Robotic Manufacturing Research Articles

Cloud Manufacturing Service Composition Optimization Based on Improved Chaos Sparrow Search Algorithm with Time-Varying Reliability and Credibility Evaluation

The economic friction and political conflicts between some countries and regions have made multinational corporations increasingly focus on the reliability and credibility of manufacturing supply chains. In view of the impact of poor manufacturing entity reliability and service reputation on the new-era manufacturing industry, the time-varying reliability and time-varying credibility of cloud manufacturing (CMfg) services were studied from the perspective of combining nature and society. Taking time-varying reliability, time-varying credibility, composition complexity, composition synergy, execution time, and execution cost as objective functions, a new six-dimension comprehensive evaluation model of service quality was constructed. To solve the optimization problem, this study proposes an improved chaos sparrow search algorithm (ICSSA), where the Bernoulli chaotic mapping formula was introduced to improve the basic sparrow search algorithm (BSSA), and the position calculation formulas of the explorer sparrow and the scouter sparrow were enhanced. The Bernoulli chaotic operator changed the symmetry of the BSSA, increased the uncertainty and randomness of the explorer sparrow position in the new algorithm, and affected the position update and movement strategies of the follower and scouter sparrows. The asymmetric chaotic characteristic brought better global search ability and optimization performance to the ICSSA. The comprehensive performance of the service composition (SvcComp) scheme was evaluated by calculating weighted relative deviation based on six evaluation elements. The WFG and DTLZ series test functions were selected, and the inverse generation distance (IGD) index and hyper volume (HV) index were used to compare and evaluate the convergence and diversity of the ICSSA, BSSA, PSO, SGA, and NSGA-III algorithms through simulation analysis experiments. The test results indicated that the ICSSA outperforms the BSSA, PSO, SGA, and NSGA-III in the vast majority of testing issues. Finally, taking disinfection robot manufacturing tasks as an example, the effectiveness of the proposed CMfg SvcComp optimization model and the ICSSA were verified. The case study results showed that the proposed ICSSA had faster convergence speed and better comprehensive performance for the CMfg SvcComp optimization problem compared with the BSSA, PSO, SGA, and NSGA-III.

Robotic additive manufacturing system: development of suitable range of process parameters for 3D printing of a large-sized object in PLA polymer

Robotic additive manufacturing system: development of suitable range of process parameters for 3D printing of a large-sized object in PLA polymer

Design, Manufacturing and Testing of a Two-Wheeled Self-Balancing Robot

This work presents a self-balancing robot's modeling, manufacturing, and testing. This study aimed to design and construct a robot capable of maintaining balance while stationary and in motion, employing an effective control strategy. The robot's design incorporates sensors, microcontrollers, actuators, and control algorithms. The control strategy involved implementing an LQR (Linear Quadratic Regulator) controller and a Kalman filter for state estimation. The results demonstrate the robot's ability to effectively maintain balance and navigate flat terrain while controlled by a joystick. This study provides valuable insights into the design and control of self-balancing robots.

A curved layering algorithm based on voxelization and geodesic distance for robotic GMA additive manufacturing

ABSTRACT Curved layering has become one of the current research hotspots for its unique advantages. In order to improve the adaptability and accuracy, this paper proposes a new curved layering and path planning algorithm based on voxelization and geodesic distance. High density discrete points are used to approximate the shortest path, voxelization and new optimized search algorithm are adopted to obtain the geodesic distance field within stereolithography (STL) model. Curved layers are formed from points with equal geodesic distance values. The developed algorithm overcomes the limitations of the existing facet offsetting method. Moreover, robot positioner angle planning strategies are exploited to achieve good formation for support-free curved layers of spatial overhang characteristics. The results of comparative analysis and verification experiment indicate that the algorithm accuracy is not affected by the size and intersection angle of the triangles, satisfies the high-precision requirements for parts with curved surface in robotic GMA additive manufacturing.

