3D printing towards implementing Industry 4.0: sustainability aspects, barriers and challenges

Purpose Environmental degradation has emerged as one of the major limitations of industrial revolution and has led to an increased focus towards developing sustainable strategies and techniques. This paper aims to highlight the sustainability aspects of three-dimensional (3D) printing technology that helps towards a better implementation of Industry 4.0. It also aims to provide a brief picture of relationships between 3D printing, Industry 4.0 and sustainability. The major goal is to facilitate the researchers, scholars, engineers and recommend further research, development and innovations in the field. Design/methodology/approach The various enabling factors for implementation of Industry 4.0 are discussed in detail. Some barriers to incorporation of 3D Printing, its applications areas and global market scenario are also discussed. A through literature review has been done to study the detailed relationships between 3D printing, Industry 4.0 and sustainability. Findings The technological benefits of 3D printing are many such as weight savings, waste minimization and energy savings. Further, the production of new 3D printable materials with improved features helps in reducing the wastage of material during the process. 3D printing if used at a large scale would help industries to implement the concept of Industry 4.0. Originality/value This paper focuses on discussing technological revolution under Industry 4.0 and incorporates 3D printing-type technologies that largely change the product manufacturing scenario. The interrelationships between 3D printing, Industry 4.0 and sustainability have been discussed.

Intelligent obstacle avoidance path planning method for picking manipulator combined with artificial potential field method

Purpose The results of obstacle avoidance path planning for the manipulator using artificial potential field (APF) method contain a large number of path nodes, which reduce the efficiency of manipulators. This paper aims to propose a new intelligent obstacle avoidance path planning method for picking robot to improve the efficiency of manipulators. Design/methodology/approach To improve the efficiency of the robot, this paper proposes a new intelligent obstacle avoidance path planning method for picking robot. In this method, we present a snake-tongue algorithm based on slope-type potential field and combine the snake-tongue algorithm with genetic algorithm (GA) and reinforcement learning (RL) to reduce the path length and the number of path nodes in the path planning results. Findings Simulation experiments were conducted with tomato string picking manipulator. The results showed that the path length is reduced from 4.1 to 2.979 m, the number of nodes is reduced from 31 to 3 and the working time of the robot is reduced from 87.35 to 37.12 s, after APF method combined with GA and RL. Originality/value This paper proposes a new improved method of APF, and combines it with GA and RL. The experimental results show that the new intelligent obstacle avoidance path planning method proposed in this paper is beneficial to improve the efficiency of the robotic arm. Graphical abstract Figure 1 According to principles of bionics, we propose a new path search method, snake-tongue algorithm, based on a slope-type potential field. At the same time, we use genetic algorithm to strengthen the ability of the artificial potential field method for path searching, so that it can complete the path searching in a variety of complex obstacle distribution situations with shorter path searching results. Reinforcement learning is used to reduce the number of path nodes, which is good for improving the efficiency of robot work. The use of genetic algorithm and reinforcement learning lays the foundation for intelligent control.

Design and kinematic analysis of new multi-mode mobile parallel mechanism with deployable platform

Purpose This study aims to make the mobile robot better adapt to the patrol and monitoring in industrial field substation area, a multi-mode mobile carrying mechanism which can carrying data collector, camera and other equipment is designed. Design/methodology/approach Based on the geometric axis analysis and interference analysis, the multi-mode mobile carrying mechanism is designed. The screw constraint topological theory and zero-moment point (ZMP) theory is used to kinematic analysis in mechanism mobile process. Findings The mobile carrying mechanism can realize the folding movement, hexagonal rolling and quadrilateral rolling movement. A series of simulation and prototype experiment results verify the feasibility and actual error of the design analysis. Originality/value The work of this paper provides a mobile carrying mechanism for carrying different data acquisition equipment and surveillance camera in industrial field substation zone. It has excellent folding performance and mobile capabilities. The mobile carrying mechanism reduces the workload of human being and injuries suffered by workers in industrial substation area.

