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(PDF) Reducing Yield Variation via Robust Parameter Design
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window.loswp.willEdgeCache = false; window.loswp.work = {"work":{"id":1977738,"created_at":"2012-09-28T06:20:12.042-07:00","from_world_paper_id":111451544,"updated_at":"2025-02-01T19:47:35.398-08:00","_data":{"ai_title_tag":"Reducing Yield Variation via Robust Parameter Design","grobid_abstract":"Variation inside a process/product is due to special or common causes. These causes may be classified as controllable and uncontrollable factors. Controllable factors may be analyzed using statistical tools to find the best setting to minimize variation; on the other side, uncontrollable, or noise factors are more difficult or expensive to control during normal operation. The factors causing variation, located inside the raw material are called Internal Noise Factors. This article studies a process experiencing high variation in a raw material characteristic identified as pH, which causes significant variation in the output yield. A Robust Parameter Design (RPD) approach is used to identify the set of working parameters that allows minimizing the process output variation. Four controllable factors (at two and three levels), and one noise factor (at three levels), are selected for the experimental design, using a L 18 orthogonal array. Confirmation runs are used to verify the ability of the model to predict responses despite the noise factor level. The results presented in this paper show that variation of the output yield due to the raw material's pH parameter, is reduced by 53%, and the amount of resulting yield is increased by 9%, when the RPD approach is used to identify the working parameters.","publication_name":"uacj.mx","grobid_abstract_attachment_id":"28593425"},"document_type":"paper","pre_hit_view_count_baseline":0,"quality":"low","language":"en","title":"Paper Title Authors","broadcastable":false,"draft":null,"has_indexable_attachment":true,"indexable":true}}["work"]; window.loswp.workCoauthors = [2575666]; window.loswp.locale = "en"; window.loswp.countryCode = "SG"; window.loswp.cwvAbTestBucket = ""; window.loswp.designVariant = "ds_vanilla"; window.loswp.fullPageMobileSutdModalVariant = "control"; window.loswp.useOptimizedScribd4genScript = false; window.loginModal = {}; window.loginModal.appleClientId = 'edu.academia.applesignon'; window.userInChina = "false";</script><script defer="" src="https://accounts.google.com/gsi/client"></script><div class="ds-loswp-container"><div class="ds-work-card--grid-container"><div class="ds-work-card--container js-loswp-work-card"><div class="ds-work-card--cover"><div class="ds-work-cover--wrapper"><div class="ds-work-cover--container"><button class="ds-work-cover--clickable js-swp-download-button" data-signup-modal="{"location":"swp-splash-paper-cover","attachmentId":28593425,"attachmentType":"pdf"}"><img alt="First page of “Paper Title Authors”" class="ds-work-cover--cover-thumbnail" src="https://0.academia-photos.com/attachment_thumbnails/28593425/mini_magick20190426-4388-tjto3d.png?1556345416" /><img alt="PDF Icon" class="ds-work-cover--file-icon" src="//a.academia-assets.com/images/single_work_splash/adobe_icon.svg" /><div class="ds-work-cover--hover-container"><span class="material-symbols-outlined" style="font-size: 20px" translate="no">download</span><p>Download Free PDF</p></div><div class="ds-work-cover--ribbon-container">Download Free PDF</div><div class="ds-work-cover--ribbon-triangle"></div></button></div></div></div><div class="ds-work-card--work-information"><h1 class="ds-work-card--work-title">Paper Title Authors</h1><div class="ds-work-card--work-authors ds-work-card--detail"><a class="ds-work-card--author js-wsj-grid-card-author ds2-5-body-md ds2-5-body-link" data-author-id="2575666" href="https://uaeh.academia.edu/GloriaPerezVera"><img alt="Profile image of Gloria Perez Vera" class="ds-work-card--author-avatar" src="https://0.academia-photos.com/2575666/807052/1003999/s65_gloria.perez_vera.jpg" />Gloria Perez Vera</a></div><div class="ds-work-card--detail"><p class="ds-work-card--detail ds2-5-body-sm">uacj.mx</p><div class="ds-work-card--work-metadata"><div class="ds-work-card--work-metadata__stat"><span class="material-symbols-outlined" style="font-size: 20px" translate="no">visibility</span><p