## 1 Introduction  
Offering complex products for highly developed markets poses the challenge of tailoring them to the individual requirements of the customer. Centerpieces in efficiently realizing this are a suitable product model that contains degrees of freedom representing adjustable product properties and options and an information system that is capable of translating customer inputs into a valid product specification [1; 2; 3]. Today's computational design systems offer plenty of possibilities to combine the two. In addition to approaches such as algorithmic and generative design, the traditional parameter and chronology-based computer-aided design (CAD) systems that are still very common in mechanical and plant engineering implement different techniques to model solution spaces instead of a single product variant [4; 5; 6]. A sophisticated approach is knowledge-based CAD configuration. CAD-based configuration systems usually offer additional functionality that is associated with the complete set of geometrical data and justify the implementation effort. Such systems contribute to complexity management, e.g., by automatically checking for manufacturability, reducing the configuration task through the automation of part or sub-assembly design and optimization, and generating assembly instructions or other documentation [7; 8; 9]. Independently from the implementation, user inputs commonly are variables and the selection of options [10; 11]. For complex products like in plant engineering, where the configuration is altered many times during the specification process, identifying and changing the respecting parameters requires high attention and a level of abstraction. From the perspective of user experience design, lowering such interaction costs, i.e., the cognitive and physical load to reach the goal of interaction, is a design rationale [12; 13]. For example, virtual and augmented reality applications reach this goal by graphical and interactive control elements that are attached to the real geometry and allow for immediate feedback [14; 15]. Implementing such control elements in a CAD model poses additional constraints for model creation. There are three reasons for that: First, since the rebuild time of the CAD model is crucial, an efficient model setup and advanced parameter planning with streamlined dependencies between the geometrical elements is a necessity, avoiding loops between part and assembly rebuild. Second, after interaction with the control element, its drop position usually needs to be checked regarding valid limits and possibly translated to a given increment, which results in additional restrictions and a feedback loop. Third, the interaction between the geometric model and the knowledge base of the configuration system requires a reasonable algorithmic abstraction for the functions to be achieved and, if necessary, additional parameters for, e.g., calculation and plausibility checks [16; 17]. From a methodology point of view, many contributing aspects of creating such interactive control elements are covered by literature, e.g., defining levels of detail, working with skeletons, and applying advanced or external knowledge-based control of CAD model parameters [18; 19; 20]. The crucial point is their combination and implementation in a given CAD system. Thus, this article addresses the research question: **How to implement interactive geometric control elements in a CAD assembly, and which generalized design features are necessary, independently from the CAD system? **  
To answer the question, the authors follow a learning-by-building approach based on [PERSON]'s design science research cycles [21]. The knowledge base is presented in the following Section 2, where foundational aspects of parametric and knowledge-based modeling in CAD systems and model planning methods are briefly introduced. Section 3 then introduces the case study, which is an interactive modular piping 3D CAD configuration system.