Mechanical performance analysis and experimental study of soft-bodied bird-billed pneumatic gripper

Purpose Flexible mechanical gripper has better safety and adaptability than a rigid mechanical hand. At present, there are few soft grippers for small objects on a millimeter scale. Therefore, the purpose of this paper is to design a soft pneumatic gripper for grasping millimeter-scale small and fragile objects such as jewelry and electronic components. Design/methodology/approach By simulating the clamping action of the bird’s mouth and combining the high flexibility of the soft material, the bird’s beak soft pneumatic gripper is designed. First, the internal cavity of the gripping end of the gripper is determined by bending deformation calculation, and the brief manufacturing process of the gripper is outlined. Then, the single finger of the soft gripper is modeled mechanically, and the relationship between air pressure and bending deformation of the single finger is obtained. Finally, the experimental platform of the soft mechanical gripper is built, and the gripping performance of silicone rubber material is tested by comparison test, bending deformation test, stability test, adaptability test and gripping accuracy test. Findings The designed gripper has the advantages of simple structure, convenient operation, easy grasping of different small objects of millimeter-scale and good adaptability. It can grasp the precise dispensing needle with a minimum diameter of 0.19 mm, and its accuracy meets daily use. Originality/value A new type of soft pneumatic, the mechanical gripper is proposed and manufactured. According to the shape of the bird’s beak and the calculation of bending performance, a hollow finger gripper with better bending performance is designed. Various test results show that the gripper has a significant clamping effect on millimeter small objects, which supplements the research field of millimeter small object gripper.

Identification of particular hydrodynamic parameters for a modular type 4 DOF underwater vehicle by means of CFD method

Purpose The development of robust control algorithms for the position, velocity and trajectory control of unmanned underwater vehicles (UUVs) depends on the accuracy of their mathematical models. Accuracy of the model is determined by precise estimation of the UUV hydrodynamic parameters. The purpose of this study is to determine the hydrodynamic forces and moments acting on an underwater vehicle with complex body geometry and moving at low speeds and to achieve the accurate coefficients associated with them. Design/methodology/approach A three-dimensional (3D) computer-aided design (CAD) model of UUV is designed with one-to-one dimensions. 3D fluid flow simulations are conducted using computational fluid dynamics (CFD) software programme in the solution of Navier Stokes equations for laminar and turbulent flow analysis. The coefficients depending on the hydrodynamic forces and moments are determined by the external flow analysis using the CFD programme. The Flow Simulation k-ε turbulence model is used for the transition from laminar flow to turbulent flow. Hydrodynamic properties such as lift and drag coefficients and roll and yaw moment coefficients are calculated. The parameters are compared with the coefficient values found by experimental methods. Findings Although the modular type UUV has a complex body geometry, the comparative results of the experiments and simulations confirm that the defined model parameters are accurate and close to the actual experimental values. In the proposed k-ε method, the percentage error in the estimation of drag and lifting coefficients is decreased to 4.2% and 8.39%, respectively. Practical implications The model coefficients determined in this study can be used in high-level control simulations which leads to the development of robust real-time controllers for complex-shaped modular UUVs. Originality/value The Lucky Fin UUV with 4 degrees of freedom is a specific design and its CAD model is first extracted. Verification of simulation results by experiments is generally less referenced in studies. However, it provides more precise parameter identification of the model. Proposed study offers a simple and low-cost experimental measurement method for verification of the hydrodynamic parameters. The extracted model and coefficients are worthwhile references for the analysis of modular type UUVs.

The Pransky interview: Dr Raffaello D’Andrea, Founder, CEO, and Chairman of the board at Verity; Entrepreneur; Professor; Scientist and Artist

