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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.

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.

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.

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.

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.

Presents a monitoring and inspecting tool for manufacturing set‐ups. A general software architecture for this type of application, which is distributed, object‐oriented and based on a client‐server model, is briefly outlined and then applied to an industrial robot. The resulting application, called EmailWare, is then introduced and demonstrated using industrial robot manipulators from ABB Robotics. The application is capable of sending e‐mail and SMS messages to selected users when the programmed events occur, and receiving and processing commands from those users. Experience with this application clearly shows its usefulness for the purpose debugging purposes by system developers, but also for monitoring routine tasks by system operators.

Associating robot manipulators with industrial welding operations is common and maybe one of the most successful applications of industrial robots. Nevertheless, it is far from being a solved technological process, mainly because the welding process is not fully understood but also because robots are still at an early satge of development, being difficult to use and program by regular operators. This is also true for Human Machine Interfaces (HMI), which are not intuitive to use and are therefore unsatisfactory. In this paper we discuss these problems and present a system designed with the double objective of serving our R&D efforts on welding applications, but also our need to assist industrial partners working with welding setups. Frequently industrial partners are not happy with available commercial systems, requiring tailored solutions that could be adapted to several robots and robot controllers. The developed system is explained in some detail, and demonstrated using two test cases which reproduce two situations very common in industry: multi‐layer butt welding (used on big structures requiring very strong welds) and multi‐point fillet welding (used for example on structural pieces for the construction industry).

Force/torque sensing is very important for several automatic and industrial robotic applications. Basically, if precise control of the forces that arise from contact between tools and parts is required to successfully complete the automatic task, then a force/torque sensor is needed along with some force/torque control technique. In this paper we focus on force/torque sensing aspects applied to industrial robotic tasks. Concentrating on a particular type of force/torque sensor, we demonstrate how to use them and how to integrate them into force/torque control applications using robots. Finally, an industrial application is presented where force control was fundamental for the success of the task.

This paper aims to introduce the ideas of practical implications of using industrial robots to implement additive/hybrid manufacturing. The process is discussed and briefly demonstrated. This paper also introduces recent developments on human–machine interface for robotic manufacturing cells, namely the ones used for additive/hybrid manufacturing, as well as interoperability methods between the computer-aided design (CAD) data and material modeling systems. It is presented – using a few solutions developed by the authors – as a set of conceptual guidelines discussed throughout the paper and as a way to demonstrate how they can be applied and their practical implications.

The possibility to program the system from CAD information, which is argued to be crucial, is explored, and the methods necessary for connecting the CAD data to material modeling systems are introduced. This paper also discusses in detail the main requirements (also from a system point-of-view) needed for a full implementation of the presented ideas and methods. A few simulations to better characterize the interactions from heat conduction and physical metallurgy were conducted in an effort to better tune the additive manufacturing process. The results demonstrate how the toolpath planning and deposition strategies can be extracted and studied from a CAD model.

The paper fully demonstrates the possibility to use a robotic setup for additive manufacturing applications and shows the first steps of an innovative system designed with that objective.

Using the aimed platform, unsupervised net-shaping of complex components will substitute the cumbersome processes, and it is expected that such a visionary concept brings about a significant reduction in cost, energy consumption, lead time and production waste through the introduction of optimized and interactive processes. This can be considered as a breakthrough in the field of manufacturing and metal processing as the performance is indicated to increase significantly compared to the current instruction-dependent methods.

Mixed reality is expanding in the industrial market and several companies in various fields are adapting this set of technologies for various purposes, such as optimizing processes, improving the programming tasks and promoting the interactivity of their products with the users, or even improving teaching or training. Robotics is another area that can benefit from these recent technologies. In fact, most of the current and futuristic robotic applications, namely, the areas related to advanced manufacturing tasks (e.g. additive-manufacturing, collaborative robotics, etc.), require new technics to actually perceive the result of several actions, including programming tasks, anticipate trajectories, visualize the motion and related information, interface with programmers and users and several other human–machine interfaces. Consequently, this paper aims to explain a new concept of human–machine interfaces aiming to improve the interaction between advanced users and industrial robotic work cells.

