• assembly
• automation
• cobots
• fast development
• grasping
• industrial grippers
• industry 4.0

Within the fourth industrial revolution the way products are manufactured is changing dramatically. The digitalization of businesses and producing companies, the inter-connection of their machines through embedded system and the internet of things (IoT), the rise of collaborative robots (cobots), as well as the use of autonomous workstations and matrix production are disrupting the conventional manufacturing model. The demand for individualized and customized products is continuously increasing. Consequently, order numbers are surging while batch sizes diminish, in extremes to a decentralized 'batch size one' production. The demand for a high level of variability in production and manufacturing through Mass Customisation is inevitable. Mass Customisation in turn requires manufacturing systems which are more flexible and adaptable. Within manufacturing processes physical parts, components and final products have to be handled, sorted, positioned, oriented, separated, and assembled. As these tasks and processes are increasingly automated the object interaction happens through robotic end-effectors, mainly grippers. With continuously shrinking batch sizes and increasing order numbers the handling of different object variants becomes a key parameter for the automatization of assembly processes in Industry 4.0. Therefore, the gripper is the element of the production system that has to be regularly redesigned. Within this scope, this PhD thesis presents an iterative development process for the fast and schematic development of new grippers from scratch. The process allows to validate ideas for new grasping systems in the earliest stage possible and to rapidly transform the proven ideas into sophisticated grasping systems. It consists of the three consecutive steps Build, Test, and Learn. Each iteration ends with the decision to either pivot or persevere and thereby allows to eliminate infeasible solutions and test as many ideas as possible. The methodology is embedded into a guiding framework that permits to assess the technological maturity of a device while it is evolving within the development process. Additionally, to guide and monitor the integration of new technologies within an existing industrial environment, as well as the transformation of the environment itself towards Industry 4.0, a two-dimensional transformation matrix is presented. The methodology has not only been demonstrated in various case studies but also been used for the development of several grasping system that are eventually employed in industrial applications. The developed devices are mainly mechanically based shape-adaptive grippers of different scale and scope. Their shape-adaptability allows them to handle objects of different sizes, shapes, weights, and materials. Employed in production processes these devices are able to deal with a great number of product variants and thereby reduce machine changeover and set-up times as well as push the entire manufacturing system towards higher adaptability and flexibility capabilities. Different scopes include but are not limited to the development and application of novel grasping principles, complex insertion into limited spaces, the increase of grasp robustness and precision to compensate misalignments with extremely high repeatability in cobotic assembly cells, and the handling of sensitive and delicate objects across many different industries. In all activities design, simulation and validation through integration played a central role as the three major pillars of this work while striving towards the development of functional groups in a modular architecture through employing interdisciplinary competences. Among others, cardboard prototyping, additive manufacturing, laser cutting, Computer Aided Design modelling, and Finite Element Method simulations are commonly used techniques and tools.
Besides the developed methodology, the results of this thesis can be structured into two categories: On the one hand, they are new automated solutions for operations that prior have been done manually and can now be done more efficiently while releasing operators from the physically demanding and dangerous parts of their work. On the other hand, they are solutions that significantly improve existing inefficient automation systems, reducing failures and subsequent machine down time by up to ten times.