An Empirical Study on the Willingness of Young Elderly to Use Elderly Companion Robot

In the context of an aging population, this study addresses the deficiency in existing research, which predominantly explores the willingness to utilize elderly companion robots from a positive standpoint. Our aim is to conduct a comprehensive analysis of the factors influencing the willingness of elderly individuals, with a focus on the young elderly population, to use elderly companion robot. We also intend to offer insights for the design and operation of these elderly companion robot. This study adopts the Behavioral Reasoning Theory (BRT) as its theoretical framework and incorporates the extended Unified Theory of Acceptance and Use of Technology (UTAUT2) model to examine the impact of various factors on users’ attitudes and behavioral intentions. We approach this analysis from both “reasons for” and “reasons against” perspectives, utilizing structural equation modeling as our analytical method to explore how each factor affects users’ attitudes and behavioral intentions. Values positively influence performance expectancy, social impact, facilitating conditions, and hedonic motivation, while negatively affecting hindering expectancy, low price value, unhabit, and privacy issues. Performance expectancy, facilitating conditions, and hedonic motivation have a positive impact on older people’s attitudes toward elderly companion robots, while unhabit negatively influences their attitudes. Social impact and hindering expectancy positively and negatively impact the elderly’s willingness to use elderly companion robots. Designers and manufacturers of robots can enhance the willingness of elderly individuals to use elderly companion robots by improving positive elements and addressing or modifying negative elements.

Robotic wire-based friction stir additive manufacturing

High axial force requirement for existing friction stir additive manufacturing technologies is a substantial obstacle to manufacturing of complex reliable components by high-freedom robots. Here, we proposed a new robotic wire-based friction stir additive manufacturing (R-WFSAM) method, characterized by paraxial feeding wires and pre-plasticization process, to enable the formation of deposited layers with significantly low force. The spiral groove design on the screw tool facilitates the pre-plasticization of materials prior to deposition and the continuous extrusion of the material, reducing axial force and diminishing the reliance on high-stiffness equipment for solid-state relevant technologies. Fully dense aluminum alloy components on the 2D plane and 3D curved surface by hybrid position/force control were successfully manufactured. The sufficient material flow and material mixing behavior during deposition on the plane induced by the multi-pin design strengthened the interfacial bonding. Homogeneous and fine grains were also achieved due to severe plastic deformation. The R-WFSAM Al-Si alloy component possessed an ultimate tensile strength of 230±5 MPa and an uniform elongation of 28.1±1.0 %, exhibiting excellent isotropic mechanical properties. This method provides a novel approach for manufacturing large-size complex structural components with high flexibility and field re-manufacturing.

Robotic Rehabilitation in India: A Survey of Attitudes and Perceptions

Abstract Introduction: Robotic rehabilitation is a recent advancement in the field of neurorehabilitation that can benefit patients suffering from traumatic brain injuries, strokes and spinal cord injuries. Despite its availability in India since more than a decade, the number of people who are availing such benefits are a minority of the patient population. Materials and Methods: The aim of the study was to understand the trends and perceptions of people from medical and non-medical professions. The objective of the study was to evaluate the awareness levels and general perceptions about robotics in rehabilitation, across people from healthcare and other fields including rehabilitation physicians from various parts of the country, through an open survey and using an 11-point scoring system for objective evaluation. The research work was conducted as an observational survey-based study. Questions related to awareness levels, self-perceived levels of affordability and effectiveness of robotics in the field of rehabilitation were covered through a questionnaire made by the author, completely masking the personal information of the respondents to ensure anonymity. Results: A total of 145 people from different parts of the country belonging to both healthcare and other fields responded. Scores were assessed out of 10, in all domains. The results show a significant number of people being completely unaware of robotics in the field of rehabilitation, with a low average awareness score of 3.4 out of 10. This trend was also seen amongst medical professionals. Although the reported levels of affordability were low at 3.6, the average scores of perceived effectiveness were high at 5.9. Most people believed financial limitations and lack of awareness and availability as common barriers of implementation of robotic rehabilitation in a country like India. Conclusion: This study shows that despite the availability of advanced resources in major cities in the country, there is a lack of awareness amongst doctors and members from other fields, which may result in potential benefiters not being able to make informed decisions about rehabilitative options. Although doctors of the field of rehabilitation generally had a more positive response, the rest of the members are largely unaware. Improving the availability and accessibility to such advances will help provide equal opportunity of access to clients who could benefit from the same. Upscaling the manufacturing and development of indigenous robots for rehabilitation, amongst other methods may help reduce the expenses on users, thereby improving logistic and financial accessibility and availability.