class="ds2-5-body-sm" id="work-metadata-view-count">…</p></div><div class="ds-work-card--work-metadata__stat"><span class="material-symbols-outlined" style="font-size: 20px" translate="no">description</span><p class="ds2-5-body-sm">11 pages</p></div><div class="ds-work-card--work-metadata__stat"><span class="material-symbols-outlined" style="font-size: 20px" translate="no">link</span><p class="ds2-5-body-sm">1 file</p></div></div><script>(async () => { const workId = 1977738; const worksViewsPath = "/v0/works/views?subdomain_param=api&work_ids%5B%5D=1977738"; const getWorkViews = async (workId) => { const response = await fetch(worksViewsPath); if (!response.ok) { throw new Error('Failed to load work views'); } const data = await response.json(); return data.views[workId]; }; // Get the view count for the work - we send this immediately rather than waiting for // the DOM to load, so it can be available as soon as possible (but without holding up // the backend or other resource requests, because it's a bit expensive and not critical). const viewCount = await getWorkViews(workId); const updateViewCount = (viewCount) => { try { const viewCountNumber = parseInt(viewCount, 10); if (viewCountNumber === 0) { // Remove the whole views element if there are zero views. document.getElementById('work-metadata-view-count')?.parentNode?.remove(); return; } const commaizedViewCount = viewCountNumber.toLocaleString(); const viewCountBody = document.getElementById('work-metadata-view-count'); if (!viewCountBody) { throw new Error('Failed to find work views element'); } viewCountBody.textContent = `${commaizedViewCount} views`; } catch (error) { // Remove the whole views element if there was some issue parsing. document.getElementById('work-metadata-view-count')?.parentNode?.remove(); throw new Error(`Failed to parse view count: ${viewCount}`, error); } }; // If the DOM is still loading, wait for it to be ready before updating the view count. if (document.readyState === "loading") { document.addEventListener('DOMContentLoaded', () => { updateViewCount(viewCount); }); // Otherwise, just update it immediately. } else { updateViewCount(viewCount); } })();</script></div><p class="ds-work-card--work-abstract ds-work-card--detail ds2-5-body-md">Variation inside a process/product is due to special or common causes. These causes may be classified as controllable and uncontrollable factors. Controllable factors may be analyzed using statistical tools to find the best setting to minimize variation; on the other side, uncontrollable, or noise factors are more difficult or expensive to control during normal operation. The factors causing variation, located inside the raw material are called Internal Noise Factors. This article studies a process experiencing high variation in a raw material characteristic identified as pH, which causes significant variation in the output yield. A Robust Parameter Design (RPD) approach is used to identify the set of working parameters that allows minimizing the process output variation. Four controllable factors (at two and three levels), and one noise factor (at three levels), are selected for the experimental design, using a L 18 orthogonal array. Confirmation runs are used to verify the ability of the model to predict responses despite the noise factor level. The results presented in this paper show that variation of the output yield due to the raw material's pH parameter, is reduced by 53%, and the amount of resulting yield is increased by 9%, when the RPD approach is used to identify the working parameters.</p><div class="ds-work-card--button-container"><button class="ds2-5-button js-swp-download-button" data-signup-modal="{"location":"continue-reading-button--work-card","attachmentId":28593425,"attachmentType":"pdf","workUrl":"https://www.academia.edu/1977738/Paper_Title_Authors"}">See full PDF</button><button class="ds2-5-button ds2-5-button--secondary js-swp-download-button" data-signup-modal="{"location":"download-pdf-button--work-card","attachmentId":28593425,"attachmentType":"pdf","workUrl":"https://www.academia.edu/1977738/Paper_Title_Authors"}"><span class="material-symbols-outlined" style="font-size: 20px" translate="no">download</span>Download PDF</button></div><div class="ds-signup-banner-trigger-container"><div class="ds-signup-banner-trigger ds-signup-banner-trigger-control"></div></div><div class="ds-signup-banner