Knowledge-based engineering (KBE) is a discipline with roots in computer-aided design (CAD) and knowledge-based systems but has several definitions and roles depending upon the context. An early role was support tool for a design engineer generally within the context of product design. Success of early KBE prototypes was remarkable (see History); eventually this led to KBE being considered as the basis for generative design with many expectations for hands-off performance where there would be limited human involvement in the design process.
||This article provides insufficient context for those unfamiliar with the subject. (April 2013)|
KBE can be defined as engineering on the basis of digital knowledge models. Such knowledge models are the result of knowledge modeling that uses knowledge representation techniques to create the computer interpretable models. The knowledge models can be imported in and/or stored in specific engineering applications that enable engineers to specify requirements or create designs on the basis of the knowledge in such models. There are various methods available for the development of knowledge models, most of them are system dependent. An example of a system-independent language for the development machine-readable ontology databases, including support for basic engineering knowledge, is called Gellish English. An example of a CAD-specific system that can store knowledge and use it for design is the CATIA program through its KnowledgeWare module. An example of a CAD-independent, language-based KBE system with full compiler and support for runtime application deployment is Genworks GDL from Genworks International.
KBE can have a wide scope that covers the full range of activities related to Product Lifecycle Management and Multidisciplinary design optimization. KBE's scope would include design, analysis (computer-aided engineering – CAE), manufacturing, and support. In this inclusive role, KBE has to cover a large multi-disciplinary role related to many computer aided technologies (CAx).
KBE also has more general overtones. One of its roles is to bridge knowledge management and design automation. Knowledge processing is a recent advance in computing. It has played a successful role in engineering and is now undergoing modifications (to be explained). An example of KBE’s role is generative mechanical design. There are others. KBE can be thought of as an advanced form of computer applications (in some forms with an extreme end-user computing flavor) that support PLM and CAx.
There are similar techniques, such as electronic design automation. AAAI provides a long list of engineering applications. some of which are within the KBE umbrella. At some point, the concept of KBE might split into several sub-categories as MCAD and ECAD are just two of many possible types of design automation.
KBE technologies suffered a downslide during AI Winter. While KBE had sufficient success stories that sustained it long enough into the 1990s, very high expectations and the inability to meet them with KBE resulted in it being considered obsolete in a way similar to LISP AI designs.
KBE and product lifecycle management 
The scope of PLM involves all the steps that exist within any industry that produces goods. KBE at this level will deal with product issues of a more generic nature than it will with CAx. Some might call this level 'assembly' in orientation. However, it's much more than that as PLM covers both the technical and the business side of a product.
KBE then needs to support the decision processes involved with configuration, trades, control, management, and a number of other areas, such as optimization.
KBE and CAX 
CAx crosses many disciplinary bounds and provides a sound basis for PLM. In a sense, CAx is a form of applied science that uses most of the disciplines of engineering and their associated fields. Materials science comes to mind.
KBE's support of CAx may have some similarities with its support of PLM but, in a sense, the differences are going to be larger.
The KBE flavor at the CAx level may assume a strong behavioral flavor. Given the underlying object oriented focus, there is a natural use of entities possessing complicated attributes and fulfilling non-trivial roles. One vendor's approach provides a means via workbenches to embed attributes and methods within sub-parts (object) or within a joining of sub-parts into a part.
As an aggregate, the individual actions, that are event driven, can be fairly involved. This fact identifies one major problem, namely control of what is essentially a non-deterministic mixture. This characteristic of the decision problem will get more attention as the KBE systems subsume more levels and encompasses a broader scope of PLM.
KBE and knowledge management 
||This section may stray from the topic of the article. (October 2012)|
KBE is related to knowledge management which has many levels itself. Some approaches to knowledge are reductionistic, as well they ought to be given the pragmatic focus of knowledge modeling. However, due to KBE dealing with aggregates that can be quite complicated both in structure and in behavior, some holistic notions (note link to Complex systems) might be apropos.
Also, given all the layers of KBE and given the fact that one part of an associated space is heavily mathematical (namely, manifold in nature), KBE is extremely interesting from the knowledge viewpoint (or one would hope).
All one has to do is note that the KBE process's goal is to produce results in the 'real world' via artifacts and to do so using techniques that are highly computational. That, in essence, is the epitome of applied science/engineering, and it could never be non-interesting.
KBE methodology 
The development of KBE applications concerns the requirements to identify, capture, structure, formalize and finally implement knowledge. Many different so-called KBE platforms support only the implementation step which is not always the main bottleneck in the KBE development process. In order to limit the risk associated with the development and maintenance of KBE application there is a need to rely on an appropriate methodology for managing the knowledge and maintaining it up to date. As example of such KBE methodology the EU project MOKA "Methodology and tools Oriented to Knowledge based Applications" propose solutions which focus on the structuration and formalization steps as well as links to the implementation.
