Högskolan i Skövde

In recent years Mixed Reality technology has been widely used to enhance operators in manual assembly operations. This paper introduces a Mixed Reality environment for assembly operations and describes how the process can be supported by virtual sensors. The structure of the environment allows seamless adaption from a fully virtual training scenario, only using virtual assets, to a full production scenario supporting operators in assembling physical products in actual production. The training system which has been developed together with the company Skandia Elevator in Sweden enables the operators to train with much less disturbance to the real production line compared to training using the actual production equipment. In fact, the training can be done only using virtual assets.

This paper presents a Mixed reality (MR) environment to support industrial cobots programming for welding purposes. Several intuitive menus allow a user to program the path, which can be configured and simulated in the virtual environment with reachability checking. A guide can be activated to show recommended steps and allows validation of the created program on the virtual robot to aid the learning process. The MR application is integrated with a robot programming platform and a robot controller, that allows the user to test paths on a real robot. Moreover, results from spatial accuracy and user experience evaluations are presented.

This paper presents a Mixed Reality (MR) approach to extend the Safe Move tool from ABB Robot Studio (RS) to view and intuitively edit the safety configuration, allowing the import of existing configurations and the export of modified ones to RS for certification by the expert. The added virtual sensor can detect the user's position allowing collision detection and avoidance. The robot's motion is not only adjusted to the safety zone in which the robot is but also to its relative position to the operator by monitoring in real-time positions of the operator’s hands as well as head and triggering appropriate action in the robot.

In a Cyber-physical system, the information flow from the cyber part to the physical part plays a crucial role. This paper presents the work of development and initial testing of an augmented reality approach to provide a user interface for operators that could be a part of a robotic production system. The solution is distributed and includes a communication hub that allows the exchange of data and information between multiple clients e.g. robot controllers, an optimization platform, and visualization devices. The main contributions of the presented work are visualization of optimization results and visualization of information obtained from the robot controller and the integrated communication framework. The paper also presents challenges faced during the development work and opportunities related to the presented approach. The implemented interface uses HoloLens 2 mixed reality device to visualize in real-time information obtained from a robot controller as well as from simulation. Information regarding the placement of work objects and targets or currently executed lines of code can be useful for robotic cell programmers and commissioning teams to validate robot programs and to select more optimal solutions toward sustainable manufacturing. The operator can simulate the execution of the robot program and visualize it by overlying the robot cell with the 3D model of the simulated robot. Moreover, visualization of future robot motion could support human-robot collaboration. Furthermore, the interface allows providing the user with details from multi-objective optimization performed on a digital twin of the robotic cell with the aim to reduce cycle time and energy consumption. It allows visualizing selected scenarios to support decision-making by allowing comparison of proposed solutions and the initial one. The visualization includes cell layout, robot path, cycle time, robot energy consumption. The presented approach is demonstrated in industry-inspired cases and with the use of an industrial ABB robot.

This paper considers the issue of rapid automated decision making in changing factory environments, situations including human-robot collaboration, mass customisation and the need to rapidly adapt activities to new conditions. The approach taken is to adapt the Monte Carlo Tree Search (MCTS) algorithm to provide online choices for the possible actions of machines and workers, interleaving them dynamically in response to the changing conditions of the production process. This paper describes how the MCTS algorithm has been adapted for use in production environments and then the proposed method is illustrated by two examples of the system in use, one simulated and one in a physical test cell.

Maintenance of assembly and manufacturing equipment is crucial to ensure productivity, product quality, on-time delivery, and a safe working environment. Predictive maintenance is an approach that utilises the condition monitoring data to predict the future machine conditions and makes decisions upon this prediction. The main aim of the present research is to achieve an improvement in predictive condition-based maintenance decision making through a cloud-based approach with usage of wide information content. For the improvement, it is crucial to identify and track not only condition related data but also context data. Context data allows better utilisation of condition monitoring data as well as analysis based on a machine population. The objective of this paper is to outline the first steps of a framework and methodology to handle and process maintenance, production, and factory related data from the first lifecycle phase to the operation and maintenance phase. Initial case study aims to validate the work in the context of real industrial applications.

