Standards, Codes of Practice and Expert Systems

1988 ◽  
Vol 21 (1) ◽  
pp. 5-9 ◽  
Author(s):  
E G McCluskey ◽  
S Thompson ◽  
D M G McSherry
Keyword(s):  
Expert System ◽  
Expert Systems ◽  
Engineering Design ◽  
Performance Criteria ◽  
Design Problems ◽  
Codes Of Practice ◽  
Engineering Design Problems ◽  
And Performance

Many engineering design problems require reference to standards or codes of practice to ensure that acceptable safety and performance criteria are met. Extracting relevant data from such documents can, however, be a problem for the unfamiliar user. The use of expert systems to guide the retrieval of information from standards and codes of practice is proposed as a means of alleviating this problem. Following a brief introduction to expert system techniques, a tool developed by the authors for building expert system guides to standards and codes of practice is described. The steps involved in encoding the knowledge contained in an arbitrarily chosen standard are illustrated. Finally, a typical consultation illustrates the use of the expert system guide to the standard.

The Complementary Roles of Symbolic Computing and Numerical Optimization in Engineering Design Software

1987 ◽  
Author(s):  
R. Ellsworth ◽  
A. Parkinson ◽  
F. Cain
Keyword(s):  
Expert System ◽  
Engineering Design ◽  
Numerical Optimization ◽  
Optimization Methods ◽  
Design Rule ◽  
Rule Base ◽  
Design Problems ◽  
Symbolic Computing ◽  
Engineering Design Problems ◽  
Trial And Error Method

Abstract In many engineering design problems, the designer converges upon a good design by iteratively evaluating a mathematical model of the design problem. The trial-and-error method used by the designer to converge upon a solution may be complex and difficult to capture in an expert system. It is suggested that in many cases, the design rule base could be made significantly smaller and more maintainable by using numerical optimization methods to identify the best design. The expert system is then used to define the optimization problem and interpret the solution, as well as to apply the true heuristics to the problem. An example of such an expert system is presented for the design of a valve anti-cavitation device. Because of the capabilities provided by the optimization software, the expert system has been able to outperform the expert in the test cases evaluated so far.

scholarly journals SRIFA: Stochastic Ranking with Improved-Firefly-Algorithm for Constrained Optimization Engineering Design Problems

Mathematics ◽  
2019 ◽  
Vol 7 (3) ◽  
pp. 250 ◽  
Author(s):  
Umesh Balande ◽  
Deepti Shrimankar
Keyword(s):  
Constrained Optimization ◽  
Engineering Design ◽  
Real World ◽  
Optimization Design ◽  
Firefly Algorithm ◽  
Optimization Problems ◽  
Design Problems ◽  
Improved Firefly Algorithm ◽  
Engineering Design Problems ◽  
And Performance

Firefly-Algorithm (FA) is an eminent nature-inspired swarm-based technique for solving numerous real world global optimization problems. This paper presents an overview of the constraint handling techniques. It also includes a hybrid algorithm, namely the Stochastic Ranking with Improved Firefly Algorithm (SRIFA) for solving constrained real-world engineering optimization problems. The stochastic ranking approach is broadly used to maintain balance between penalty and fitness functions. FA is extensively used due to its faster convergence than other metaheuristic algorithms. The basic FA is modified by incorporating opposite-based learning and random-scale factor to improve the diversity and performance. Furthermore, SRIFA uses feasibility based rules to maintain balance between penalty and objective functions. SRIFA is experimented to optimize 24 CEC 2006 standard functions and five well-known engineering constrained-optimization design problems from the literature to evaluate and analyze the effectiveness of SRIFA. It can be seen that the overall computational results of SRIFA are better than those of the basic FA. Statistical outcomes of the SRIFA are significantly superior compared to the other evolutionary algorithms and engineering design problems in its performance, quality and efficiency.

Quantified Relations: A Class of Predicate Logic Design Constraints Among Sets of Manufacturing, Operating, and Other Variations

1996 ◽  
Author(s):  
William W. Finch ◽  
Allen C. Ward
Keyword(s):  
Engineering Design ◽  
Predicate Logic ◽  
Operating Conditions ◽  
Causal Relationships ◽  
Engineering Systems ◽  
Multiple Sources ◽  
Design Problems ◽  
Engineering Design Problems ◽  
Computing Effort ◽  
And Performance

Abstract This paper addresses a class of engineering design problems in which multiple sources of variations affect a product’s design, manufacture, and performance. Examples of these sources include uncertainty in nominal dimensions, variations in manufacture, changing environmental or operating conditions, and operator adjustments. Quantified relations (QR’s) are defined as a class of predicate logic expressions representing constraints between sets of design variations. Within QR’s, each variable’s quantifier and the order of quantification express a physical system’s causal relationships. This paper also presents an algorithm which propagates intervals through QR’s involving continuous, monotonic equations. Causal relationships between variables in engineering systems are discussed, and a tabular representation for them is presented. This work aims to broaden the application of automated constraint satisfaction algorithms, shortening design cycles for this class of problem by reducing modeling, and possibly computing effort. It seems to subsume Ward’s prior work on the Label Interval Calculus, extending the approach to a wider range of engineering design problems.