The presented concept uses two different applications (apps) developed to explore the advanced features of the Microsoft HoloLens device. The objectives of the project reported in this paper are to optimize robot paths, just by allowing the advanced user to adjust the selected path through the mixed reality environment, and create new paths, just by allowing the advanced user to insert points in the mixed reality environment, correct them as needed, connect them using a certain type of motion, parametrize them (in terms of velocity, motion precision, etc.) and command them to the robot controller.

The solutions demonstrated in this paper show how mixed reality can be used to allow users, with limited programming experience, to fully use the robotics fields. They also show clearly that the integration of the mixed reality technology in the current robot systems will be a turning point in reducing the complexity for end-users.

There are two challenges in the developed applications. The first relates to the robot tool identification, which is very sensitive to lighting conditions or to very complex robot tools. This can result in positioning errors when the software shows the path in the mixed reality scene. The paper presents solutions to overcome this problem. Another unattended challenge is associated with handling the robot singularities when adjusting or creating new paths. Ongoing work is concentrated in creating mechanisms that prevent the end-user to create paths that contain unreachable points or paths that are not feasible because of bad motion parameters.

This paper demonstrates the utilization of mixed reality device to improve the tasks of programming and commanding manufacturing work cells based on industrial robots [see video in (Pires et al., 2018)]. As the presented devices and robot cells are the basis for Industry 4.0 objectives, this demonstration has a vast field of application in the near future, positively influencing the way complex applications, that require much close cooperation between humans and machines, are thought, planned and built. The paper presents two different applications fully ready to use in industrial environments. These applications are scientific experiments designed to demonstrate the principles and technologies of mixed reality applied to industrial robotics, namely, for improving the programming task.

Although the HoloLens device opens outstanding new areas for robot command and programming, it is still expensive and somehow heavy for everyday use. Consequently, this opens an opportunity window to combine these devices with other mobile devices, such as tablets and phones, building applications that take advantage of their combined features.

The paper presents two different applications fully ready to use in industrial environments. These applications are scientific experiments designed to demonstrate the principles and technologies of mixed reality applied to industrial robotics, namely, for improving the programming task. The first application is about path visualization, i.e. enables the user to visualize, in a mixed reality environment, any path preplanned for the robot cell. With this feature, the advanced user can follow the robot path, identify problems, associate any difficulty in the final product with a particular issue in the robot paths, anticipate execution problems with impact on the final product quality, etc. This is particularly important for not only advanced applications, but also for cases where the robot path results from a CAD package (in an offline fashion). The second application consists of a graphical path manipulation procedure that allows the advanced user to create and optimize a robot path. Just by exploring this feature, the end-user can adjust any path obtained from any programming method, using the mixed reality approach to guide (visually) the path manipulation procedure. It can also create a completely new path using a process of graphical insertion of point positions and paths into the mixed reality scene. The ideas and implementations of the paper are original and there is no other example in the literature applied to industrial robot programming.

The purpose of this paper is to report a collection of developments that enable users to program industrial robots using speech, several device interfaces, force control and code generation techniques.

The reported system is explained in detail and a few practical examples are given that demonstrate its usefulness for small to medium‐sized enterprises (SMEs), where robots and humans need to cooperate to achieve a common goal (coworker scenario). The paper also explores the user interface software adapted for use by non‐experts.

The programming‐by‐demonstration (PbD) system presented proved to be very efficient with the task of programming entirely new features to an industrial robotic system. The system uses a speech interface for user command, and a force‐controlled guiding system for teaching the robot the details about the task being programmed. With only a small set of implemented robot instructions it was fairly easy to teach the robot system a new task, generate the robot code and execute it immediately.

Although a particular robot controller was used, the system is in many aspects general, since the options adopted are mainly based on standards. It can obviously be implemented with other robot controllers without significant changes. In fact, most of the features were ported to run with Motoman robots with success.

It is important to stress that the robot program built in this section was obtained without writing a single line of code, but instead just by moving the robot to the desired positions and adding the required robot instructions using speech. Even the upload task of the obtained module to the robot controller is commanded by speech, along with its execution/termination. Consequently, teaching the robotic system a new feature is accessible for any type of user with only minor training.

This type of PbD systems will constitute a major advantage for SMEs, since most of those companies do not have the necessary engineering resources to make changes or add new functionalities to their robotic manufacturing systems. Even at the system integrator level these systems are very useful for avoiding the need for specific knowledge about all the controllers with which they work: complexity is hidden beyond the speech interfaces and portable interface devices, with specific and user‐friendly APIs making the connection between the programmer and the system.