The presented work is toward population-based predictive maintenance of manufacturing equipment with consideration of the automaticselection of signals and processing methods. This paper describes an analysis performed on double ball-bar measurement from a population ofsimilar machine tools. The analysis is performed after aggregation of information from Computerised Maintenance Management System,Supervisory Control and Data Acquisition, NC-code and Condition Monitoring from a time span of 4 years. Economic evaluation is performedwith use of Monte Carlo simulation based on data from real manufacturing setup.

The cloud technology provides sustainable solutions to the modern industrial robotic cells. Within the context, the objective of this research is to minimise the energy consumption of robots during assembly in a cloud environment. Given a robot path and based on the inverse kinematics and dynamics of the robot from the cloud, a set of feasible configurations of the robot can be derived, followed by calculating the desirable forces and torques on the joints and links of the robot. Energy consumption is then calculated for each feasible configuration along the path. The ones with the lowest energy consumption are chosen. Since the energy-efficient robot configurations lead to reduced overall energy consumption, this approach becomes instrumental and can be applied to energy-efficient robotic assembly. This cloud-based energy-efficient approach for robotic applications can largely enhance the current practice as demonstrated by the results of three case studies, leading towards sustainable manufacturing.

In sustainable manufacturing, the proper maintenance is crucial to minimise the negative environmental impact. In the context of Cloud Manufacturing, Internet of Things and Big Data, amount of available information is not an issue, the problem is to obtain the relevant information and process them in a useful way. In this paper a maintenance decision support system is presented that utilises information from multiple sources and of a different kind. The key elements of the proposed approach are processing and machine learning method evaluation and selection, as well as estimation of long-term key performance indicators (KPIs) such as a ratio of unplanned breakdowns or a cost of maintenance approach. Presented framework is applied to machine tool linear axes. Statistical models of failures and Condition Based Maintenance (CBM) are built based on data from a population of 29 similar machines from the period of over 4 years and with use of proposed processing approach. Those models are used in simulation to estimate the long-term effect on selected KPIs for different strategies. Simple CBM approach allows, in the considered case, a cost reduction of 40% with the number of breakdowns reduced 6 times in respect to an optimal time-based approach.

The research presented in this thesis is focused on improving industrial maintenance by using better decision support that is based on a wider range of input information. The core objective is to research how to integrate information from a population of similar monitored objects. The data to be aggregated comes from multiple disparate sources including double ball-bar circularity tests, the maintenance management system, and the machine tool’s controller. Various data processing and machine learning methods are applied and evaluated. Finally, an economic evaluation of the proposed approach is presented. The work performed is presented in five appended papers.

Paper I presents an investigation of cloud-based predictive maintenance concepts and their potential benefits and challenges.

Paper II presents the results of an investigation of available and potentially useful data from the perspective of predictive analytics with a focus on the linear axes of machine tools.

Paper III proposes a semantic framework for predictive maintenance, and investigates means of acquiring relevant information from different sources (i.e., ontology-based data retrieval).

Paper IV presents a method for data integration. The method is applied to data obtained from a real manufacturing setup. Simulation-based evaluation is used to compare results with a traditional time-based approach.

Paper V presents the results from additional simulation-based experiments based on the method from Paper IV. The aim is to improve the method and provide additional information that can support maintenance decision-making (e.g., determining the optimal interval for inspections).

The method developed in this thesis is applied to a population of linear axes from a set of similar multipurpose machine tools. The linear axes of machine tools are very important, as their performance directly affects machining quality. Measurements from circularity tests performed using a double ball-bar measuring device are combined with event and context information to build statistical failure and classification models. Based on those models, a decision-making process is proposed and evaluated. In the analysed case, the proposed approach leads to direct maintenance cost reduction of around 40 % compared to a time-based approach.