ds-signup-banner-control"><div id="ds-signup-banner-close-button"><button class="ds2-5-button ds2-5-button--secondary ds2-5-button--inverse"><span class="material-symbols-outlined" style="font-size: 20px" translate="no">close</span></button></div><div class="ds-signup-banner-ctas"><img src="//a.academia-assets.com/images/academia-logo-capital-white.svg" /><h4 class="ds2-5-heading-serif-sm">Sign up for access to the world's latest research</h4><button class="ds2-5-button ds2-5-button--inverse ds2-5-button--full-width js-swp-download-button" data-signup-modal="{"location":"signup-banner"}">Sign up for free<span class="material-symbols-outlined" style="font-size: 20px" translate="no">arrow_forward</span></button></div><div class="ds-signup-banner-divider"></div><div class="ds-signup-banner-reasons"><div class="ds-signup-banner-reasons-item"><span class="material-symbols-outlined" style="font-size: 24px" translate="no">check</span><span>Get notified about relevant papers</span></div><div class="ds-signup-banner-reasons-item"><span class="material-symbols-outlined" style="font-size: 24px" translate="no">check</span><span>Save papers to use in your research</span></div><div class="ds-signup-banner-reasons-item"><span class="material-symbols-outlined" style="font-size: 24px" translate="no">check</span><span>Join the discussion with peers</span></div><div class="ds-signup-banner-reasons-item"><span class="material-symbols-outlined" style="font-size: 24px" translate="no">check</span><span>Track your impact</span></div></div></div><script>(() => { // Set up signup banner show/hide behavior: // 1. 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Within this framework, identifying the optimal factor settings that achieve desired process targets with minimum variance is critical and can translate to significant reductions in product waste and processing costs. In solving this problem, most traditional RPD models consider only a single quality characteristic of interest. However, products are often judged by multiple quality characteristics, which often have conflicting objectives. Conventional RPD models that address the multi‐response problem typically only examine like‐type cases, and those that consider mixed types of quality characteristics often overlook any asymmetry that is likely to exist in certain types.In contrast, this article proposes a multidisciplinary RPD methodology that provides an enhanced approach for modeling multiple, mixed type quality characteristics...</p><div class="ds-related-work--ctas"><button class="ds2-5-text-link ds2-5-text-link--inline js-swp-download-button" data-signup-modal="{"location":"wsj-grid-card-download-pdf-modal","work_title":"Solving the Multidisciplinary Robust Parameter Design Problem for Mixed Type Quality Characteristics under Asymmetric Conditions","attachmentId":112738258,"attachmentType":"pdf","work_url":"https://www.academia.edu/116675244/Solving_the_Multidisciplinary_Robust_Parameter_Design_Problem_for_Mixed_Type_Quality_Characteristics_under_Asymmetric_Conditions","alternativeTracking":true}"><span class="material-symbols-outlined" style="font-size: 18px" translate="no">download</span><span class="ds2-5-text-link__content">Download free PDF</span></button><a class="ds2-5-text-link ds2-5-text-link--inline js-wsj-grid-card-view-pdf" href="https://www.academia.edu/116675244/Solving_the_Multidisciplinary_Robust_Parameter_Design_Problem_for_Mixed_Type_Quality_Characteristics_under_Asymmetric_Conditions"><span class="ds2-5-text-link__content">View PDF</span><span class="material-symbols-outlined" style="font-size: 18px" translate="no">chevron_right</span></a></div></div><div class="ds-related-work--container js-wsj-grid-card" data-collection-position="1" data-entity-id="85093153" data-sort-order="default"><a class="ds-related-work--title js-wsj-grid-card-title ds2-5-body-md ds2-5-body-link" href="https://www.academia.edu/85093153/Robust_Parameter_Design_Methodology_for_Mixture_Process">Robust Parameter Design Methodology for Mixture Process</a><div class="ds-related-work--metadata"><a class="js-wsj-grid-card-author ds2-5-body-sm ds2-5-body-link" data-author-id="981591" href="https://independent.academia.edu/BereniceRios">Berenice Rios</a></div><p class="ds-related-work--metadata ds2-5-body-xs">DYNA MANAGEMENT</p><p class="ds-related-work--abstract ds2-5-body-sm">ABSTRACT: Robust parameter design (RPD) involves