An alternative to MOKA is to use a general methodology for developing knowledge bases for expert systems and for intranet pages. Such a methodology is described in "Knowledge Acquisition in Practice: A Step-by-step Guide" by Nick Milton (click here for more details).
Languages for KBE 
Some questions can be asked in regard to KBE implementation: can we represent knowledge in a vendor-neutral format? can the knowledge in our designs be retained for decades, long after a vendor system (such as CATIA) has disappeared?
These questions are addressed in the 2007 NASA-ESA Workshop on Product Data Exchange presentation A Language for Engineering Design by Walter Wilson of Lockheed Martin.
Mr. Wilson advocates using a type of programming language to define design data—operations, parameters, formulas, etc. -- instead of a proprietary file format (such as Dassault's CATIA). One's data would no longer be tied to a specific CAD system. Unlike STEP, which inevitably lags commercial CAD systems in the features it supports, programmability would allow the definition of new design features.
A logic programming language is proposed as the basis for the engineering design language because of its simplicity and extensibility. The geometric engine for the language features would be open source to give engineers control over approximation algorithms and to better guarantee long-term accessibility of the data.
Meanwhile, Genworks GDL, a commercial product whose core is based on the AGPL-licensed Gendl Project, addresses the issue of application longevity by providing a high-level declarative language kernel which is a superset of a standard dialect of the Lisp programming language (ANSI Common Lisp, or CL).
The Gendl kernel follows a concise, pragmatic language specification representing a proposed de facto neutral format for representing KBE-style knowledge. It consists of the same Smalltalk-inspired declarative object-oriented (and object-centric) message-passing format which been a common thread among classical KBE systems for more than two decades. While CL is a multi-paradigm language (supporting procedural, object-oriented, and functional programming), the core features of KBE tend to share several aspects with Functional programming languages "under the hood" (e.g. lazy evaluation, immutable data). However there is a consensus that pure Functional programming is too esoteric for typical engineers to practice, so one of the purposes of a declarative KBE language such as Genworks GDL is to provide an "engineer-friendly" object-centric front-end on what is essentially a Functional language programming environment.
Because Genworks GDL applications are written as a strict superset of a standard Lisp dialect, only the high-level declarative surface syntax is GDL-specific. The bulk of application code is purely compliant with the underlying language standard. And because of Lisp's inherent (and unique) support for code transformation macros, even this surface syntax is subject to straightforward automated conversion among other variations of the de facto standard. It is reasonable to expect that implementations following this approach will eventually converge on a true vendor-neutral Standard KBE language specification.
The Pacelab Suite addresses the problem that functional programming languages are still not widely accepted by potential KBE users. Engineers are usually trained in procedural and object-oriented programming languages; in quite a few software environments (Excel, MATLAB and FORTRAN) used by engineers the procedural programming style clearly dominates. Therefore the Pacelab Suite rebases the inherent technological advantages offered by a development and runtime environment like Common Lisp, on a new technological paradigm (.NET Framework) equally supporting procedural, object-oriented and functional languages.
KBE in Academia 
- Knowledge-based engineering at the Norwegian University of Science and Technology (NTNU)
- Knowledge Based Engineering department at the Faculty of Aerospace Engineering of the Delft University of Technology
- See Webliography for AI in Design hosted by Worcester Polytechnic Institute and the NSF Report "Research Opportunities in Engineering Design."
The following KBE development packages are commercially available:
For CAD 
- CADECWorks Solidworks Certified Gold Partner by Mark Design Solutions Pvt Ltd India Mark Design Solutions
- CADECEdge KBE tool for SolidEdge by Mark Design Solutions Pvt Ltd India Mark Design Solutions
- Adaptive Modeling Language from TechnoSoft Inc.
- DriveWorks A SolidWorks Certified Gold Partner 
- The Gendl Project
- Genworks GDL from Genworks International
- Kadviser from NIMTOTH previously edited by Kade-Tech
- KBEWorks by VisionKBE
- Knowledge Fusion from Siemens PLM Software
- Rulestream from Siemens PLM Software
- Knowledgeware from Dassault Systemes
- ICAD from Dassault Systemes (no longer available)
- Pro/ENGINEER Expert Framework from Parametric Technology Corporation
- SmartAssembly for Pro/ENGINEER from Sigmaxim Inc
- TactonWorks Interactive design automation inside SolidWorks 
- YVE - Your Variant Engineer from tecneos software-engineering
- KBMax Product Configurator Software
- Genus Designer by Genus Software, Inc.