Electric Vehicle Battery Disassembly Using Interfacing Toolbox for Robotic Arms

This paper showcases the integration of the Interfacing Toolbox for Robotic Arms (ITRA) with our newly developed hybrid Visual Servoing (VS) methods to automate the disassembly of electric vehicle batteries, thereby advancing sustainability and fostering a circular economy. ITRA enhances collaboration between industrial robotic arms, server computers, sensors, and actuators, meeting the intricate demands of robotic disassembly, including the essential real-time tracking of components and robotic arms. We demonstrate the effectiveness of our hybrid VS approach, combined with ITRA, in the context of Electric Vehicle (EV) battery disassembly across two robotic testbeds. The first employs a KUKA KR10 robot for precision tasks, while the second utilizes a KUKA KR500 for operations needing higher payload capacity. Conducted in T1 (Manual Reduced Velocity) mode, our experiments underscore a swift communication protocol that links low-level and high-level control systems, thus enabling rapid object detection and tracking. This allows for the efficient completion of disassembly tasks, such as removing the EV battery’s top case in 27 s and disassembling a stack of modules in 32 s. The demonstrated success of our framework highlights its extensive applicability in robotic manufacturing sectors that demand precision and adaptability, including medical robotics, extreme environments, aerospace, and construction.

Soft Robot Design, Manufacturing, and Operation Challenges: A Review

Advancements in smart manufacturing have embraced the adoption of soft robots for improved productivity, flexibility, and automation as well as safety in smart factories. Hence, soft robotics is seeing a significant surge in popularity by garnering considerable attention from researchers and practitioners. Bionic soft robots, which are composed of compliant materials like silicones, offer compelling solutions to manipulating delicate objects, operating in unstructured environments, and facilitating safe human–robot interactions. However, despite their numerous advantages, there are some fundamental challenges to overcome, which particularly concern motion precision and stiffness compliance in performing physical tasks that involve external forces. In this regard, enhancing the operation performance of soft robots necessitates intricate, complex structural designs, compliant multifunctional materials, and proper manufacturing methods. The objective of this literature review is to chronicle a comprehensive overview of soft robot design, manufacturing, and operation challenges in conjunction with recent advancements and future research directions for addressing these technical challenges.

Detecting Anomalous Robot Motion in Collaborative Robotic Manufacturing Systems

Detecting Anomalous Robot Motion in Collaborative Robotic Manufacturing Systems

Deep learning assisted fabrication of metallic components using the robotic wire arc additive manufacturing

PurposeBecause of the advantages of high deposition efficiency and low manufacturing cost compared with other additive technologies, robotic wire arc additive manufacturing (WAAM) technology has been widely applied for fabricating medium- to large-scale metallic components. The additive manufacturing (AM) method is a relatively complex process, which involves the workpiece modeling, conversion of the model file, slicing, path planning and so on. Then the structure is formed by the accumulated weld bead. However, the poor forming accuracy of WAAM usually leads to severe dimensional deviation between the as-built and the predesigned structures. This paper aims to propose a visual sensing technology and deep learning–assisted WAAM method for fabricating metallic structure, to simplify the complex WAAM process and improve the forming accuracy.Design/methodology/approachInstead of slicing of the workpiece modeling and generating all the welding torch paths in advance of the fabricating process, this method is carried out by adding the feature point regression branch into the Yolov5 algorithm, to detect the feature point from the images of the as-built structure. The coordinates of the feature points of each deposition layer can be calculated automatically. Then the welding torch trajectory for the next deposition layer is generated based on the position of feature point.FindingsThe mean average precision score of modified YOLOv5 detector is 99.5%. Two types of overhanging structures have been fabricated by the proposed method. The center contour error between the actual and theoretical is 0.56 and 0.27 mm in width direction, and 0.43 and 0.23 mm in height direction, respectively.Originality/valueThe fabrication of circular overhanging structures without using the complicate slicing strategy, turning table or other extra support verified the possibility of the robotic WAAM system with deep learning technology.

