Design for Six Sigma through collaborative multiobjective optimization

Baril, Chantal; Yacout, Soumaya; Clément, Bernard

doi:10.1016/j.cie.2010.09.015

Cited by 52 publications

(21 citation statements)

References 34 publications

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“…The conclusion is that PP yields an evenly distributed set of solutions for an evenly distributed set of preference input parameters. [2]. LPP is integrated into the optimization step of this methodology.…”

Section: Other Methodological Contributionsmentioning

confidence: 99%

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Physical Programming: A Review of the State of the Art

Ilgın¹,

Gupta²

2012

SIC

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Most traditional multi-criteria optimization techniques require that the decision maker construct an aggregate objective function using the weights determined as a result of a trial and error process. Physical programming (PP) eliminates this tedious weight assignment process by providing decision makers with a flexible and more natural problem formulation. In PP, the decision maker specifies ranges of different degrees of desirability instead of defining weights. In this paper, we present a comprehensive review of PP studies by classifying them into four major categories (viz., methodological papers, industrial engineering applications, mechanical engineering applications and other applications). Insights from the review and future research directions conclude the paper.

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Section: Other Methodological Contributionsmentioning

confidence: 99%

“…LPP is integrated into the optimization step of this methodology. Baril et al [2] propose a collaborative interactive multi-objective algorithm by integrating several concepts involving the payoff table, physical programming, non-linear goal programming, the stability sets and sets subtraction algorithm.…”

Section: Other Methodological Contributionsmentioning

confidence: 99%

Physical Programming: A Review of the State of the Art

Ilgın¹,

Gupta²

2012

SIC

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“…Sci. 2018, 8,2635 3 of 23 be compared to a funnel (funnel model) that starts with a variety of ideas and a huge amount of information and ends with one idea in production. The process consists of different stages: -Planning: Includes assessment of technological developments and market objectives.…”

Section: Introductionmentioning

confidence: 99%

Advanced Design Applied to an Original Multi-Purpose Ventilator Achievable by Additive Manufacturing

Frizziero¹,

Donnici²,

Dhaimini³

et al. 2018

Applied Sciences

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Featured Application: After this work, we were able to check-with our own hands-the quality of the finished parts and the tensile strength, fall, and manipulation offered by the fusion of the material through MJF: we can therefore say that this type of process is very interesting in all those cases where it is necessary to prototypes characteristic of definitive and functional pieces and at the same time obtain a production with high numbers. This work aims to demonstrate how the high precision and operational speed of this production system make it suitable for the mass production of functional and finished objects and this can happen in motorcycling, automotive, nautical, aerospace, precision mechanics, etc.Abstract: Considering the progressively expansive trade world, "time to market" of productions and goods has turned into a key element for business accomplishment. There are diverse practices that antedate design faults and unveil products on the market in minus time. Among the most used methods in the design and explanation of the necessities, quality function deployment (QFD) and design for Six Sigma (DFSS) can be used. In the prototyping stage, it is probable to address the emergent technology of additive manufacturing. Today, 3D printing is employed as a quick prototyping technique. Nevertheless, the tangible task which industry is fronting is the adoption of these machines for large-scale production of components, which is now possible with new HP multi fusion. The goal of this paper is to illustrate the entire product development process taking advantage of the most modern models and technologies for the final realization of a case study that involves the design and prototyping of an innovative multifunctional fan (lamp, aroma diffuser, and fan) through the multi jet fusion of HP. To begin with, issues related to the DFSS, the QFD and their application to identify the fan requirements are explored. Once the requirements have been defined, the modern CAD design systems and the CAE systems for the validation of the case study will be analyzed and applied. Finally, HP's multi jet fusion methodology and design rules for additive manufacturing will be analyzed in detail, trying to exploit all the positive aspects it offers.

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“…Reliability estimation based on production phase data is an effective instrument to investigate the effect of production process on system reliability (Jinghuan et al 2012). Variability of production processes and system performance can be quantitatively estimated, and the control and reliability improvement in systems can be investigated using process capability indexes (PCI) (Pearn * et al 2005;Baril et al 2011), which reflect the condition of reliability and quality in the production process (Jeang and Chung 2008). There are papers that they have investigated the performance of production process by using PCI like Cp, Cpk, sigma level and studied the product design based on the six sigma and reliability.…”

Section: Introductionmentioning

confidence: 99%

“…Used in this procedure is the sigma level index, to which reliability can be directly attributed. For example, for a sigma level of 3, the probability of the system's performing a defined function within a day under existing conditions is 99.73% and the probability of failure is 0.27% and for achieving 100% reliability, it is unnecessary to optimize in 6 sigma level (Baril et al 2011).…”

Section: Introductionmentioning

confidence: 99%

Developing an economical model for reliability allocation of an electro-optical system by considering reliability improvement difficulty, criticality, and subsystems dependency

Mohamadi

Karbasian

2018

J Ind Eng Int

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The nature of electro-optical equipment in various industries and the pursuit of the goal of reducing costs demand high reliability on the part of electro-optical systems. In this respect, reliability improvement could be addressed through a reliability allocation problem. Subsystem reliability must be increased such that the requirements as well as defined requisite functions are ensured in accordance with the designers' opinion. This study is an attempt to develop a multiobjective model by maximizing system reliability and minimizing costs in order to investigate design phase costs as well as production phase costs. To investigate reliability improvement feasibility in the design phase, effective feasibility factors in the system are used and the sigma level index is incorporated in the production phase as the reliability improvement difficulty factor. Thus, subsystem reliability improvement priorities are taken into consideration. Subsystem dependency degree is investigated through the design structure matrix and incorporated into the model's limitation together with modified criticality. The primary model is converted into a single-objective model through goal programming. This model is implemented on electro-optical systems, and the results are analyzed. In this method, reliability allocation follows two steps. First, based on the allocation weights, a range is determined for the reliability of subsystems. Afterward, improvement is initiated based upon the costs and priorities of subsystem reliability improvement. Keywords Reliability allocation Á Design and production cost Á Reliability improvement difficulty Á Sigma level Á Design structure matrix (DSM)

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Design for Six Sigma through collaborative multiobjective optimization

Cited by 52 publications

References 34 publications

Physical Programming: A Review of the State of the Art

Physical Programming: A Review of the State of the Art

Advanced Design Applied to an Original Multi-Purpose Ventilator Achievable by Additive Manufacturing

Developing an economical model for reliability allocation of an electro-optical system by considering reliability improvement difficulty, criticality, and subsystems dependency

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