Assembly Scenario Research Articles

Human acceptance evaluation of AR-assisted assembly scenarios

The contribution examines the acceptance of Augmented Reality (AR) in assembly scenarios by a model-based approach for acceptance evaluation. After a critical literature research and analysis, a proprietary model for acceptance measurement is developed, which includes and synthesizes previous models and simplifies them considerably for the purpose of industrial assembly. Consequently, a structural model is derived which is based on the basic concepts of the Unified Theory of Acceptance and Use of Technology (UTAUT) and Technology Acceptance Model (TAM). This is built up as a path model to explain user acceptance. Following, a laboratory experiment is set up to collect data. At the final assembly workstation of the FHWS c-Factory the study participants assemble a toy truck once without and once with AR support. The c-Factory is a concept factory for smart production in an IoT environment. Afterwards, relevant data are collected by means of a survey. The evaluation shows that the acceptance of AR is given and the model is resilient. The results show also, that AR is accepted by the participants supporting their work.

Generating a Hand Pose Data Set for Vision Based Manual Assembly Assistance Systems

Current assistance systems for manual assembly reduce the efficiency of the worker by being invasive in the workflow. To restore the efficiency and at the same time to maintain the benefits of assistance systems, real time hand pose estimation can be used. However, no suitable data set is available for training such application specific detectors. In the presented work, a data set is generated that allows the use of different work gloves and prepares the overlay of realistic hand textures. We use low cost data gloves for hand pose tracking and a RGBD camera to capture the data set with 30 data points per second. This low cost approach is presented in an application for the manual assembly scenario, although transfer of the method to other scenarios is possible.

Applying Task-Oriented Safety Field Calibration in Human Robot Collaborative Systems

Effective and safe human-robot collaboration (HRC) in assembly requires a reasonable proximity between humans and robots. This paper considers an assembly scenario that humans and robots collaborate in a shared workspace, where safety is the most important issue. A dynamic safety field is defined for HRC to ensure safe collaboration and interaction between a human operator and a robot to achieve assembly tasks. The safety field can be adjusted and calibrated based on predicted assembly tasks. Utilizing the dynamically calibrated safety field, the robot movement will be obtained. The dynamic calibration of safety field for robot movement presents an alternative easier way for robot motion planning based on safety sense. Case studies are performed to demonstrate the effectiveness of the proposed dynamic safety field motion planning method.

ErgoTakt: A novel approach of human-centered balancing of manual assembly lines

Although the increasing use of automation in industry, manual assembly stations are still common and, in some situations, even inevitable. Current practice in manual assembly lines is to balance them using the takt-time of each workstation and harmonize it. However, this approach mostly does not include ergonomic aspects and thus it may lead to workforce musculoskeletal disorders, extended leaves, and demotivation. This paper presents a holistic human-centric optimization method for line balancing using a novel indicator ̶ the ErgoTakt. ErgoTakt improves the legacy takt-time and helps to find an optimum between the ergonomic evaluation of an assembly station and its balance in time. The authors used a custom version of the ErgoSentinel Software and a Microsoft Kinect depth camera to perform online and real-time ergonomic assessment. An optimization algorithm is developed to find the best-fitting solution by minimizing a function of the ergonomic RULA-value and the cycle time of each assembly workstation with respect to the worker's ability. The paper presents the concept, the system-setup and preliminary evaluation of an assembly scenario. The results demonstrate that the new approach is feasible and able to optimize an entire manual assembly process chain in terms of both, economic aspects of a well-balanced production line as well as the ergonomic issue of long term human healthy work.

Transfer Learning-enabled Action Recognition for Human-robot Collaborative Assembly

Human-robot collaboration (HRC) is critical to today’s tendency towards high-flexible assembly in manufacturing. Human action recognition, as one of the core prerequisites for HRC, enables industrial robots to understand human intentions and to execute planning adaptively. However, existing deep learning-based action recognition methods rely heavily on a huge amount of annotation data, which may not be effective or realistic in practice. Therefore, a transfer learning-enabled action recognition approach is proposed in this research to facilitate robot reactive control in HRC assembly. Meanwhile, a decision-making mechanism for robotic planning is introduced as well. Lastly, the proposed approach is evaluated in an aircraft bracket assembly scenario to reveal its significance.