the effects size, component restrictions, and fixed effects of mixture-process-noise variables (MPNV) in product and process design. The traditional methodology does not necessarily ensure a minimum variance and a reasonable model adjustment. Contrary to process and noise variables, mixture factor levels are not independent, negative proportions are not allowed, and their summary must be equal to unity. A transformation function enables the incorporation of noise factors into the response surface model, thus estimating the mixture-process optimum variables that minimize the variance expression without a cross array. In addition, it allows the determination of a confidence region over a point to achieve another component combination with similar or higher quality products. The third-generation process capability index, Cpkm as an alternative signal to noise ratio (SNR) estimator, improve process capability and product quality for fixed...</p><div class="ds-related-work--ctas"><button class="ds2-5-text-link ds2-5-text-link--inline js-swp-download-button" data-signup-modal="{"location":"wsj-grid-card-download-pdf-modal","work_title":"Robust Parameter Design Methodology for Mixture Process","attachmentId":89901346,"attachmentType":"pdf","work_url":"https://www.academia.edu/85093153/Robust_Parameter_Design_Methodology_for_Mixture_Process","alternativeTracking":true}"><span class="material-symbols-outlined" style="font-size: 18px" translate="no">download</span><span class="ds2-5-text-link__content">Download free PDF</span></button><a class="ds2-5-text-link ds2-5-text-link--inline js-wsj-grid-card-view-pdf" href="https://www.academia.edu/85093153/Robust_Parameter_Design_Methodology_for_Mixture_Process"><span class="ds2-5-text-link__content">View PDF</span><span class="material-symbols-outlined" style="font-size: 18px" translate="no">chevron_right</span></a></div></div><div class="ds-related-work--container js-wsj-grid-card" data-collection-position="2" data-entity-id="65715856" data-sort-order="default"><a class="ds-related-work--title js-wsj-grid-card-title ds2-5-body-md ds2-5-body-link" href="https://www.academia.edu/65715856/Design_of_process_parameters_using_robust_design_techniques_and_multiple_criteria_optimization">Design of process parameters using robust design techniques and multiple criteria optimization</a><div class="ds-related-work--metadata"><a class="js-wsj-grid-card-author ds2-5-body-sm ds2-5-body-link" data-author-id="58601853" href="https://independent.academia.edu/Pattipati">Krishna Pattipati</a></div><p class="ds-related-work--metadata ds2-5-body-xs">IEEE Transactions on Systems, Man, and Cybernetics, 1995</p><p class="ds-related-work--abstract ds2-5-body-sm">This paper presents a methodology for the design of productdprocesses that makes use of the concepts of robust design and the techniques of muldiple criteria optimization for simultaneously optimizing many quality characteristics. First, a systematic approach to the selection of an efficient matrix experiment for a design problem is presented. Appropriate performance measures are obtained so that their joint optimization results in the minimum variation of product characteristics. The use of transformations is highlighted as a useful technique to statistically validate the design process. A discrete multiple criteria optimization algorithm that incorporates the methods of dominated approximations and reference points is developed to obtain nondominated solutions for the design problem. The methodology is illustrated using a case study gleaned from the literature. Alan A. Song (M'92) received the B.Eng. and M.Eng. degrees in electrical engineering, and the Ph.D. degree in systems engineering from</p><div class="ds-related-work--ctas"><button class="ds2-5-text-link ds2-5-text-link--inline js-swp-download-button" data-signup-modal="{"location":"wsj-grid-card-download-pdf-modal","work_title":"Design of process parameters using robust design techniques and multiple criteria optimization","attachmentId":77191650,"attachmentType":"pdf","work_url":"https://www.academia.edu/65715856/Design_of_process_parameters_using_robust_design_techniques_and_multiple_criteria_optimization","alternativeTracking":true}"><span class="material-symbols-outlined" style="font-size: 18px" translate="no">download</span><span class="ds2-5-text-link__content">Download free PDF</span></button><a