the proposed cyber-physical system can be used so that it competes against the concept of using machine learning to optimize manufacturing processes. The proposed cyber-physical system enables the transition from traditional robot automation to autonomous robot systems.

Digital Twin Implementation for an Additive Manufacturing Robotic Cell based on the ISO 23247 Standard

Digital Twin Implementation for an Additive Manufacturing Robotic Cell based on the ISO 23247 Standard

Robotical Automation in CNC Machine Tools: A Review

Abstract Robotics and automation have significantly transformed Computer Numerical Control (CNC) machining operations, enhancing productivity, precision, and efficiency. Robots are employed to load and unload raw materials, workpieces, and finished parts onto CNC machines. They can efficiently handle heavy and bulky components, reducing the demand of manual labour and minimizing the risk of injuries. Robots can also be used in CNC machine tools to perform tasks such as automatic tool changing system, part inspection, and workpiece positioning. Automation technologies, including in-line inspection systems and Non-Destructive Testing (NDT) methods, can be integrated into CNC machining cells to enhance accuracy and reduce scrap and rework in machining operations. These systems collect real-time data on process parameters and machine tool performance to predict maintenance, optimize machining parameters, and improve overall efficiency. In the current study, applications of robotics and automation in the modification of CNC machine tools are reviewed and discussed. Different applications of robotics and automation in CNC machine tools, such as automated material handling, automatic tool changing, robotic work cells, adaptive machining, machine tending, quality inspection, data monitoring and analysis, and production line integration, are discussed. Thus, by analysing recent achievements in published papers, new ideas and concepts of future research works are suggested. As a result, accuracy as well as productivity in the process of part production can be enhanced by applying robotics and automation in CNC machining operations.

Comprehensive Review of Robotized Freight Packing

Background: This review addresses the emerging field of automated packing cells, which lies at the intersection of robotics and packing problems. Integrating these two fields is critical for optimizing logistics and e-commerce operations. The current literature focuses on packing problems or specific robotic applications without addressing their integration. Methods: To bridge this gap, we conducted a comprehensive review of 46 relevant studies, analyzing various dimensions, including the components of robotic packing cells, the types of packing problems, the solution approaches, and performance comparisons. Results: Our review reveals a significant trend towards addressing online packing problems, which reflects the dynamic nature of logistics operations where item information is often incomplete. We also identify several research gaps, such as the need for standardized terminologies, comprehensive methodologies, and the consideration of real-world constraints in robotic algorithms. Conclusions: This review uniquely integrates insights from robotics and packing problems, providing a structured framework for future research. It highlights the importance of considering practical robotic constraints. It proposes a research structure that enhances the reproducibility and comparability of results in real-world scenarios. By doing so, we aim to guide future research efforts and facilitate the development of more robust and practical automated packing systems.

Robot Cell that Allows Deep Hole Drilling

Ott-Jakob Spanntechnik has been supplied a robot cell that allows deep hole drilling to be automated and is reaping the benefits of the move

Design of Soft Microjoint to Improve Robotic Cell Micromanipulation Flexibility

Dexterity micromanipulation is a significant topic in the field of robotics. Herein, a novel robotic rotating microjoint controlled by an exogenous magnetic field based on magnetic programmable soft materials, which brings more dexterity to the micromanipulation task is proposed. First, the magnetic soft material is synthesized and the microjoint is manufactured. The maximum rotation angle and response time are characterized. Then the force analysis of the microjoint in a uniform magnetic field is performed and the relationship between the deformation bending angle of a microjoint and the magnetic field magnitude is first proposed. A sliding mode controller based on the deformation mechanism of microjoints and radial basis function neural network (DMRSMC) is proposed to achieve robust deformation control of the microjoint. The experiment that depicts the microjoint is able to achieve a maximum rotation angle of 22.16° and a response time of 16 ms. The DMRSMC controller performs angle control with higher accuracy than conventional SMC and PID controllers. Finally, a dexterous suction mechanism is developed by combining the microjoint with a soft micropipette needle to achieve dexterous grasping of zebrafish embryos with a 97% success rate and an orientation adjustment error is 1.36°.

Process-Driven Layout Optimization of a Portable Hybrid Manufacturing Robotic Cell Structure

Hybrid manufacturing combines manufacturing processes (typically additive manufacturing and machining) exploiting the benefits of each and enabling repair scenarios. Such an approach can be integrated with a robot, and if made portable, can form a flexible machine tool that can be easily transported anywhere to enable repairs in the field. However, the design of the load-bearing structure determines its transportability, and its stiffness plays a crucial functional role under dynamic loads and affects the product quality. Finding the right balance between weight and stiffness requires accurate boundary conditions and an effective design. In this work, a method is proposed towards process-driven optimization of a portable manufacturing cell structure. The dynamic cutting forces are determined through a machining process model and, via a simplified model of the robot arm, the forces at the base of the robot are estimated. Since the frame consists of beams, the layout optimization method is applied, using the estimated process forces as boundary conditions to optimize the arrangement of beams. The proposed method achieved 0.05 mm displacement in the load-bearing structure under milling and an acceptable accuracy of the position of a hole’s center during drilling, while the overall weight reduced by 17.6%.

A comparative analysis of constraint and connectivity graph techniques for assembly sequence generation in robotic assembly cells

The paper describes a comparative study on the assortment of appropriate assembly-generating techniques for robotic assembly cells. Several techniques have been developed and tested for the creation of viable sequences. Most of these techniques employ computational techniques to generate assembly sequences. Nevertheless, these techniques rely on a large number of assumptions and use insignificant data during the development of assembly sequences. In the present study, two techniques, viz., the connectivity graph technique and the constraint technique are selected, since they are very general, expedient, and easy to implement. Further, selected techniques can easily be automated to increase the productivity of robotics assembly cell. Each technique was applied to four products with different complexities to check their suitability in the context of robotic assembly cell. In the connectivity graph technique, the disassembly of components begins with the sink node in the connectivity graph and ends when no sink node is available. The constraint technique chooses two constraints: the G-constraint and the C-constraint. The process of disassembling the components starts with a component that does not have any G-constraint and satisfies the C-constraint. The G-constraint checks for geometric interference, and the C-constraint checks for connectivity relationships among the components. Both techniques are automated using C++ programming to obtain rapid and efficient results. The automation of assembly techniques is expected to lead to a revolutionary transformation in the assembly sector. The connectivity graph technique is found to be suitable for the robotic assembly cells as described in this article.

Investigation on robotic cells design improvement in the welding process of body in white

Investigation on robotic cells design improvement in the welding process of body in white

Fast Algorithm for High-Throughput Screening Scheduling Based on the PERT/CPM Project Management Technique

High-throughput screening systems are robotic cells that automatically scan and analyze thousands of biochemical samples and reagents in real time. The problem under consideration is to find an optimal cyclic schedule of robot moves that ensures maximum cell performance. To address this issue, we proposed a new efficient version of the parametric PERT/CPM project management method that works in conjunction with a combinatorial subalgorithm capable of rejecting unfeasible schedules. The main result obtained is that the new fast PERT/CPM method finds optimal robust schedules for solving large size problems in strongly polynomial time, which cannot be achieved using existing algorithms.