Leveraging model based definition and STEP AP242 in task specification for robotic assembly

This article explores using aspects of STEP AP242 for constraint-based robot programming for assembly operations. Industry 4.0 envisions smart, connected factories where all the operations are connected by an unbroken thread of product and process data. As part of these efforts, many industries are adopting Model Based Definition and STEP AP242. STEP AP242 is an exchange format that allows Model Based Definition where Product Manufacturing Information is directly associated with the 3D CAD model. This article relates the geometric and assembly constraints from the CAD model to motion constraints on the robot during the assembly process. This article also discusses the use of Product Manufacturing Information from STEP AP242 files for automatic robot programming. The results are showcased with a prototype for a motor assembly scenario.

Modelling of Robotic Single Peg-In-Hole Assembly Using ADAMS/MATLAB Co-simulation

A virtual assembly model of robotic single peg-in-hole assembly environment is developed to determine contact force using ADAMS/MATLAB Co-simulation in this work. The existing dynamic models of the insertion process are incapable of replicating the real assembly scenarios due to its complicated mathematical expressions and handling variety of dynamic parameters. In order to address this problem, a co-simulation model is proposed to study the dynamic behaviour of a planar robot in executing an assembly process. Initially, the physical robotic assembly environment with 3-R planar manipulator is built in Solid Works and it is imported to ADAMS environment for the analysis. A Cartesian trajectory for the end-effector is given as input for executing the assembly process. Since simulation in ADAMS is executed with joint values, inverse kinematic calculations of the manipulator are employed in MATLAB environment to convert the given Cartesian descriptions into their corresponding joint descriptions. Further, the joint values are properly exchanged between these different software environments using co-simulation model to simulate the intended assembly process. Besides, the contact forces at peg end are analysed under defined lateral error condition.

The Assembly of the First Massive Black Holes

The existence of ∼109M⊙supermassive black holes (SMBHs) within the first billion years of the Universe has stimulated numerous ideas for the prompt formation and rapid growth of black holes (BHs) in the early Universe. Here, we review ways in which the seeds of massive BHs may have first assembled, how they may have subsequently grown as massive as ∼109M⊙, and how multimessenger observations could distinguish between different SMBH assembly scenarios. We conclude the following: ▪ The ultrarare ∼109M⊙SMBHs represent only the tip of the iceberg. Early BHs likely fill a continuum from the stellar-mass (∼10M⊙) to the supermassive (∼109) regimes, reflecting a range of initial masses and growth histories. ▪ Stellar-mass BHs were likely left behind by the first generation of stars at redshifts as high as ∼30, but their initial growth typically was stunted due to the shallow potential wells of their host galaxies. ▪ Conditions in some larger, metal-poor galaxies soon became conducive to the rapid formation and growth of massive seed holes, via gas accretion and by mergers in dense stellar clusters. ▪ BH masses depend on the environment (such as the number and properties of nearby radiation sources and the local baryonic streaming velocity) and on the metal enrichment and assembly history of the host galaxy. ▪ Distinguishing between assembly mechanisms will be difficult, but a combination of observations by the Laser Interferometer Space Antenna (probing massive BH growth via mergers) and by deep multiwavelength electromagnetic observations (probing growth via gas accretion) is particularly promising.

Considerations of potential runaway motion and physical interaction for speed and separation monitoring

In the field of human-robot collaboration (HRC), the speed and separation monitoring (SSM) collaboration have attracted much attention owing to its non-contact safety strategy. 3D sensing applications are currently of interest for industrial automation technology and are considered to be a promising method for maximizing the efficiency of SSM. However, little attention has been given to the runaway space of the robot or the potential contact due to the foreseeable misuse of the operator. In this study, experiments are conducted using a radar system as an example of a 3D safety-related sensor, and battery assembly scenarios are carried out for the comparison. In the experiment, two different orientations of the robot are tested, considering the potential runaway motion of the robot. Also, the maximum permissible speed of the robot is calculated by geometrical transfer energy, which is based on effective mass and the velocity of the manipulator and human injury criteria. From the experimental results, it is evident that it is better to avoid placing the vertical articulated robot in front of the operator from the perspective of minimizing the effect of runaway motion into the safety distance. Finally, the proposed framework of speed limitation is thought to be an effective method to link SSM and power and force limiting safety function.