class="ds2-5-text-link ds2-5-text-link--inline js-wsj-grid-card-view-pdf" href="https://www.academia.edu/65715856/Design_of_process_parameters_using_robust_design_techniques_and_multiple_criteria_optimization"><span class="ds2-5-text-link__content">View PDF</span><span class="material-symbols-outlined" style="font-size: 18px" translate="no">chevron_right</span></a></div></div><div class="ds-related-work--container js-wsj-grid-card" data-collection-position="3" data-entity-id="99519352" data-sort-order="default"><a class="ds-related-work--title js-wsj-grid-card-title ds2-5-body-md ds2-5-body-link" href="https://www.academia.edu/99519352/Robust_optimization_framework_for_process_parameter_and_tolerance_design">Robust optimization framework for process parameter and tolerance design</a><div class="ds-related-work--metadata"><a class="js-wsj-grid-card-author ds2-5-body-sm ds2-5-body-link" data-author-id="19496463" href="https://independent.academia.edu/FernandoManuelAlmeida">Fernando Manuel Almeida</a></div><p class="ds-related-work--metadata ds2-5-body-xs">AIChE Journal, 1998</p><p class="ds-related-work--abstract ds2-5-body-sm">This article introduces a framework for including different uncertainties at the chemical plant design stage. Through an integrated robust optimization approach and problem formulation, equipment, operating, control, and quality costs are simultaneously taken into account, leading to system, parameter, and tolerance design. Rather than using single pointwise solutions in the decision space, operating windows leading to overall best performance are identified and defined. Such windows and their width allow us to point out control needs and goals at a very early stage of plant design. Two small-scale case studies (for a CSTR and a batch distillation column) provide enough evidence to support the practicality of the optimization framework: the robust solutions found are digerent and much better than the corresponding solutions obtained with the fully deterministic optimization paradigms.</p><div class="ds-related-work--ctas"><button class="ds2-5-text-link ds2-5-text-link--inline js-swp-download-button" data-signup-modal="{"location":"wsj-grid-card-download-pdf-modal","work_title":"Robust optimization framework for process parameter and tolerance design","attachmentId":100586016,"attachmentType":"pdf","work_url":"https://www.academia.edu/99519352/Robust_optimization_framework_for_process_parameter_and_tolerance_design","alternativeTracking":true}"><span class="material-symbols-outlined" style="font-size: 18px" translate="no">download</span><span class="ds2-5-text-link__content">Download free PDF</span></button><a class="ds2-5-text-link ds2-5-text-link--inline js-wsj-grid-card-view-pdf" href="https://www.academia.edu/99519352/Robust_optimization_framework_for_process_parameter_and_tolerance_design"><span class="ds2-5-text-link__content">View PDF</span><span class="material-symbols-outlined" style="font-size: 18px" translate="no">chevron_right</span></a></div></div><div class="ds-related-work--container js-wsj-grid-card" data-collection-position="4" data-entity-id="49383606" data-sort-order="default"><a class="ds-related-work--title js-wsj-grid-card-title ds2-5-body-md ds2-5-body-link" href="https://www.academia.edu/49383606/Robust_Parameter_Design_Based_on_Back_Propagation_Neural_Network">Robust Parameter Design Based on Back Propagation Neural Network</a><div class="ds-related-work--metadata"><a class="js-wsj-grid-card-author ds2-5-body-sm ds2-5-body-link" data-author-id="2822349" href="https://pknu.academia.edu/TritiyaArungpadang">Tritiya Arungpadang</a></div><p class="ds-related-work--metadata ds2-5-body-xs">Korean Management Science Review</p><p class="ds-related-work--abstract ds2-5-body-sm">Since introduced by Vining and Myers in 1990, the concept of dual response approach based on response surface methodology has widely been investigated and adopted for the purpose of robust design. Separately estimating mean and variance responses, dual response approach may take advantages of optimization modeling for finding optimum settings of input factors. Explicitly assuming functional relationship between responses and input factors, however, it may not work well enough especially when the behavior of responses are poorly represented. A sufficient number of experimentations are required to improve the precision of