Taking the Grunt Work Out of Tolerance Optimization

2006 ◽  
Author(s):  
Christopher Jayakaran ◽  
Ragini Patel ◽  
Prashant Momaya ◽  
K. Roopesh ◽  
Umeshchandra Ananthanarayana ◽  
...  
Keyword(s):  
Optimization Problem ◽  
Optimal Allocation ◽  
Tolerance Allocation ◽  
Manufacturing Processes ◽  
Process Selection ◽  
Second Stage ◽  
Product Design Process ◽  
Stage Process ◽  
Manufacturing Process Selection ◽  
Selection Of

The activity of tolerance allocation and optimization is a critical step in the product design process. This inherent trade-off between design objectives and process capability poses challenges in achieving right tolerances, both technically and effort-wise. Traditional methods in tolerance allocation are mostly regressive and are constrained by selection of the manufacturing processes. A progressive approach to tolerance allocation that does not assume these processes helps in achieving optimality of the tolerances and selection of manufacturing processes to realize the design. The two-stage process suggested in this paper formulates an optimization problem that allocates the tolerances based on sensitivities of tolerance values at the first stage followed by manufacturing process selection and further optimization to adhere to the processes selected in the second stage. The approach aims at achieving optimal allocation of tolerances and assignment of the manufacturing processes, while keeping the optimization problem computationally simple, although iterative.

Systematic Process Selection in Mechanical Design

1996 ◽  
Author(s):  
Amal M. K. Esawi ◽  
Michael F. Ashby
Keyword(s):  
Manufacturing Process ◽  
Mechanical Design ◽  
Selection Procedure ◽  
Manufacturing Processes ◽  
Process Selection ◽  
Systematic Selection ◽  
Cost Penalty ◽  
The Right ◽  
The Cost ◽  
Manufacturing Process Selection

Abstract There has been a recent awareness of the importance of making the right manufacturing decisions early in the design process before the cost penalty of making changes becomes too high. The selection of the most appropriate manufacturing process — of which there are a very large number — is one such decision. It is commonly based on human-resident experience or on established local practice. As such, some potentially-usable processes may be overlooked. This paper explores ways in which process selection might be made more systematic. It presents a procedure for manufacturing process selection which considers all manufacturing processes and eliminates the ones which cannot satisfy the design requirements. This is achieved using Process Selection Charts in which process capabilities are displayed graphically. A procedure for the ranking of the successful processes based on cost is under development. The systematic selection procedure lends itself well to computer implementation. A database of manufacturing processes and an advanced user interface thus provide ideal support for designers. Cambridge Materials Selector (CMS) software is currently being applied to manufacturing process selection.

Methods for Process Selection in Design

1990 ◽  
Author(s):  
K. Ishii ◽  
C. H. Lee ◽  
R. A. Miller
Keyword(s):  
Life Cycle Cost ◽  
Object Oriented ◽  
Manufacturing Processes ◽  
Future Challenge ◽  
Process Selection ◽  
Development Environment ◽  
Types Of Knowledge ◽  
Major Factors ◽  
Case Based ◽  
Selection Of

Abstract This paper describes our proposed methodology for process selection that applies to the early stages of product design. We focus on net-shape manufacturing processes and identify the major factors that affect the selection of an appropriate process. The sequence at which designers typically make decisions depends largely on the nature of the product and the development environment. Thus, a versatile methodology should consider all the factors simultaneously in assessing the suitability of the candidate processes. The paper describes three types of knowledge that represent the compatibility of various processes to a given set of specifications: a) Case-based knowledge, i.e., templates of good, bad, and poor combination of decisions, b) Ordinal relationships among candidate processes based on interval analysis of cost, and c) Life-cycle cost estimate. Each type of knowledge gives an evaluation of suitability (compatibility) of candidate processes. Our future challenge lies in combining these measures at various stages of product development. Our initial studies on relationships between process selection and influencing factors lead to a HyperCard stack which stores information in an object-oriented fashion. This stack contains information which is the basis for our future computer-aid for process selection.

