Additive manufacturing technologies such as laser material deposition (LMD) enable manufacturers to economically produce complex and individualized products. However, improved productivity and more economic use of LMD are necessary to benefit from these advantages in a wider range of applications. Through the use of industrial robots in LMD applications, large workspaces and geometric flexibility can be achieved at low cost. Possible effects of reduced path accuracies compared to conventional machines for LMD are not currently quantified. Initial studies suggest effects of path deviations on the component geometry. In this paper, an initial approach to investigate the influence of robot path deviations on the LMD component geometry is introduced. A novel approach toward correlation between path deviations of the robot and resulting surface waviness of the component is presented. The correlation is investigated on two different industrial robots with a powder-based LMD process. Tool center point (TCP) paths of the industrial robots are measured by means of a laser tracker. Robot TCP paths and resulting specimen surface topography are geometrically correlated and compared. The magnitude of the correlation is quantified by the calculation of the Pearson coefficient and a linear approximation of the correlation is made. In addition, the resulting correlation is checked by model calculations regarding the weld path formation as a function of the real tool paths with the aim of quantifying to what extent the waviness of the tool path is reflected in the waviness of the weld track.
Today’s industrial world is characterized by ever-shortening product development cycles and increasing degrees of product individualization which demand tools and enablers for accelerated prototyping. In addition, the existing uncertainty in the product development cycle should be reduced by involving stakeholders as early as possible. However, should an engineering change request (ECR) be necessary in the product development cycle, a fast iteration step into production is inevitable. The methodological description of such an ECR in the product development cycle is described in the previous chapter. Together with researchers from the Internet of Production (IoP), information from the product development process will be transferred to the digital shadow established in the IoP. The digital shadow collects information from all areas of the product lifecycle and provides it to the appropriate departments, adapted to the corresponding task. To tackle this challenge, a new type of product development process, the method of agile product development, is applied. Within the Enablers and Tools project, the development of various advanced manufacturing technologies (AMTs) for agile product development are at the forefront of the work. The enablers and tools are further developed with the principles of agile product development. They also serve to map the requirements for rapidly available and specific prototypes which are used to answer specific questions that arise during the product development cycle. To answer these questions, the concept of the Minimum Viable Product (MVP), an approach to reduce development time and increase customer satisfaction, is introduced and applied to all development tasks.
The Internet of Production (IoP) promises to be the answer to major challenges facing the Industrial Internet of Things (IIoT) and Industry 4.0. The lack of inter-company communication channels and standards, the need for heightened safety in Human Robot Collaboration (HRC) scenarios, and the opacity of data-driven decision support systems are only a few of the challenges we tackle in this chapter. We outline the communication and data exchange within the World Wide Lab (WWL) and autonomous agents that query the WWL which is built on the Digital Shadows (DS). We categorize our approaches intomachine level, process level, and overarching principles. This chapter surveys the interdisciplinary work done in each category, presents different applications of the different approaches, and offers actionable items and guidelines for future work.The machine level handles the robots and machines used for production and their interactions with the human workers. It covers low-level robot control and optimization through gray-box models, task-specific motion planning, and optimization through reinforcement learning. In this level, we also examine quality assurance through nonintrusive real-time quality monitoring, defect recognition, and quality prediction. Work on this level also handles confidence, verification, and validation of re-configurable processes and reactive, modular, transparent process models. The process level handles the product life cycle, interoperability, and analysis and optimization of production processes, which is overall attained by analyzing process data and event logs to detect and eliminate bottlenecks and learn new process models. Moreover, this level presents a communication channel between human workers and processes by extracting and formalizing human knowledge into ontology and providing a decision support by reasoning over this information. Overarching principles present a toolbox of omnipresent approaches for data collection, analysis, augmentation, and management, as well as the visualization and explanation of black-box models.
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