estimations. This study proposes an alternative to dual response approach in which additional experiments are not required. An artificial neural network has been applied to model relationships between responses and input factors. Mean and variance responses correspond to output nodes while input factors are used for input nodes. Training, validating, and testing a neural network with empirical process data, an artificial data based on the neural network may be generated and used to estimate response functions without performing real experimentations. A drug formulation example from pharmaceutical industry has been investigated to demonstrate the procedures and applicability of the proposed approach.</p><div class="ds-related-work--ctas"><button class="ds2-5-text-link ds2-5-text-link--inline js-swp-download-button" data-signup-modal="{"location":"wsj-grid-card-download-pdf-modal","work_title":"Robust Parameter Design Based on Back Propagation Neural Network","attachmentId":67752585,"attachmentType":"pdf","work_url":"https://www.academia.edu/49383606/Robust_Parameter_Design_Based_on_Back_Propagation_Neural_Network","alternativeTracking":true}"><span class="material-symbols-outlined" style="font-size: 18px" translate="no">download</span><span class="ds2-5-text-link__content">Download free PDF</span></button><a class="ds2-5-text-link ds2-5-text-link--inline js-wsj-grid-card-view-pdf" href="https://www.academia.edu/49383606/Robust_Parameter_Design_Based_on_Back_Propagation_Neural_Network"><span class="ds2-5-text-link__content">View PDF</span><span class="material-symbols-outlined" style="font-size: 18px" translate="no">chevron_right</span></a></div></div><div class="ds-related-work--container js-wsj-grid-card" data-collection-position="5" data-entity-id="78074253" data-sort-order="default"><a class="ds-related-work--title js-wsj-grid-card-title ds2-5-body-md ds2-5-body-link" href="https://www.academia.edu/78074253/Improving_Quality_by_Optimizing_the_Controllable_Parameters_of_Industrial_Processes_Using_the_Design_of_Experiments_Method">Improving Quality by Optimizing the Controllable Parameters of Industrial Processes Using the Design of Experiments Method</a><div class="ds-related-work--metadata"><a class="js-wsj-grid-card-author ds2-5-body-sm ds2-5-body-link" data-author-id="20765653" href="https://independent.academia.edu/ZinebAman">Zineb Aman</a></div><p class="ds-related-work--metadata ds2-5-body-xs">2021</p><p class="ds-related-work--abstract ds2-5-body-sm">In the last few decades, quality improvement has evolved a lot. It has gone from temporary and limited measures concerning specific aspects of production to a general approach aimed at continuously mobilizing employees around objectives that affect the whole company. The improvement in quality results in innovative modifications in various fields such as the reduction or elimination of the number of faults in the good or service delivered, the reduction of waste (idle time, unnecessary travel, materials, etc..) and increasing the efficiency of work processes. The present research aims to present a study in order to improve quality by optimizing the controllable parameters of industrial processes using the design of experiments method (DoE). Our case study concerns the optimization of the parameters affecting the strength of drawn steel wires using response surface design. We proceed by the screening study after presenting the parameters. Screening study is implemented to eliminate n...</p><div class="ds-related-work--ctas"><button class="ds2-5-text-link ds2-5-text-link--inline js-swp-download-button" data-signup-modal="{"location":"wsj-grid-card-download-pdf-modal","work_title":"Improving Quality by Optimizing the Controllable Parameters of Industrial Processes Using the Design of Experiments Method","attachmentId":85247934,"attachmentType":"pdf","work_url":"https://www.academia.edu/78074253/Improving_Quality_by_Optimizing_the_Controllable_Parameters_of_Industrial_Processes_Using_the_Design_of_Experiments_Method","alternativeTracking":true}"><span class="material-symbols-outlined" style="font-size: 18px" translate="no">download</span><span class="ds2-5-text-link__content">Download free PDF</span></button><a class="ds2-5-text-link ds2-5-text-link--inline js-wsj-grid-card-view-pdf" href="https://www.academia.edu/78074253/Improving_Quality_by_Optimizing_the_Controllable_Parameters_of_Industrial_Processes_Using_the_Design_of_Experiments_Method"><span