The Reduced Tolerance Allocation Problem

2016 ◽  
Author(s):  
David Sh. L. Shoukr ◽  
Mohamed H. Gadallah ◽  
Sayed M. Metwalli
Keyword(s):  
Genetic Algorithm ◽  
Optimization Problem ◽  
Computational Effort ◽  
Allocation Problem ◽  
Tolerance Allocation ◽  
Benchmark Problems ◽  
Manufacturing Processes ◽  
Manufacturing Cost ◽  
Functional Requirements ◽  
Different Dimensions

Tolerance allocation is a necessary and important step in product design and development. It involves the assignment of tolerances to different dimensions such that the manufacturing cost is minimum, while maintaining the tolerance stack-up conditions satisfied. Considering the design functional requirements, manufacturing processes, and dimensional and/or geometrical tolerances, the tolerance allocation problem requires intensive computational effort and time. An approach is proposed to reduce the size of the tolerance allocation problem using design of experiments (DOE). Instead of solving the optimization problem for all dimensional tolerances, it is solved for the significant dimensions only and the insignificant dimensional tolerances are set at lower control levels. A Genetic Algorithm is developed and employed to optimize the synthesis problem. A set of benchmark problems are used to test the proposed approach, and results are compared with some standard problems in literature.

Optimal Tolerance Allocation Over Multiple Manufacturing Alternatives

1992 ◽  
Author(s):  
Jonathan Cagan ◽  
Thomas R. Kurfess
Keyword(s):  
Simulated Annealing ◽  
Optimization Problem ◽  
Simulated Annealing Algorithm ◽  
Minimum Cost ◽  
Tolerance Allocation ◽  
Hyperbolic Function ◽  
Manufacturing Processes ◽  
Worst Case ◽  
Annealing Algorithm ◽  
Manufacturing Alternatives

Abstract We introduce a methodology for concurrent design that considers the allocation of tolerances and manufacturing processes for minimum cost. Cost is approximated as a hyperbolic function over tolerance, and worst-case stack-up tolerance is assumed. Two simulated annealing techniques are introduced to solve the optimization problem. The first assumes independent, unordered, manufacturing processes and uses a Monte-Carlo simulation; the second assumes well known individual process cost functions which can be manipulated to create a single continuous function of cost versus tolerance with discontinuous derivatives solved with a continuous simulated annealing algorithm. An example utilizing a system of friction wheels over the manufacturing processes of turning, grinding, and saw cutting bar stock demonstrates excellent results.

A Model for Text Summarization

2017 ◽  
Vol 13 (1) ◽  
pp. 67-85 ◽  
Author(s):  
Rasim M. Alguliyev ◽  
Ramiz M. Aliguliyev ◽  
Nijat R. Isazade ◽  
Asad Abdi ◽  
Norisma Idris
Keyword(s):  
Optimization Problem ◽  
Novel Mutation ◽  
Selection Method ◽  
Text Summarization ◽  
Data Sets ◽  
Second Stage ◽  
Optimum Selection ◽  
Adaptive Differential Evolution ◽  
Mutation Strategy ◽  
Selection Of

Text summarization is a process for creating a concise version of document(s) preserving its main content. In this paper, to cover all topics and reduce redundancy in summaries, a two-stage sentences selection method for text summarization is proposed. At the first stage, to discover all topics the sentences set is clustered by using k-means method. At the second stage, optimum selection of sentences is proposed. From each cluster the salient sentences are selected according to their contribution to the topic (cluster) and their proximity to other sentences in cluster to avoid redundancy in summaries until the appointed summary length is reached. Sentence selection is modeled as an optimization problem. In this study, to solve the optimization problem an adaptive differential evolution with novel mutation strategy is employed. With a test on benchmark DUC2001 and DUC2002 data sets, the ROUGE value of summaries got by the proposed approach demonstrated its validity, compared to the traditional methods of sentence selection and the top three performing systems for DUC2001 and DUC2002.

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