Nanocellulose-mediated bilayer hydrogel actuators with thermo-responsive, shape memory and self-sensing performances

Inspired by creatures, abundant stimulus-responsive hydrogel actuators with diverse functionalities have been manufactured for applications in soft robotics. However, constructing a shape memory and self-sensing bilayer hydrogel actuator with high mechanical strength and strong interfacial bonding still remains a challenge. Herein, a novel bilayer hydrogel with a stimulus-responsive TEMPO-oxidized cellulose nanofibers/poly(N-isopropylacrylamide) (TOCN/PNIPAM) layer and a non-responsive TOCN/polyacrylamide (TOCN/PAM) layer is proposed as a thermosensitive actuator. TOCNs as a nano-reinforced phase provide a high mechanical strength and endow the hydrogel actuator with a strong interfacial bonding. Due to the incorporation of TOCNs, the TOCN/PNIPAM hydrogel exhibits a high compressive strength (~89.2 kPa), elongation at break (~170.7 %) and tensile strength (~24.0 kPa). The prepared PNIPAM/TOCN/PAM hydrogel actuator performs the roles of an encapsulation, jack, temperature-controlled fluid valve and temperature-control manipulator. The incorporation of Fe3+ further endows the bilayer hydrogel actuator with a synergistic performance of shape memory and temperature-driven, which can be used as a temperature-responsive switch to detect ambient temperature. The PNIPAM/TOCN/PAM-Fe3+ conductive hydrogel can be assembled into a flexible sensor and generate sensing signals when driven by temperature changes to achieve real-time feedback. This research may lead to new insights into the design and manufacturing of intelligent flexible soft robots.

Research on Key Technologies of Artificial Intelligence for Intelligent Manufacturing

Intelligent manufacturing, as the main form of future manufacturing, is the highland of a new round of competition in the global manufacturing industry, which will fundamentally change the production and manufacturing methods and technological and economic paradigms formed by human beings since the industrial revolution. At present, the intelligent manufacturing industry is still in the early stage of development, which is characterized by three typical characteristics: continuous growth of market size, strong promotion by national governments and diversification of competition subjects. At present, China's manufacturing industry is facing transformation, and the manufacturing industry is bound to need to transform to intelligent manufacturing. Industrial robots are the main content of current intelligent manufacturing, how to fully apply artificial intelligence technology to the manufacturing of industrial robots, so that its manufacturing efficiency, manufacturing accuracy and other product attributes are improved, to maximize its economic benefits, has a particularly important application value. The application of intelligent manufacturing and robots can not only promote the faster development of manufacturing, but also reconstruct new forms of manufacturing. Based on this, the paper first summarizes the application technology of intelligent manufacturing, and then analyzes the key technology of robot application for reference to relevant workers.

Implementing machine learning in robotic wire arc additive manufacturing for minimizing surface roughness

ABSTRACT Wire arc additive manufacturing (WAAM) has emerged as an important type of metal additive manufacturing technique in recent years. The most critical challenge in WAAM is to achieve the desired surface roughness of deposited layers. The improvement of surface roughness can play an important role in improving the quality of parts made using WAAM. In this study, three machine learning (ML) algorithms, such as artificial neural network (ANN), random forest (RF) and XGboost algorithm, have been used to predict the surface roughness of deposited beads. A dataset for training and testing the ML models has been obtained using a 3D optical profilometer by measuring the surface roughness of a single-layer deposited bead of aluminium alloy. The performance of ML algorithms was evaluated using statistical metrics such as mean square error (MSE), mean absolute error (MAE) and coefficient of determination (R2). Tuning of hyperparameters helped in improving the performance of ML models. Among the three ML algorithms, the XGBoost and ANN algorithms outperformed RF in predicting the roughness of WAAM deposited parts. This novel approach can help industries in the modelling of process parameters and easy implementation of WAAM with minimal resources, just with the use of real-time data.