For General-purpose development of Web-deployed applications 
For analysis, design and engineering processes 
- Adaptive Modeling Language from TechnoSoft Inc.
- Enventive by Enventive Engineering, Inc.
- the Gendl Project
- Genworks GDL from Genworks International
- Pacelab Suite by PACE Aerospace Engineering and Information Technology GmbH
- PCPACK by Tacit Connexions
- Quaestor by Maritime Research Institute Netherlands
KBE futures, KBE theory 
|This section lacks a single coherent topic. (October 2012)|
KBE, as a particular example of KS, is a multi-disciplinary framework that has more than practical considerations. Not only will KBE require successful handling of issues of the computational (Ontology, Artificial Intelligence, Entscheidungsproblem, Interactive computation, Category Theory, ...) and logic (non-monotonic issues related to the qualification, frame, and ramification problems)), it will touch upon all sciences that deal with matter, its manipulations, and the related decisions. In a sense, PLM allows us to have the world as a large laboratory for experimental co-evolution of our knowledge and our artificial co-horts. As noted in the ACM Communications, "Computers will grow to become scientists in their own right, with intuitions and computational variants of fascination and curiosity."  What better framework is there to explore the "increasingly complicated mappings between the human world and the computational"?
In terms of methodology and their associated means, KBE offers support via several paradigms. These range from the home-grown all the way to strategically defined and integrated tools that cover both breadth and depth. A continuing theme will be resolving the contextual definitions for KBE into a coherent discipline (or at least attempting this) and keeping a handle on managing the necessary quantitative comparisons. One issue of importance considers what limits there may be to the computational; this study requires a multi-disciplinary focus and an understanding of the quasi-empirical. Given the knowledge focus of KBE, another issue involves what limits there might be to a computational basis for knowledge and whether these are overcome with the more advanced types of human-machine interface.
Finally, it is important not to treat the KBE technology in isolation, but focus more on its role in the overall Product Development Process (PDP). This standpoint applies to the development, use and deployment of KBE applications. During development, it is important to streamline the process from knowledge capture towards software implementation. To this end, close-coupling between Knowledge Management and KBE is desired. Transitions from data and information inside a Knowledge Base towards software code is of particular relevance. The best results can be achieved by using Model-Driven Software Engineering principles, which includes automatic code generation and round-tripping. The use of KBE during a PDP not only requires the ability to easily set-up (existing) KBE applications from a knowledge level, but also the ability to store back the results after execution of the tool. In order to use KBE on a strategical level as decision-making and planning support mechanism, it is important to relate results back to the system engineering domain (requirements, functions, options, embodiment). From deployment perspective, a better integration with other IT tools should be realized. Couplings between KBE applications, Knowledge Bases and Simulation Workflow Management software are of particular importance. The iProd project tries to take KBE to the next level by addressing these aspects. The iProd framework uses KBE technology as a reasoning mechanism to infer new product knowledge and, as a means to automate virtual execution (CAE simulation) and as MDO-enabler. On an IT level, it prototypes KB-KBE couplings (code generation, round-tripping, results storage and automatic workflow generation) and SWFM-KBE integration (on the basis of the software-as-a-service paradigm).
See also 
- Knowledge-based systems
- Functional programming
- Multidisciplinary design optimization
- Model (abstract)
- Decision problem - KBE is mainly a collection of decision points. Several aspects of KBE approach what might be called a 'decidable' bounds though it's hard to get this fact discussed (hence the node here to foster the discussion).
- Differential geometry
- Faculty of Aerospace Engineering, Delft University of Technology
- COE Newsnet, 06/05, What distinguishes KBE from automation?
- COE Newsnet, 04/05 Generative Mechanical Design: An Asset for Small & Medium-Sized Businesses
- AAAI Engineering, subtopic of Applications
- COE Newsnet 10/05 Knowledge Based Engineering (KBE: Update)
- KBE services for PLM
- MOKA project
- ACM Communications, 11/09 Deep Data Dives Discover Natural Laws
- iProd project (integrated management of product heterogeneous data)
- Alcyon Engineering: Introduction to Knowledge Based Engineering
- A KBE System for the Design of Wind Tunnel Models Using Reusable Knowledge Components
- ASME celebrates 125th Anniversary
- KE-works knowledge engineering - a company introducing KBE applications to industry - KBE explanatory video
- Keys to Success with Knowledge-Based Techniques - SAE Paper Number 2008-01-2262
- Knowledge Based Engineering across Product Realization - A whitepaper presented on KBE in PLM domain.
- Knowledge Technologies - a free e-book by Nick Milton that has a chapter describing KBE (Chapter 3, co-authored with G. La Rocca from TU Delft)
- Truth Engineering