Production systems have to meet quality requirements despite increasing product individuality, varying batch sizes and a scarcity of resources. The transfer of experience-based knowledge in a flexible and self-optimizing production and process planning offers the potential to meet these challenges. Biological systems solve conceptually similar challenges pertaining to the transfer of knowledge, flexibility of individual reactions and adaptation over time. Thus, in the context of digital transformation, mechanisms derived from biology are interpreted and applied to the knowledge domain of production technology. To be able to exploit the potential of bio-inspired production systems, genetic and intelligent properties of technical components and machines were identified and brought together under the concept of “Gentelligence”. Expanding upon this concept with the new idea of process-DNA and biologically inspired optimization algorithms facilitates a more flexible, learning and self-optimizing production, which is shown in three different applications. By using the new concept of gentelligent process planning it is possible to determine machine-specific process parameters in turning processes in order to ensure appropriate roughness within the requirements. Furthermore, the combination of the concept with a material removal simulation allows the determination of the resulting process force in tool grinding for subsequent unknown workpiece geometries. As a result of using the process-DNA, a workpiece-independent knowledge transfer and thus process adaptation for shape error compensation becomes possible. Gentelligent production scheduling enables a process-parallel, holistically optimized machine allocation, and as a result, a significantly reduced lead time.

尽管产品个性化，批次大小不同和资源短缺，但生产系统仍必须满足质量要求。在灵活且自我优化的生产和过程计划中基于经验的知识的转移提供了应对这些挑战的潜力。生物系统解决了概念上类似的挑战，涉及知识的转移，个体反应的灵活性和随着时间的推移的适应性。因此，在数字转换的背景下，从生物学中衍生的机制被解释并应用于生产技术的知识领域。为了能够利用生物启发性生产系统的潜力，技术组件和机器的遗传和智能属性已被识别，并以“绅士化”的概念归纳在一起。通过过程DNA和受生物学启发的优化算法的新思想扩展了这一概念，从而实现了更加灵活，学习和自我优化的生产，这在三种不同的应用中得到了展示。通过使用新的高级工艺计划概念，可以确定车削过程中特定于机器的工艺参数，以确保在要求范围内具有适当的粗糙度。此外，将概念与材料去除模拟相结合，可以确定随后的未知工件几何形状在刀具磨削中所产生的过程力。通过使用过程DNA，可以进行与工件无关的知识转移，从而可以进行形状误差补偿的过程自适应。精巧的生产计划可实现流程并行，