The Complementary Roles of Knowledge-Based Systems and Numerical Optimization in Engineering Design Software

1989 ◽  
Vol 111 (1) ◽  
pp. 100-103 ◽  
Author(s):  
R. Ellsworth ◽  
A. Parkinson ◽  
F. Cain
Keyword(s):  
Expert System ◽  
Engineering Design ◽  
Numerical Optimization ◽  
Optimization Methods ◽  
Design Rule ◽  
Rule Base ◽  
Design Problems ◽  
Knowledge Based ◽  
Engineering Design Problems ◽  
Trial And Error Method

In many engineering design problems, the designer converges upon a good design by iteratively evaluating a mathematical model of the design problem. The trial-and-error method used by the designer to converge upon a solution may be complex and difficult to capture in an expert system. It is suggested that in many cases, the design rule base could be made significantly smaller and more maintainable by using numerical optimization methods to identify the best design. The expert system is then used to define the optimization problem and interpret the solution, as well as to apply the true heuristics to the problem. An example of such an expert system is presented for the design of a valve anticavitation device. Because of the capabilities provided by the optimization software, the expert system has been able to outperform the expert in the test cases evaluated so far.

Preferences and Correlated Uncertainties in Engineering Design

2003 ◽  
Author(s):  
Tomonori Honda ◽  
Erik K. Antonsson
Keyword(s):  
Engineering Design ◽  
Design Variable ◽  
Design Space ◽  
Design Method ◽  
Design Problems ◽  
Performance Variables ◽  
Design Variables ◽  
Engineering Design Problems ◽  
Engineering Problems ◽  
And Performance

The Method of Imprecision (MOI) is a multi-objective design method that maximizes the overall degree of both design and performance preferences. Sets of design variables are iteratively selected, and the corresponding performances are approximately computed. The designer’s judgment (expressed as preferences) are combined (aggregated) with the customer’s preferences, to determine the overall preference for sets of points in the design space. In addition to degrees of preference for values of the design and performance variables, engineering design problems also typically include uncertainties caused by uncontrolled variations, for example, measuring and fabrication limitations. This paper illustrates the computation of expected preference for cases where the uncertainties are uncorrelated, and also where the uncertainties are correlated. The result is a “best” set of design variable values for engineering problems, where the overall aggregated preference is maximized. As is illustrated by the examples shown here, where both preferences and uncontrolled variations are present, the presence of uncertainties can have an important effect on the choice of the overall best set of design variable values.

Architecture of an Expert System for Oceanographic Mooring Design

1989 ◽  
Vol 111 (2) ◽  
pp. 138-143 ◽  
Author(s):  
S. L. Wood ◽  
R. A. Skop
Keyword(s):  
Expert System ◽  
Engineering Design ◽  
Design Process ◽  
Single Point ◽  
End User ◽  
Design Problems ◽  
Acoustic Data ◽  
Engineering Design Problems ◽  
Engineering Design Problem ◽  
Rules Of Thumb

Methods for the design and analysis of oceanographic moorings are well established (Berteaux, 1976). However, as with most engineering design problems, there are certain “rules-of-thumb” or “tricks-of-the-trade” that streamline the design process and enhance the performance of the final product. These rules-of-thumb are normally known to only a small cadre of people—experts—who have deep involvement and experience in the particular engineering design problem. These rules-of-thumb and other knowledge of several experts are incorporated to develop the fundamental architecture of an expert system for the design of single-point, subsurface, oceanographic moorings. Such moorings are used worldwide to collect oceanographic and acoustic data. The projected end user of this expert system is the oceanographer or acoustician who wishes to design and/or cost out a mooring but has not the access to or support for a mooring design group.

Biologically Inspired Design: A Macrocognitive Account

2010 ◽  
Author(s):  
Swaroop S. Vattam ◽  
Michael Helms ◽  
Ashok K. Goel
Keyword(s):  
Biological Sciences ◽  
Information Processing ◽  
Engineering Design ◽  
Design Patterns ◽  
Information Processing Model ◽  
Biologically Inspired ◽  
Design Problems ◽  
Engineering Design Problems ◽  
Other Information ◽  
Biologically Inspired Design

Biologically inspired engineering design is an approach to design that espouses the adaptation of functions and mechanisms in biological sciences to solve engineering design problems. We have conducted an in situ study of designers engaged in biologically inspired design. Based on this study we develop here a macrocognitive information-processing model of biologically inspired design. We also compare and contrast the model with other information-processing models of analogical design such as TRIZ, case-based design, and design patterns.

scholarly journals Lévy-Flight Moth-Flame Algorithm for Function Optimization and Engineering Design Problems

2016 ◽  
Vol 2016 ◽  
pp. 1-22 ◽  
Author(s):  
Zhiming Li ◽  
Yongquan Zhou ◽  
Sen Zhang ◽  
Junmin Song
Keyword(s):  
Engineering Design ◽  
Function Optimization ◽  
Superior Performance ◽  
Local Optimum ◽  
Lévy Flight ◽  
Design Problems ◽  
Levy Flight ◽  
Spiral Path ◽  
Straight Line ◽  
Engineering Design Problems

The moth-flame optimization (MFO) algorithm is a novel nature-inspired heuristic paradigm. The main inspiration of this algorithm is the navigation method of moths in nature called transverse orientation. Moths fly in night by maintaining a fixed angle with respect to the moon, a very effective mechanism for travelling in a straight line for long distances. However, these fancy insects are trapped in a spiral path around artificial lights. Aiming at the phenomenon that MFO algorithm has slow convergence and low precision, an improved version of MFO algorithm based on Lévy-flight strategy, which is named as LMFO, is proposed. Lévy-flight can increase the diversity of the population against premature convergence and make the algorithm jump out of local optimum more effectively. This approach is helpful to obtain a better trade-off between exploration and exploitation ability of MFO, thus, which can make LMFO faster and more robust than MFO. And a comparison with ABC, BA, GGSA, DA, PSOGSA, and MFO on 19 unconstrained benchmark functions and 2 constrained engineering design problems is tested. These results demonstrate the superior performance of LMFO.

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