Latest Innovation in Robotics

Robotics is the science of creating and assembling tangible robots to enhance automation and creativity. Engineering and computer science are combined in the field of robotics, which deals with the creation, manufacturing, and use of robots. Robotics is undergoing a fast evolution with ground-breaking discoveries that are changing entire sectors and societal contexts. Recent advances in AI have accelerated robotics' progress toward more flexibility and autonomy. There are many different types of robotics. A robot could be an artificial intelligence (AI) device that looks like a person, or it could be a robotic application like robotic process automation, which resembles how people interact with software to carry out repetitive, rule-based tasks. The continued convergence of robotics with artificial intelligence, materials science, and other interdisciplinary domains holds the potential to open up new avenues for automation and human-robot cooperation. This study offers an overview of the most recent advancements in robotics, including significant technological innovations, cutting-edge applications, and developing trends

High-Throughput Additive Manufacturing of Continuous Carbon Fiber-Reinforced Plastic by Multifilament.

Additive manufacturing (or 3D printing) of continuous carbon fiber-reinforced plastics with fused deposition modeling is a burgeoning manufacturing method because of its potential as a powerful approach to produce lightweight, high strength and complex parts without the need for a mold. Nevertheless, it cannot manufacture parts rapidly due to low throughput. This paper proposes a high-throughput additive manufacturing of continuous carbon fiber-reinforced plastics by multifilament with reference to fiber tape placement. Three filaments were fed and compaction printed simultaneously by a robotic manufacturing system. The coupled thermal-mechanical model of the filament deformation during printing was developed to eliminate the initial interval between the filaments and improved mechanical properties. Furthermore, the mathematical relationship between filament deformation and printing parameters consisting of printing temperature, printing speed and roller pressure was proposed using response surface methodology with the line width as the response. The tensile tests demonstrate that the tensile properties of printed parts are positively correlated with the line width, but not infinitely improved. The maximum tensile strength and tensile modulus are 503.4 MPa and 83.11 Gpa, respectively, which are better than those obtained by traditional methods. Void fraction and scanning electron microscope images also reveal that the appropriate line width achieved by the reasonable printing parameters contributes to the high-throughput multifilament additive manufacturing of continuous carbon fiber-reinforced plastics. The comparison results indicate that the high-throughput multifilament additive manufacturing proposed in this paper can effectively improve the speed of continuous carbon fiber-reinforced plastics additive manufacturing without degrading the mechanical performance.

Development of a robotic cluster for automated and scalable cell therapy manufacturing

Background aimsThe production of commercial autologous cell therapies such as chimeric antigen receptor T cells requires complex manual manufacturing processes. Skilled labor costs and challenges in manufacturing scale-out have contributed to high prices for these products. MethodsWe present a robotic system that uses industry-standard cell therapy manufacturing equipment to automate the steps involved in cell therapy manufacturing. The robotic cluster consists of a robotic arm and customized modules, allowing the robot to manipulate a variety of standard cell therapy instruments and materials such as incubators, bioreactors, and reagent bags. This system enables existing manual manufacturing processes to be rapidly adapted to robotic manufacturing, without having to adopt a completely new technology platform. Proof-of-concept for the robotic cluster's expansion module was demonstrated by expanding human CD8+ T cells. ResultsThe robotic cultures showed comparable cell yields, viability, and identity to those manually performed. In addition, the robotic system was able to maintain culture sterility. ConclusionsSuch modular robotic solutions may support scale-up and scale-out of cell therapies that are developed using classical manual methods in academic laboratories and biotechnology companies. This approach offers a pathway for overcoming manufacturing challenges associated with manual processes, ultimately contributing to the broader accessibility and affordability for personalized immunotherapies.