Model accelerated reinforcement learning for high precision robotic assembly

Peg-in-hole assembly with narrow clearance is a typical robotic contact-rich task in industrial manufacturing. Robot learning allows robots to directly acquire the assembly skills for this task without modeling and recognizing the complex contact states. However, learning such skills is still challenging for robot because of the difficulties in collecting massive transitions data and transferring skills to new tasks, which inevitably leads to low training efficiency. This paper formulated the assembly task as a Markov decision process, and proposed a model accelerated reinforcement learning method to efficiently learn assembly policy. In this method, the assembly policy is learned with the maximum entropy reinforcement learning framework and executed with an impedance controller, which ensures exploration efficiency meanwhile allows transferring skills between tasks. To reduce sample complexity and improve training efficiency, the proposed method learns the environment dynamics with Gaussian Process while training policy, then, the learned dynamic model is utilized to improve target value estimation and generate virtual data to argument transition samples. This method can robustly learn assembly skills while minimizing real-world interaction requirements which makes it suitable for realistic assembly scenarios. To verify the proposed method, experiments on an industrial robot are conducted, and the results demonstrate that the proposed method improves the training efficiency by 31% compared with the method without model acceleration and the learned skill can be transferred to new tasks to accelerate the training for new policies.

Lowe syndrome patient cells display mTOR- and RhoGTPase-dependent phenotypes alleviated by rapamycin and statins.

Lowe syndrome (LS) is an X-linked developmental disease characterized by cognitive deficiencies, bilateral congenital cataracts and renal dysfunction. Unfortunately, this disease leads to the early death of affected children often due to kidney failure. Although this condition was first described in the early 1950s and the affected gene (OCRL1) was identified in the early 1990s, its pathophysiological mechanism is not fully understood and there is no LS-specific cure available to patients. Here we report two important signaling pathways affected in LS patient cells. While RhoGTPase signaling abnormalities led to adhesion and spreading defects as compared to normal controls, PI3K/mTOR hyperactivation interfered with primary cilia assembly (scenario also observed in other ciliopathies with compromised kidney function). Importantly, we identified two FDA-approved drugs able to ameliorate these phenotypes. Specifically, statins mitigated adhesion and spreading abnormalities while rapamycin facilitated ciliogenesis in LS patient cells. However, no single drug was able to alleviate both phenotypes. Based on these and other observations, we speculate that Ocrl1 has dual, independent functions supporting proper RhoGTPase and PI3K/mTOR signaling. Therefore, this study suggest that Ocrl1-deficiency leads to signaling defects likely to require combinatorial drug treatment to suppress patient phenotypes and symptoms.

The Next Generation Fornax Survey (NGFS): VII. A MUSE view of the nuclear star clusters in Fornax dwarf galaxies

ABSTRACT Clues to the formation and evolution of nuclear star clusters (NSCs) lie in their stellar populations. However, these structures are often very faint compared to their host galaxy, and spectroscopic analysis of NSCs is hampered by contamination of light from the rest of the system. With the introduction of wide-field integral field unit (IFU) spectrographs, new techniques have been developed to model the light from different components within galaxies, making it possible to cleanly extract the spectra of the NSCs and study their properties with minimal contamination from the light of the rest of the galaxy. This work presents the analysis of the NSCs in a sample of 12 dwarf galaxies in the Fornax Cluster observed with the Multi-Unit Spectroscopic Explorer (MUSE). Analysis of the stellar populations and star formation histories reveal that all the NSCs show evidence of multiple episodes of star formation, indicating that they have built up their mass further since their initial formation. The NSCs were found to have systematically lower metallicities than their host galaxies, which is consistent with a scenario for mass assembly through mergers with infalling globular clusters, whilst the presence of younger stellar populations and gas emission in the core of two galaxies is indicative of in-situ star formation. We conclude that the NSCs in these dwarf galaxies likely originated as globular clusters that migrated to the core of the galaxy that have built up their mass mainly through mergers with other infalling clusters, with gas-inflow leading to in-situ star formation playing a secondary role.

