engineering

Knowledge-based engineering

Knowledge-based engineering is a engineering topic covered in the lgStudy science library. This page brings together a partial reference excerpt, illustrations, worked examples, real-world applications and a short study plan, so you can understand Knowledge-based engineering rather than just read about it. In short: Knowledge-based engineering (KBE) is the application of knowledge-based systems technology to the domain of design of manufacturing systems. The design process is inherently a knowledge-intensive activity, so a great deal of the emphasis for KBE is on the use of knowledge-based technology to support computer-aided design (CAD) however knowledge-based techniques (e.g. knowledge management) can be applied to the entir…

Key takeaways

Knowledge-based engineering belongs to engineering; place it in that map before memorising details.
Learn the definition first, then one example that makes the definition concrete.
Connect Knowledge-based engineering to a quantity you can measure, compute or draw — that is where exam questions come from.
Reproduce the core statement of Knowledge-based engineering from memory before moving on to harder problems.

Reference excerpt

Knowledge-based engineering (KBE) is the application of knowledge-based systems technology to the domain of design of manufacturing systems. The design process is inherently a knowledge-intensive activity, so a great deal of the emphasis for KBE is on the use of knowledge-based technology to support computer-aided design (CAD) however knowledge-based techniques (e.g. knowledge management) can be applied to the entire product lifecycle. The CAD domain has always been an early adopter of software-engineering techniques used in knowledge-based systems, such as object-orientation and rules. Knowledge-based engineering integrates these technologies with CAD and other traditional engineering software tools. Benefits of KBE include improved collaboration of the design team due to knowledge management, improved re-use of design artifacts, and automation of major parts of the product lifecycle.

Overview KBE is essentially engineering on the basis of knowledge models. A knowledge model uses knowledge representation to represent the artifacts of the design process (as well as the process itself) rather than or in addition to conventional programming and database techniques. The advantages to using knowledge representation to model industrial engineering tasks and artifacts are:

Improved integration. In traditional CAD and industrial systems each application often has its own slightly different model. Having a standardized knowledge model makes integration easier across different systems and applications. More re-use. A knowledge model facilitates storing and tagging design artifacts so that they can easily be found again and re-used. Also, knowledge models are themselves more re-usable by virtue of using formalism such as IS-A relations (classes and subclasses in the object-oriented paradigm). With subclassing it can be very easy to create new types of artifacts and processes by starting with an existing class and adding a new subclass that inherits all the default properties and behaviors of its parents and then can be adapted as needed. Better maintenance. Class hierarchies not only facilitate re-use they also facilitate maintenance of systems. By having one definition of a class that is shared by multiple systems, issues of change control and consistency are greatly simplified. More automation. Expert system rules can capture and automate decision making that is left to human experts with most conventional systems. 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 includes 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). There are two primary ways that KBE can be implemented:

Build knowledge models from the ground up using knowledge-based technology Layer knowledge-based technology on top of existing CAD, simulation, and other engineering applications An early example of the first approach was the Simkit tool developed by Intellicorp in the 1980s. Simkit was developed on top of Intellicorp's Knowledge Engineering Environment (KEE). KEE was a very powerful knowledge-based systems development environment. KEE started on Lisp and added frames, objects, and rules, as well as powerful additional tools, such as hypothetical reasoning and truth maintenance. Simkit added stochastic simulation capabilities to the KEE environment. These capabilities included an event model, random distribution generators, simulation visualization, and more. The Simkit tool was an early example of KBE. It could define a simulation in terms of class models and rules and then run the simulation as a conventional simulation would. Along the way, the simulation could continue to invoke rules, demons, and object methods, providing the potential for much richer simulation as well as analysis than conventional simulation tools. One of the issues that Simkit faced was a common issue for most early KBE systems developed with this method: The Lisp knowledge-based environments provide very powerful knowledge representation and reasoning capabilities; however, they did so at the cost of massive requirements for memory and processing that stretched the limits of the computers of the time. Simkit could run simulations with thousands of objects and do very sophisticated analysis on those objects. However, industrial simulations often required tens or hundreds of thousands of objects, and Simkit had difficulty scaling up to such levels. The second alternative to developing KBE is illustrated by the CATIA product suite. CATIA started with products for CAD and other traditional industrial engineering applications and added knowledge-based capabilities on to them; for example, their KnowledgeWare module.