Additive manufacturing (AM) technologies have recently turned into a mainstream production method in many industries. The adoption of new manufacturing scenarios led to the necessity of cross-disciplinary developments by combining several fields such as materials, robotics and computer programming. This paper aims to describe an innovative solution for implementing robotic simulation for AM experiments using a robot cell, which is controlled through a system control application (SCA).

For this purpose, the emulation of the AM tasks was executed by creating a robot working station in RoboDK software, which is responsible for the automatic administration of additive tasks. This is done by interpreting gcode from the Slic3r software environment. Posteriorly, all the SCA and relevant graphical user interface (GUI) were developed in Python to control the AM tasks from the RoboDK software environment. As an extra feature, Slic3r was embedded in the SCA to enable the generation of gcode automatically, without using the original user interface of the software. To sum up, this paper adds a new insight in the field of AM as it demonstrates the possibility of simulating and controlling AM tasks into a robot station.

The purpose of this paper is to contribute to the AM field by introducing and implementing an SCA capable of executing/simulating robotic AM tasks. It also shows how an advanced user can integrate advanced simulation technologies with a real AM system, creating in this way a powerful system for R&D and operational manufacturing tasks. As demonstrated, the creation of the AM environment was only possible by using the RoboDk software that allows the creation of a robot working station and its main operations.

Although the AM simulation was satisfactory, it was necessary to develop an SCA capable of controlling the whole simulation through simple commands instructed by users. As described in this work, the development of SCA was entirely implemented in Python by using official libraries. The solution was presented in the form of an application capable of controlling the AM operation through a server/client socket connection. In summary, a system architecture that is capable of controlling an AM simulation was presented. Moreover, implementation of commands in a simple GUI was shown as a step forward in implementation of modern AM process controls.

This study aims to report the development of a provisional robotic cell for additive manufacturing (AM) of metallic parts. To this end, the paper discusses cross-disciplinary concepts related to the development of the robotic cell and the associated command and control system such as the Computer-Aided Design (CAD) interface, the slicing software and the path planning for the robot manipulator toward printing the selected workpiece. This study also reports the development of a virtual production cell that simulates the AM toolpath generated for the desired workpiece, the adaptation of the simulation environments to enable AM and the development of a user application to setup, command and control the AM processes. If a digital twin setup is efficiently built, with a good correlation between the simulation environment and the real systems, developers may explore this functionality to significantly reduce the development cycle, which can be very long in AM applications where metallurgic properties, part distortion and other properties need to be monitored and controlled.

To generate the robot manipulator path, several simulation programs were considered, resulting in different solutions to program and control the robot of choice [in this study, Kuka and Asea Brown Boveri (ABB) robots were considered]. By integrating the solutions from Slic3r, Inventor, Kuka.Sim, Kuka.Officelite, RobotStudio and Visual Studio software packages, this study aims to develop a functional simulation system capable of producing a given workpiece. For this purpose, a graphical user interface (GUI) was designed to provide the user with a higher level of control over the entire process toward simplifying the programming and implementation events.

The presented solutions are compatible with the simulation environments of specific robot manufacturers, namely, ABB and Kuka, meaning that the authors aim to align the developments with most of the currently realized AM processing cells. In the long-term, the authors aim to build an AM system that implements a produce-from-CAD strategy i.e. that can be commanded directly from the CAD package used to design the part the authors are interested in.

This study attempts to shed light on the industrial AM, a field that is being constantly evolved. Arguably, one of the most important aspects of an AM system is path planning for the AM operation, which must be independent of the robotic system used. This study depicts a generic implementation that can be used with several robot control systems. The paper demonstrates the principle with ABB and Kuka robots, exploiting in detail simulation environments that can be used to create digital twins of the real AM systems. This is very important in actual industrial setups, as a good correlation between the digital twins (simulation environment and real system) will enable developers to explore the AM system in not only a more efficient manner, greatly reducing the development cycle but also as a way to fully develop new solutions without stopping the real setup. In this research, a systematic review of robot systems through simulation environments was presented, aiming to emulate the logic that is, used in the production cell development, disregarding the system brand. The adopted digital twin strategy enables the authors to fully simulate, both operationally and functionality, the real AM system. For this purpose, different solutions were explored using robots from two different manufacturers and related simulation environments, illustrating a generic solution that is not bound to a certain brand.