Sensitivity Analysis of Performance Tests for Six-Degree-of-Freedom Serial Industrial Robots

Abstract The international standard ISO 9283:1998 is popular for performance tests of industrial robots at present. It is desirable that the tests described in this standard should be sensitive to error sources of robot end positioning/orientation. In this paper, first, the kinematic and the joint stiffness model parameters are identified experimentally for two models of six-DOF (degree-of-freedom) serial industrial robots (i.e., the ABB IRB 1410 and UR5 robots). Then, the standard deviations of the derived model parameters are obtained as error inputs for the sensitivity analysis of the performance tests including the positioning/orientation accuracy/repeatability tests. By simulating the error sensitivity of the positioning/orientation accuracy/repeatability test methods for industrial robots, it is analyzed whether the tests described in the ISO 9283:1998 Standard are sensitive to the focused error sources, showing the limitations of the evaluation index of the ISO 9283:1998 Standard. The results show that for six-DOF serial industrial robots, the positioning accuracy test is the key to determining their motion performance. The orientation accuracy and repeatability tests are not necessary if the positioning accuracy and repeatability tests can be done for six-DOF serial industrial robots. Finally, the improvement suggestion of the performance test method is proposed. The research of this paper is beneficial for improving the performance evaluation methods of industrial robots. It can also help robot manufacturing enterprises analyze and improve the positioning/orientation accuracy/repeatability of their products.

A novel vision-based multi-functional sensor for normality and position measurements in precise robotic manufacturing

Cobots play an essential role in the fourth industrial revolution and the automation of complex manufacturing processes. However, cobots still face challenges in achieving high precision, which obstructs their usage in precise applications such as the aerospace industry. Nonetheless, advances in perception systems unlock new cobot manufacturing capabilities. This paper presents a novel multi-functional sensor that combines visual and tactile feedback using a single optical sensor, featuring a moving gate mechanism. This work also marks the first integration of Vision-Based Tactile Sensing (VBTS) into a robotic machining end-effector. The sensor provides vision-based tactile perception capabilities for precise normality control and exteroceptive perception for robot localization and positioning. Its performance is experimentally demonstrated in a precise robotic deburring application, where the sensor achieves the high-precision requirements of the aerospace industry with a mean normality error of 0.13° and a mean positioning error of 0.2 mm. These results open a new paradigm for using vision-based sensing for precise robotic manufacturing, which surpasses conventional approaches in terms of precision, weight, size, and cost-effectiveness.

Robotic surgery: public perceptions and current misconceptions

Whilst surgeons and robotic companies are key stakeholders involved in the adoption of robotic assisted surgery (RS), the public’s role is overlooked. However, given that patients hold ultimate power over their healthcare decisions, public acceptance of RS is crucial. Therefore, this study aims to identify public understanding, opinions, and misconceptions about RS. An online questionnaire distributed between February and May 2021 ascertained the views of UK adults on RS. The themes of questions included familiarity, experience and comfort with RS, opinions on its ethical implications, and the impact of factual information provided to the participant. The data were evaluated using thematic and statistical analysis, including assessing for statistical differences in age, gender, education level, and presence in the medical field. Overall, 216 responses were analysed. Participants were relatively uninformed about RS, with a median knowledge score of 4.00(2.00–6.00) on a 10-point Likert scale. Fears surrounding increased risk, reduced precision and technological failure were identified, alongside misconceptions about its autonomous nature. However, providing factual information in the survey about RS statistically increased participant comfort (p = < 0.0001). Most (61.8%) participants believed robot manufacturers were responsible for malfunctions, but doctors were held accountable more by older, less educated, and non-medical participants. Our findings suggest that there is limited public understanding of RS. The numerous common misconceptions identified present a major barrier to the widespread acceptance of RS, since inaccurate fears about its nature could discourage potential patients from engaging with robotic procedures.