but they retain the “how” and “why.” In a sense, the CAD approach can be considered a system “posterior,” because before it can be moved to the system it is necessary to know what the principle is like. It can be argued that CAD is geometry or drawing/drawing engineering to distinguish this approach from KBE.
Figure 1.3 KBE.
KBE-supported technology is different. Technology Instead of shifting “what,” engineering experts are trying to move “how” and “why,” encapsulating in the KBE process knowledge and thinking instead of geometry in the CAD framework. Not only does this work by manipulating geometric structures, but programming is needed rather than writing. The “how” and “why” in engineering are in some cases used in textbooks, databases, tip sheets, and several other outlets. Much of the knowledge is held up by engineers, mostly in a manner that is strongly compiled and not specifically suitable for translation to the KBE procedure. This experience should be sufficiently transparent to create a KBE program to be codified into a software application capable of producing all kinds of product specifics, including geometry templates, scores, and data that are not associated with the geometry. Because of its capacity to generate a specification rather than simply text, it is widely referred to as a generative model.
1.5.2 When Can KBE Be Used?
How easy is the use of KBE? It is only necessary to rapidly generate different configurations and variants of a given variable. In certain practical cases, this is not essential, so it may be a wrong expenditure to try to clarify details and program a KBE system. One-off prototypes and designs that need not be optimized or prototype versions for space travel are usually outside the scope of KBE implementation.
It explores the design field through the development of different design variants within the product family and evaluates its performance compared to previously tested versions with the multidisciplinary optimization (MDO) application. KBE will assist in many respects in this case.
It enables stable product parametric models to be generated which make topology changes and the freedom to make adaptation changes usually impossible for those built with a conventional CAD framework. This is important when considering broad variations like those which occur when a yacht manufacturer decides to accept one or more hull settings. It supports the integration into MDO through automation of the generation of necessary disciplinary abstractions of heterogeneous sets of analytical methods (low and high fidelity, in-house and off-shelf). It removes the optimizer from the challenge of managing the spatial integration constraints that generative models should guarantee. This is essential because the user does not need to specify constraints on configuration variables or restrictions to avoid intersection of two elements; or because a certain structural element does not need to remain beyond the same outer mold line; or because, during optimization, two products are expected to have a certain relative position apart.
KBE generative models can be a secret in producing MDO systems that are not multidisciplinary in return for adherence to science and that can handle complex problems reflecting actual industrial circumstances. We discuss the different models of current MDO systems and compare them to advanced KBE implementations in the next section to clarify this claim.
The third set of MDO structure implementation is available to overcome the weaknesses of the two approaches described earlier in this section by introducing generative models into the system. One advantage of this approach is that the exact geometry representations normally used for the use of high faithfulness analysis instruments may serve as a basis for the disciplinary study. It is therefore well adapted to the geometric nuances that are not included in a few general criteria of modern products. This geometry depiction is generated following individual tools of multidisciplinary system analyzers (BB SA) along with others, usually not schematic, product abstractions, and are systematically updated after each optimization loop. These MDO systems can fully resolve multidisciplinary cases without penalizing the degree of faithfulness and can contribute to the early stages of the design phase by addressing substantial changes in the shape and topology. They may also support a more sophisticated modeling method in which complex and accurate geometric models for high fidelity analysis are needed. These functions allow the early use of highly reliable testing approaches to be implemented with novel prototypes that do not have correct or unavailable semi-empirical and predictive technologies. The product modeling scheme which is the key feature of the MDO system is undermined by this approach.
1.5.3 CAD or KBE?
It is a mistake to know whether KBE is greater than CAD or vice versa. One is in the whole sense no bigger than the other, and we argue here that KBE should replace CAD. In certain circumstances, the KBE programming process is more suitable than the interactive application of the CAD platform, given that MDO supports one of the interests of this novel. This chapter is beyond the scope of a general debate about the suitability of one option for the next. The suggestions are as follows:
Where the focus is only on geometry development and manipulation; where considerations such as direct interaction with geometric models are important, graphical rendering and inspection are essential; if uniform, aesthetic, and heuristic design are the guides behind modeling, rather than engineering laws.
When it comes to design purposes, vocabulary is required instead of design results. The programming method of KBE systems offers the best solution in this case. Although CAD systems are committed to better documenting the results of the human design phase, KBE systems are designed to report the design procedure (i.e., the purpose of the design) and not just the results.
• A language is needed to promote automation while preserving continuity. Whenever the generative model is “played.” The same protocol (i.e., the same rules and logic processes are applied) is constantly repeated with different appropriate inputs regardless of which operator and of how many replays. In some engineering design cases, one of which is the optimization of design, an obstacle to automation is placed in the loop (except process supervision).
• A vocabulary offers a competitive advantage when it comes to the ease of interaction with external modeling and simulation applications. Usually, both CAD and KBE systems link (to each other and) through standard data interchange formats including IGES and Move. In times of ad hoc interchange files that are dependent on ASCIIs, the most useful approach to dedicated writers and parser production is full-function language programming. Also, the KBE system can detect and largely simplify these processes where the tool to be connected is required by complicated and knowledge-intensive pre-processing operations to schedule the input.
• Where there is an aesthetic facet of the architecture and details are produced, but at the same time an multidisciplinary research (MDA) and an optimization approach are used in the design and size of a given product, the best possible solution is provided by combined applications of CAD and KBE. In this case, the CAD process geometry would become the KBE application’s feedback. This implementation will support the complex MDA structure and return the material (partially or fully) to the CAD system, where comprehensive work can take place more immersive.
At the end of the day, the heuristic and non-respecting, geometric or non-repeatable, one-off, and repetitive aspects of the design phase coexist and are interlinked: Both CAD and KBE can contribute to this step, which must be the focus of both the creators of CADs and KBE’s smooth integration.
1.6 Guided Random Search and Network Techniques
There are some methods designed to find suitable designs using techniques that avoid the use of the pursuit of gradients or almost gradients. A system that either uses random variations in design variables or avoids direct variations in design variables by the use of learning networks supplements the use of directional searches. We have selected the genetic algorithm (GA) as a representative in the first category, RAT (GRS), and we have