Purpose The following article is a “Q&A interview” conducted by Joanne Pransky of Industrial Robot Journal as a method to impart the combined technological, business and personal experience of a prominent, robotic industry PhD and inventor regarding his pioneering efforts and the commercialization of bringing a technological invention to market. This paper aims to discuss these issues. Design/methodology/approach The interviewee is Dr Raffaello D’Andrea, a highly successful entrepreneur and proven business leader and one of the world’s foremost leaders in robotics and machine learning. D’Andrea is Founder, CEO and Chairman of the Board at Verity, the world’s leading autonomous indoor drone company, as well as a Professor of Dynamic Systems and Control at the Swiss Federal Institute of Technology (ETH) in Zurich. D’Andrea is also one of the co-founders and advisors of Robo-Global, an index and research company focused on investments in robotics, automation and artificial intelligence. In this interview, D’Andrea shares some of his business and personal experiences of working in industry and academia and his criteria for turning his ideas into successful working systems. Findings Raffaello D’Andrea’s entire career is built on his ability to bridge theory and practice. D’Andrea combined his love for science with his need to create and received a BS degree in engineering science at the University of Toronto, where he was awarded the Wilson Medal as the top graduating student in 1991. He obtained both his MS and PhD degrees in electrical engineering at Caltech, and then he joined the Cornell faculty as an assistant professor. While on leave from Cornell, from 2003 to 2007, he co-founded the disruptive warehouse automation company Kiva Systems, where he led the systems architecture, robot design, robot navigation and coordination, and control algorithms efforts. In 2014, D’Andrea took robotics technology into the air and founded Verity, the world’s first company to deliver a fully integrated autonomous, indoor drone-based system solution. Originality/value Raffaello D’Andrea combines academia, business and the arts to reinvent autonomous systems. D’Andrea was a founding member of the Systems Engineering Program at Cornell, where he established robot soccer as the flagship, multidisciplinary team project. In addition to pioneering the use of semi-definite programming for the design of distributed control systems, he went on to lead the Cornell Robot Soccer Team to win four world international RoboCup championships. Kiva Systems, co-founded by D’Andrea and acquired by Amazon in 2012, helped the re-branded Amazon Robotics to disrupt the entire warehousing and logistics systems industry. Additionally, D’Andrea is an internationally-exhibited new media artist, best known for the Robotic Chair (Ars Electronica, ARCO, London Art Fair, National Gallery of Canada) and Flight Assembled Architecture (FRAC Centre). With his team at Verity, he created the drone design and choreography for Cirque Du Soleil’s Paramour on Broadway, Metallica’s WorldWired Tour and Céline Dion’s Courage Tour. Other D’Andrea creations include the Flying Machine Arena, where flying robots perform aerial acrobatics, juggle balls, balance poles and cooperate to build structures; the Distributed Flight Array, a flying platform consisting of multiple autonomous single propeller vehicles that are able to drive, dock with their peers and fly in a coordinated fashion; the Balancing Cube, a dynamic sculpture that can balance on any of its edges or corners and its little brother Cubli, a small cube that can jump up, balance and walk; Blind Juggling Machines that can juggle balls without seeing them, and without catching them. D’Andrea is also collaborating with scientists, engineers, and wingsuit pilots to create an actively controlled suit that will allow humans to take off and land at will, to gain altitude, even to perch, while preserving the intimacy of wingsuit flight. D’Andrea has received the IEEE Robotics and Automation Award, the Engelberger Robotics Award, the IEEE/IFR Invention and Entrepreneurship Award in Robotics and Automation and the Presidential Early Career Award for Scientists and Engineers. In 2020, he was inducted in the National Inventors Hall of Fame and elected to the National Academy of Engineering.

Design and slip prevention control of a multi-sensory anthropomorphic prosthetic hand

Purpose The purpose of this paper is to design a multi-sensory anthropomorphic prosthetic hand and a grasping controller that can detect the slip and automatically adjust the grasping force to prevent the slip. Design/methodology/approach To improve the dexterity, sensing, controllability and practicability of a prosthetic hand, a modular and multi-sensory prosthetic hand was presented. In addition, a slip prevention control based on the tactile feedback was proposed to improve the grasp stability. The proposed controller identifies slippages through detecting the high-frequency vibration signal at the sliding surface in real time and the discrete wavelet transform (DWT) was used to extract the eigenvalues to identify slippages. Once the slip is detected, a direct-feedback method of adjusting the grasp force related with the sliding times was used to prevent it. Furthermore, the stiffness of different objects was estimated and used to improve the grasp force control. The performances of the stiffness estimation, slip detection and slip control are experimentally evaluated. Findings It was found from the experiment of stiffness estimation that the accuracy rate of identification of the hard metal bottle could reach to 90%, while the accuracy rate of identification of the plastic bottles could reach to 80%. There was a small misjudgment rate in the identification of hard and soft plastic bottles. The stiffness of soft plastic bottles, hard plastic bottles and metal bottles were 0.64 N/mm, 1.36 N/mm and 32.55 N/mm, respectively. The results of slip detection and control show that the proposed prosthetic hand with a slip prevention controller can fast and effectively detect and prevent the slip for different disturbances, which has a certain application prospect. Practical implications Due to the small size, low weight, high integration and modularity, the prosthetic hand is easily applied to upper-limb amputees. Meanwhile, the method of the slip prevention control can be used for upper-limb amputees to complete more tasks stably in daily lives. Originality/value A multi-sensory anthropomorphic prosthetic hand is designed, and a method of stable grasps control based on slip detection by a tactile sensor on the fingertip is proposed. The method combines the stiffness estimation of the object and the real-time slip detection based on DWT with the design of the proportion differentiation robust controller based on a disturbance observer and the force controller to achieve slip prevention and stable grasps. It is verified effectively by the experiments and is easy to be applied to commercial prostheses.