class="ds2-5-text-link__content">View PDF</span><span class="material-symbols-outlined" style="font-size: 18px" translate="no">chevron_right</span></a></div></div><div class="ds-related-work--container js-wsj-grid-card" data-collection-position="6" data-entity-id="2235896" data-sort-order="default"><a class="ds-related-work--title js-wsj-grid-card-title ds2-5-body-md ds2-5-body-link" href="https://www.academia.edu/2235896/OPTIMIZING_CHEMICAL_PROCESS_THROUGH_ROBUST_TAGUCHI_DESIGN_A_CASE_STUDY">OPTIMIZING CHEMICAL PROCESS THROUGH ROBUST TAGUCHI DESIGN: A CASE STUDY</a><div class="ds-related-work--metadata"><a class="js-wsj-grid-card-author ds2-5-body-sm ds2-5-body-link" data-author-id="2604173" href="https://iimv.academia.edu/vishalpatyal">vishal patyal</a></div><p class="ds-related-work--metadata ds2-5-body-xs">International Journal of Mechanical Engineering and technology, 2012</p><p class="ds-related-work--abstract ds2-5-body-sm">The aim of this study is to design process optimization for chemical process through robust Taguchi design to identify the best parameter setting for purity maximization of chemical 'X'.</p><div class="ds-related-work--ctas"><button class="ds2-5-text-link ds2-5-text-link--inline js-swp-download-button" data-signup-modal="{"location":"wsj-grid-card-download-pdf-modal","work_title":"OPTIMIZING CHEMICAL PROCESS THROUGH ROBUST TAGUCHI DESIGN: A CASE STUDY","attachmentId":30553017,"attachmentType":"pdf","work_url":"https://www.academia.edu/2235896/OPTIMIZING_CHEMICAL_PROCESS_THROUGH_ROBUST_TAGUCHI_DESIGN_A_CASE_STUDY","alternativeTracking":true}"><span class="material-symbols-outlined" style="font-size: 18px" translate="no">download</span><span class="ds2-5-text-link__content">Download free PDF</span></button><a class="ds2-5-text-link ds2-5-text-link--inline js-wsj-grid-card-view-pdf" href="https://www.academia.edu/2235896/OPTIMIZING_CHEMICAL_PROCESS_THROUGH_ROBUST_TAGUCHI_DESIGN_A_CASE_STUDY"><span class="ds2-5-text-link__content">View PDF</span><span class="material-symbols-outlined" style="font-size: 18px" translate="no">chevron_right</span></a></div></div><div class="ds-related-work--container js-wsj-grid-card" data-collection-position="7" data-entity-id="116675241" data-sort-order="default"><a class="ds-related-work--title js-wsj-grid-card-title ds2-5-body-md ds2-5-body-link" href="https://www.academia.edu/116675241/Analysing_the_effects_of_variability_measure_selection_on_process_and_product_optimisation">Analysing the effects of variability measure selection on process and product optimisation</a><div class="ds-related-work--metadata"><a class="js-wsj-grid-card-author ds2-5-body-sm ds2-5-body-link" data-author-id="41470998" href="https://usma.academia.edu/GregoryBoylan">Gregory Boylan</a></div><p class="ds-related-work--metadata ds2-5-body-xs">International Journal of Quality Engineering and Technology, 2011</p><p class="ds-related-work--abstract ds2-5-body-sm">Since the integration of response surface methods into process robustness studies, many researchers have suggested numerous approaches to further enhance product development. Generally, these robust design methods seek the factor settings that minimise variability and the deviation of the mean from the desired target value. In the absence of a uniform approach to modelling process variability, researchers have typically chosen the standard deviation, variance, or logarithm of the standard deviation. Each measure, however, can produce a different set of optimal factor settings, thus complicating comparison studies. The purpose of this paper is to examine the effects of variability measure selection on solutions and suggest a uniform approach.