Design optimization application in accordance with product and process requirements

Prete, Antonio Del; Mazzotta, Daniele Giuseppe; Anglani, A.

doi:10.1016/j.advengsoft.2009.03.007

Cited by 9 publications

(6 citation statements)

References 5 publications

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“…In case when two tasks have a containment relationship, an arrow of a weight of ''100%'' is used. Optimizations of the design are often made retrospectively, taking new knowledge into account, [14]. Fig.…”

Section: Details Of Task Cmentioning

confidence: 99%

Knowledge evaluation in product lifecycle design and support

Bernard

Perry

et al. 2014

Knowledge-Based Systems

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Section: Details Of Task Cmentioning

confidence: 99%

Knowledge evaluation in product lifecycle design and support

Bernard

Perry

et al. 2014

Knowledge-Based Systems

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show abstract

“…Another disadvantage pertains to the assumption that the inputs and the outputs are certain values. In fact, the inputs and the outputs are uncertain in some cases, especially during the earlier stage of product development (Del Prete et al 2010). Assume the design variables form in a certain probabilistic distribution.…”

Section: The Disadvantagesmentioning

confidence: 99%

“…Conventional approaches, such as statistical methods or optimisation techniques, provide a robust way for engineers to determine the optimal parameters during the process of new product development (Bendsoe and Kikuchi 1988, Jeang 2008, Del Prete et al 2010. However, these tasks occur at a late stage of the product development cycle.…”

Section: Introductionmentioning

confidence: 99%

An innovation funnel process for set-based conceptual design via DOE exploration, DEA selection and computer simulation

Jeang

Liang²

2012

International Journal of Production Research

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This paper presents a conceptual design approach including pattern creation from designers, alternative exploration with a DOE matrix, alternative analysis via computer simulation and alternative selection by DEA analysis. Designers possessing domain knowledge create various design patterns to meet the requirements of product performance and customer expectations. Then, based on these design patterns, the alternatives, considered as decision-making units (DMUs), are extracted from various quality level combinations by following the use of the DOE matrix. The nature of the DOE matrix ensures that distinctive representatives are constructed for all design alternatives. The total alternatives (DMUs) consist of the alternatives associated with all the patterns. Computer simulation with ANSYS software is introduced to convert the quality level combination of each alternative (DMU) into simulated outputs, which are further categorised into DEA inputs and DEA outputs for DEA frontier analysis. Four DEA methods, CCR-min input, CCR-max output, BCC-min input and BCC-max output, are used for analysing typical market representatives resulting from market uncertainty. The found efficiencies are used to rank and select the explored alternatives (DMUs) for the next stage of the detailed design. A bike-frame product is chosen as an example to demonstrate the proposed approach. The results clearly show that the proposed approach enables designers to economically select appropriate design alternatives that satisfy performance expectations during the conceptual design stage.

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“…Their work utilises CL data to compute the tool's inclination angle relative to the generated surface and then the tool engagement in the material as it was mentioned in [9]. Der Prete et al [30] proposed a methodology capable of utilising coordinate points measured from a virtual coordinate measuring machine (CMM) so as to optimise machining operations. In contrast to traditional inspection approaches, their work investigates in advance final virtual outputs thus resulting to the reduction of the large machining time required due to the high uncertainty level.…”

Section: Introductionmentioning

confidence: 99%

Development of a software-automated intelligent sculptured surface machining optimization environment

Fountas

Vaxevanidis

Stergiou

et al. 2014

Int J Adv Manuf Technol

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To ensure the quality of machined products at minimum cost and maximum effectiveness, it is crucial that selection of optimum machining parameters should be done when computer numerically controlled (CNC) machine tools technology is employed. Traditionally, experience of the operator plays a major role in the selection of efficient parameter values; however, attaining optimum ones each time by even skilled end users, is extremely difficult. This paper takes advantage of the possibilities of current computer-aided design (CAD)/computer-aided manufacturing (CAM) technology and implements a genetic algorithm for optimising CNC machining operations mainly for sculptured surfaces. The algorithm has been developed as a hosted application to a cutting-edge CAD/CAM system. Collaboration among applications has been achieved through programming for software automation by utilising the application programme interface of the system. The approach was implemented to a group of test sculptured models with different properties whilst one of them has been actually machined using typical resources. Results obtained after the implementation indicated that the methodology is capable of providing optimum values for process parameters on its way to maintain both productivity and high quality.

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Design optimization application in accordance with product and process requirements

Cited by 9 publications

References 5 publications

Knowledge evaluation in product lifecycle design and support

Knowledge evaluation in product lifecycle design and support

An innovation funnel process for set-based conceptual design via DOE exploration, DEA selection and computer simulation

Development of a software-automated intelligent sculptured surface machining optimization environment

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