Using specific programming tools, fully functional virtual production cells were conceived that can receive the instructions for the movements of the robot, using a transmission control protocol/internet protocol. Conversion of the CAD information into the robot path instructions for the robot was the main research question in this study. With the different simulation systems, a program that translates the CAD data into an acceptable format brings the robot closer to the automatic path planning based on CAD data. Both ABB and Kuka systems can access the CAD data, converting it to the correct robot instructions that are executed. Eventually, a functional and intuitive GUI application capable of commanding the simulation for the execution of the AM was implemented. The user can set the desired object and run a completely automatic AM process through the designated GUI. Comparing ABB simulation with the Kuka system, an important distinction can be found, namely, in the exportation of the programs. As the Kuka program runs with add-ons, the solution will not be exported while maintaining its functionality, whereas the ABB program can be integrated with a real controller because it is completely integrated with modules of the virtual controller.

To conclude, with the solutions exploited, this study reports a step forward into the development of a fully functional generic AM cell. The final objective is to implement an AM system that is, independent of any robot manufacturer brand and uses a produce-from-CAD strategy (c.f. digital manufacturing). In other words, the authors presented a system that is fully automatic, can be explored from a CAD package and, consequently, can be used by any CAD designer, without specific knowledge of robotics, materials and AM systems.

The Industry 4.0 initiative – with its ultimate objective of revolutionizing the supply-chain – putted more emphasis on smart and autonomous systems, creating new opportunities to add flexibility and agility to automatic manufacturing systems. These systems are designed to free people from monotonous and repetitive tasks, enabling them to concentrate in knowledge-based jobs. One of these repetitive functions is the order-picking task which consists of collecting parts from storage (warehouse) and distributing them among the ordering stations. An order-picking system can also pick finished parts from working stations to take them to the warehouse. The purpose of this paper is to present a simplified model of a robotic order-picking system, i.e. a mobile manipulator composed by an automated guided vehicle (AGV), a collaborative robot (cobot) and a robotic hand.

Details about its implementation are also presented. The AGV is needed to safely navigate inside the factory infrastructure, namely, between the warehouse and the working stations located in the shop-floor or elsewhere. For that purpose, an ActiveONE AGV, from Active Space Automation, was selected. The collaborative robot manipulator is used to move parts from/into the mobile platform (feeding the working stations and removing parts for the warehouse). A cobot from Kassow Robots was selected (model KR 810), kindly supplied by partner companies Roboplan (Portugal) and Kassow Robotics (Denmark). An Arduino MKR1000 board was also used to interconnect the user interface, the AGV and the collaborative robot. The graphical user interface was developed in C# using the Microsoft Visual Studio 2019 IDE, taking advantage of this experience in this type of language and programming environment.

The resulting prototype was fully demonstrated in the partner company warehouse (Active Space Automation) and constitutes a possible order-picking solution, which is ready to be integrated into advanced solutions for the factories of the future.

A solution to fully automate the order-picking task at an industrial shop-floor was presented and fully demonstrated. The objective was to design a system that could be easy to use, to adapt to different applications and that could be a basic infrastructure for advanced order-picking systems. The system proved to work very well, executing all the features required for an order-picking system working in an Industry 4.0 scenario where humans and machines must act as co-workers. Although all the system design objectives were accomplished, there are still opportunities to improve and add features to the presented solution. In terms of improvements, a different robotic hand will be used in the final setup, depending on the type of objects that are being required to move. The amount of equipment that is located on-board of the AGV can be significantly reduced, freeing space and lowering the weight that the AGV carries. For example, the controlling computer can be substituted by a single-board-computer without any advantage. Also, the cobot should be equipped with a wrist camera to identify objects and landmark. This would allow the cobot to fully identify the position and orientation of the objects to pick and drop. The wrist camera should also use bin-picking software to fully identify the shape of the objects to pick and also their relative position (if they are randomly located in a box, for example). These features are easy to add to the developed mobile manipulator, as there are a few vision systems in the market (some that integrate with the selected cobot) that can be easily integrated in the solution. Finally, this paper reports a development effort that neglected, for practical reasons, all issues related with certification, safety, training, etc. A future follow-up paper, reporting a practical use-case implementation, will properly address those practical and operational issues.