Impact of phages on soil bacterial communities and nitrogen availability under different assembly scenarios

BackgroundBacteriophages, the viruses infecting bacteria, are biological entities that can control their host populations. The ecological relevance of phages for microbial systems has been widely explored in aquatic environments, but the current understanding of the role of phages in terrestrial ecosystems remains limited. Here, our objective was to quantify the extent to which phages drive the assembly and functioning of soil bacterial communities. We performed a reciprocal transplant experiment using natural and sterilized soil incubated with different combinations of two soil microbial communities, challenged against native and non-native phage suspensions as well as against a cocktail of phage isolates. We tested three different community assembly scenarios by adding phages: (a) during soil colonization, (b) after colonization, and (c) in natural soil communities. One month after inoculation with phage suspensions, bacterial communities were assessed by 16S rRNA amplicon gene sequencing.ResultsBy comparing the treatments inoculated with active versus autoclaved phages, our results show that changes in phage pressure have the potential to impact soil bacterial community composition and diversity. We also found a positive effect of active phages on the soil ammonium concentration in a few treatments, which indicates that increased phage pressure may also be important for soil functions.ConclusionsOverall, the present work contributes to expand the current knowledge about soil phages and provide some empirical evidence supporting their relevance for soil bacterial community assembly and functioning.73gJaULr7UMQ2JCNToVonTVideo

How patchiness controls the properties of chain-like assemblies of colloidal platelets

Patchy colloidal platelets with non-spherical shapes have been realized with different materials at length scales ranging from nanometers to microns. While the assembly of these hard shapes tends to maximize edge-to-edge contacts, as soon as a directional attraction is added—by means of, e.g. specific ligands along the particle edges—a competition between shape and bonding anisotropy sets in, giving rise to a complex assembly scenario. Here we focus on a two-dimensional system of patchy rhombi, i.e. colloidal platelets with a regular rhombic shape decorated with bonding sites along their perimeter. Specifically, we consider rhombi with two patches, placed on either opposite or adjacent edges. While for the first particle class only chains can form, for the latter we observe the emergence of either chains or loops, depending on the system parameters. According to the patch positioning—classified in terms of different configurations, topologies and distances from the edge center—we are able to characterize the emerging chain-like assemblies in terms of length, packing abilities, flexibility properties and nematic ordering.

Automated scenario analysis of reinforcement learning controlled line-less assembly systems

The concept of line-less matrix-structured assembly systems with dynamical job routes is an alternative concept to line configurations with a promising performance for complex production scenarios. Due to the dynamical environment, fast online decisions for controlling the job routes are required. A reinforcement-learning algorithm based on Monte Carlo tree search for time-efficient online decision-making is proposed. For the performance comparison of matrix assembly systems with line configurations, a seamlessly automated scenario analysis tool generates, simulates and processes the results for a large set of assembly scenarios. The results show a significant improvement in the utilization of work stations especially for a high number of variants.

Advanced Human-Robot Collaborative Assembly Using Electroencephalogram Signals of Human Brains

This paper introduces an intelligent system that can manipulate an industrial robot using the electroencephalogram signals of human brains to perform collaborative assembly tasks. The system is initiated by capturing the brain signals using a wearable headset, and the signals are then filtered to remove any possible artifact. Consequently, the process continues by identifying the brain signals patterns using a classifier based on pre-recorded samples. The classifier’s output determines the proper matching of the robot command that is intended by the human. To validate the results, an industrial collaborative assembly scenario of a car manifold is examined as a case study.

Dynamic Value Stream Optimization for Manual Assembly in the Learning Factory for Cyber-Physical Production Systems

In terms of globalization, producing companies are facing a high international competition. Therefore, many companies have to enable their production systems and their employees for mass customization. Especially in manual assembly, rising product variance and complexity can lead to a lower overall equipment effectiveness (OEE) and higher error rates. In order to master rising product variance and complexity, digital assistance systems can support the production manager and the assembly worker by providing, transmitting and receiving product information ongoing and in real-time. This paper presents a process model for selecting digital assistance systems and developing an agile work organization in the learning factory for cyber-physical production systems (LVP). Based on an assembly scenario of remote-controlled cars, participants are supported by a digital dashboard, which visualizes their net production time, open capacity and level of synchronization in real time. First observations in the LVP show that participants are able to analyze their current value stream, detect the bottleneck assembly station and optimize their value stream by shifting participants to assembly stations with a higher net-production time or shifting assembly tasks to other assembly stations with a lower net-production time independently.