Real-time pixel-wise grasp affordance prediction based on multi-scale context information fusion

Purpose This paper aims to use fully convolutional network (FCN) to predict pixel-wise antipodal grasp affordances for unknown objects and improve the grasp detection performance through multi-scale feature fusion. Design/methodology/approach A modified FCN network is used as the backbone to extract pixel-wise features from the input image, which are further fused with multi-scale context information gathered by a three-level pyramid pooling module to make more robust predictions. Based on the proposed unify feature embedding framework, two head networks are designed to implement different grasp rotation prediction strategies (regression and classification), and their performances are evaluated and compared with a defined point metric. The regression network is further extended to predict the grasp rectangles for comparisons with previous methods and real-world robotic grasping of unknown objects. Findings The ablation study of the pyramid pooling module shows that the multi-scale information fusion significantly improves the model performance. The regression approach outperforms the classification approach based on same feature embedding framework on two data sets. The regression network achieves a state-of-the-art accuracy (up to 98.9%) and speed (4 ms per image) and high success rate (97% for household objects, 94.4% for adversarial objects and 95.3% for objects in clutter) in the unknown object grasping experiment. Originality/value A novel pixel-wise grasp affordance prediction network based on multi-scale feature fusion is proposed to improve the grasp detection performance. Two prediction approaches are formulated and compared based on the proposed framework. The proposed method achieves excellent performances on three benchmark data sets and real-world robotic grasping experiment.

The role of robotics in additive manufacturing: review of the AM processes and introduction of an intelligent system

Purpose Additive manufacturing (AM) is a rapidly evolving manufacturing process, which refers to a set of technologies that add materials layer-by-layer to create functional components. AM technologies have received an enormous attention from both academia and industry, and they are being successfully used in various applications, such as rapid prototyping, tooling, direct manufacturing and repair, among others. AM does not necessarily imply building parts, as it also refers to innovation in materials, system and part designs, novel combination of properties and interplay between systems and materials. The most exciting features of AM are related to the development of radically new systems and materials that can be used in advanced products with the aim of reducing costs, manufacturing difficulties, weight, waste and energy consumption. It is essential to develop an advanced production system that assists the user through the process, from the computer-aided design model to functional components. The challenges faced in the research and development and operational phase of producing those parts include requiring the capacity to simulate and observe the building process and, more importantly, being able to introduce the production changes in a real-time fashion. This paper aims to review the role of robotics in various AM technologies to underline its importance, followed by an introduction of a novel and intelligent system for directed energy deposition (DED) technology. Design/methodology/approach AM presents intrinsic advantages when compared to the conventional processes. Nevertheless, its industrial integration remains as a challenge due to equipment and process complexities. DED technologies are among the most sophisticated concepts that have the potential of transforming the current material processing practices. Findings The objective of this paper is identifying the fundamental features of an intelligent DED platform, capable of handling the science and operational aspects of the advanced AM applications. Consequently, we introduce and discuss a novel robotic AM system, designed for processing metals and alloys such as aluminium alloys, high-strength steels, stainless steels, titanium alloys, magnesium alloys, nickel-based superalloys and other metallic alloys for various applications. A few demonstrators are presented and briefly discussed, to present the usefulness of the introduced system and underlying concept. The main design objective of the presented intelligent robotic AM system is to implement a design-and-produce strategy. This means that the system should allow the user to focus on the knowledge-based tasks, e.g. the tasks of designing the part, material selection, simulating the deposition process and anticipating the metallurgical properties of the final part, as the rest would be handled automatically. Research limitations/implications This paper reviews a few AM technologies, where robotics is a central part of the process, such as vat photopolymerization, material jetting, binder jetting, material extrusion, powder bed fusion, DED and sheet lamination. This paper aims to influence the development of robot-based AM systems for industrial applications such as part production, automotive, medical, aerospace and defence sectors. Originality/value The presented intelligent system is an original development that is designed and built by the co-authors J. Norberto Pires, Amin S. Azar and Trayana Tankova.

Warehouse robot market boosted by Covid pandemic and technological innovations

Purpose This paper aims to provide details of recent commercial and technological developments that are driving robotic warehouse automation. Design/methodology/approach Following a short introduction, this first provides a commercial background and identifies the factors driving the market growth. It then gives examples of robotics companies, products and applications that exploit innovations in artificial intelligence (AI). It then considers future prospects, and finally, brief conclusions are drawn. Findings Amazon’s acquisition of Kiva led to a community of new robot manufacturers and the realisation by major e-commerce companies that robotic automation would be required to maintain competitiveness. The Covid pandemic caused a surge in e-commerce and a critical shortage of labour, which further highlighted the need for automation and boosted robotic deployments. Recent advances in AI have resulted in a rapidly growing community of companies producing AI-powered robots which offer advanced capabilities such as mixed product picking, sorting and kitting. These are being deployed by a growing number of e-commerce and logistics companies and are paving the way towards ever-higher levels of warehouse automation. Full automation will soon become a reality. Originality/value This paper identifies the factors driving the rapidly developing warehouse robot business by considering the companies, products, technology and applications.