History KBE developed in the 1980s. It was part of the initial wave of investment in Artificial Intelligence for business that fueled expert systems. Like expert systems, it relied on what at the time were leading edge advances in corporate information technology such as PCs, workstations, and client-server architectures. These same technologies were also facilitating the growth of CAx and CAD software. CAD tended to drive leading edge technologies and even push them past their current limits. The best example of this was object-oriented programming and database technology, which were adapted by CAD when most corporate information technology shops were dominated by relational databases and procedural programming. As with expert systems, KBE suffered a downturn during the AI Winter. Also, as with expert systems and artificial intelligence technology in general, there was renewed interest with the Internet. In the case of KBE, the interest was perhaps strongest in the business-to-business type of electronic commerce and technologies that facilitate the definition of industry standard vocabularies and ontologies for manufactured products. The semantic web is an extension of the World Wide Web standards proposed by Tim Berners Lee. This will be a knowledge-based Internet built on ontologies, objects, and frame technologies that were also enabling technologies for KBE. Important technologies for the semantic web are XML, RDF, and OWL. The semantic web has excellent potential for KBE, and KBE ontologies and projects are a strong area for current research.

… excerpt ends here. Continue reading the full article.

Worked examples

Example 1 — a first encounter with Knowledge-based engineering

Start with the simplest possible case. Write down what Knowledge-based engineering claims or describes in one sentence, then invent the smallest concrete situation in which that sentence is true. In engineering, the smallest case is usually a single object, a single equation or a single measurement. Check that every symbol or term in your sentence has a meaning in that case.

Example 2 — changing one variable

Take the situation from Example 1 and change exactly one quantity: double it, halve it, or set it to zero. Predict what should happen to Knowledge-based engineering before you calculate. Comparing your prediction with the result is the fastest way to find out whether you understand the idea or only the words.

Example 3 — an exam-style question

Typical questions about Knowledge-based engineering ask you to (a) state it precisely, (b) apply it to given data, and (c) explain a limitation. Practise writing all three answers in under five minutes; the third part is what separates a full-mark answer from an average one.

Applications of Knowledge-based engineering

In research: Knowledge-based engineering appears in engineering research whenever the underlying quantities have to be modelled precisely. Papers usually cite it as a starting assumption and then explore where it breaks down.
In technology and industry: Engineering practice reuses Knowledge-based engineering in design rules, simulations and safety margins. Knowing the idea lets you read a specification sheet and understand why the numbers look the way they do.
In the classroom: Knowledge-based engineering is common in secondary-school and first-year university syllabi. It links to neighbouring topics Computer-aided design, Knowledge engineering, Knowledge management, so understanding it makes those chapters shorter.
In everyday life: Look for Knowledge-based engineering outside the textbook — in sport, cooking, traffic, electronics or the sky above you. An example you found yourself is remembered far longer than one you were given.

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How to study Knowledge-based engineering in 20 minutes

Read the reference excerpt below once, without taking notes.
Close the page and write down what Knowledge-based engineering means in your own words.
Compare your version with the excerpt and mark what you missed.
Work through the three examples above with pen and paper.
Explain Knowledge-based engineering out loud to somebody else — or to Teacher Smith in the lgStudy chat.

Frequently asked questions

What is Knowledge-based engineering in simple terms?

Why does Knowledge-based engineering matter?

Because it connects several engineering ideas at once: it gives you a definition you can apply, a quantity you can calculate, and a way to check whether a result is plausible.

How should I study Knowledge-based engineering?

Read the excerpt, restate it from memory, then work through the examples and applications listed on this page. The five-step study plan above takes about twenty minutes.

What does this page cover?

It gives you a compact reference excerpt plus original lgStudy explanations, examples, applications and study material on Knowledge-based engineering.