A System Architecture for CAD-Based Robotic Assembly With Sensor-Based Skills

Specifying assembly tasks in computer-aided design (CAD) level is a promising approach to intuitively program complex robot skills. In this article, a three-layered system architecture is presented to generate sensor-based robot skills from an assembly task instance. The architecture consists of an application layer where the user instantiates assembly tasks by specifying CAD constraints between geometric primitives pairs. A process layer infers the most suitable robot skills and their appropriate parameters. This inference is made possible by reasoning on a knowledge database represented as an ontology. The ontology contains semantic models of relevant classes such as tasks, skills, and geometric primitives as well as the relations between them. A control layer executes the sensor-based skills in real time using the eTaSL programming framework. A software implementation for the three layers is presented. The application layer is implemented in FreeCAD, whereas the process layer consists of a Web ontology language (OWL) ontology, a Prolog-based reasoner, and fuzzy inference to correctly select the skill and generate its parameters. In the control layer, the instantiated eTaSL skills execute the assembly tasks by sending an optimized control command to the robot. The system is validated on two challenging assembly cases with two distinct robot types, thus demonstrating the system's capability across different scenarios. Note to Practitioners-The widespread use of computer-aided design (CAD) models for describing parts assembly has motivated the research community to create systems that automatically generate robot programs satisfying the assembly goal. While most of the existing literature focuses on generating the assembly sequence, this article deals with the aspect of translation from CAD-level assembly specification to executable robot motion, also called skills. This article systematically addresses the problem by dividing it into different layers and solving them separately. Parameters that influence the successful execution of an assembly task are identified and categorized into application- and process-related parameters. Different inference techniques are employed to address each parameter category. Experimental results show that the proposed system can successfully generate and execute robot skills for assembly scenarios of an air compressor and an electric motor.

On the assessment of human-robot collaboration in mechanical product assembly by use of Virtual Reality

This work focuses on human - robot collaboration as exemplified in modern assembly cells. Assembly with robotic assistance was studied, in order to investigate in quantitative terms the robot contribution in collaboration with the human towards better efficiency and avoidance of musculoskeletal strain. The above were studied, presented and discussed on the basis of a case study concerning collaboration of a human with a virtual robot in assembling a prototype robotic filament winding head. The product consists of a number of mechanical engineering artifacts, such as a frame, axles, wheels, springs etc. The virtual environment was constructed in Unity 3DTM, whilst Leap MotionTM was used to track user’s hands and a Head Mounted Display was exploited for immersion and navigation purposes. Multiple experiments with an audience of mechanical engineering students were conducted. Data collected from the execution of assembly scenarios was processed in order to provide both ergonomic indices and completion time that quantify the human – robot equilibrium in assembly tasks. Such indices can be easily relaced by others, if a different collaboration scenario between human and robot is to be set up.

Flexible Framework to Model Industry 4.0 Processes for Virtual Simulators

Virtual reality (VR)- and augmented reality (AR)-based simulations are key technologies in Industry 4.0 which allow for testing and studying of new processes before their deployment. A simulator of industrial processes needs a flexible way in which to model the activities performed by the worker and other elements involved, such as robots and machinery. This work proposes a framework to model industrial processes for VR and AR simulators. The desk method was used to review previous research and extract the most important features of current approaches. Novel features include interaction among human workers and a variety of automation systems, such as collaborative robots, a broader set of tasks (including assembly and disassembly of components), flexibility of modeling industrial processes for different domains and purposes, a clear separation of process definition and simulator, and independence of specific programming languages or technologies. Three industrial scenarios modeled with this framework are presented: an aircraft assembly scenario, a guidance tool for high-voltage cell security, and an application for the training of machine-tool usage.