</p><div class="ds-related-work--ctas"><button class="ds2-5-text-link ds2-5-text-link--inline js-swp-download-button" data-signup-modal="{"location":"wsj-grid-card-download-pdf-modal","work_title":"Analysing the effects of variability measure selection on process and product optimisation","attachmentId":112738252,"attachmentType":"pdf","work_url":"https://www.academia.edu/116675241/Analysing_the_effects_of_variability_measure_selection_on_process_and_product_optimisation","alternativeTracking":true}"><span class="material-symbols-outlined" style="font-size: 18px" translate="no">download</span><span class="ds2-5-text-link__content">Download free PDF</span></button><a class="ds2-5-text-link ds2-5-text-link--inline js-wsj-grid-card-view-pdf" href="https://www.academia.edu/116675241/Analysing_the_effects_of_variability_measure_selection_on_process_and_product_optimisation"><span class="ds2-5-text-link__content">View PDF</span><span class="material-symbols-outlined" style="font-size: 18px" translate="no">chevron_right</span></a></div></div><div class="ds-related-work--container js-wsj-grid-card" data-collection-position="8" data-entity-id="99519353" data-sort-order="default"><a class="ds-related-work--title js-wsj-grid-card-title ds2-5-body-md ds2-5-body-link" href="https://www.academia.edu/99519353/Quality_costs_and_robustness_criteria_in_chemical_process_design_optimization">Quality costs and robustness criteria in chemical process design optimization</a><div class="ds-related-work--metadata"><a class="js-wsj-grid-card-author ds2-5-body-sm ds2-5-body-link" data-author-id="19496463" href="https://independent.academia.edu/FernandoManuelAlmeida">Fernando Manuel Almeida</a></div><p class="ds-related-work--metadata ds2-5-body-xs">Computers &amp; Chemical Engineering, 2001</p><p class="ds-related-work--abstract ds2-5-body-sm">The identification and incorporation of quality costs and robustness criteria is becoming a critical issue while addressing chemical process design problems under uncertainty. This article presents a systematic design framework that includes Taguchi loss functions and other robustness criteria within a single-level stochastic optimization formulation, with expected values in the presence of uncertainty being estimated by an efficient cubature technique. The solution obtained defines an optimal design, together with a robust operating policy that maximizes average process performance. Two process engineering examples (synthesis and design of a separation system and design of a reactor and heat exchanger plant) illustrate the potential of the proposed design framework. Different quality cost models and robustness criteria are considered, and their influence in the nature and location of best designs systematically studied. This analysis reinforces the need for carefully considering/addressing process quality and robustness related criteria while performing chemical process plant design.</p><div class="ds-related-work--ctas"><button class="ds2-5-text-link ds2-5-text-link--inline js-swp-download-button" data-signup-modal="{"location":"wsj-grid-card-download-pdf-modal","work_title":"Quality costs and robustness criteria in chemical process design optimization","attachmentId":100586017,"attachmentType":"pdf","work_url":"https://www.academia.edu/99519353/Quality_costs_and_robustness_criteria_in_chemical_process_design_optimization","alternativeTracking":true}"><span class="material-symbols-outlined" style="font-size: 18px" translate="no">download</span><span class="ds2-5-text-link__content">Download free PDF</span></button><a class="ds2-5-text-link ds2-5-text-link--inline js-wsj-grid-card-view-pdf" href="https://www.academia.edu/99519353/Quality_costs_and_robustness_criteria_in_chemical_process_design_optimization"><span class="ds2-5-text-link__content">View PDF</span><span class="material-symbols-outlined" style="font-size: 18px" translate="no">chevron_right</span></a></div></div><div class="ds-related-work--container js-wsj-grid-card" data-collection-position="9" data-entity-id="98809723" data-sort-order="default"><a class="ds-related-work--title js-wsj-grid-card-title ds2-5-body-md ds2-5-body-link" href="https://www.academia.edu/98809723/Development_of_Dual_Response_Approach_using_Artificial_Intelligence_for_Robust_Parameter_Design">Development of Dual Response Approach using Artificial Intelligence for Robust Parameter Design</a><div class="ds-related-work--metadata"><a class="js-wsj-grid-card-author ds2-5-body-sm ds2-5-body-link" data-author-id="46964533" href="https://independent.academia.edu/BennyMaluegha">Benny Maluegha</a></div><p class="ds-related-work--metadata ds2-5-body-xs">2018</p><p class="ds-related-work--abstract ds2-5-body-sm">Prediction process of parameters in robust design is very important. If the prediction results is fairly precise then the quality improvement process will economize time and reduce cost. Dual response approach based on response surface methodology has widely investigated. Separately estimating mean and variance responses, dual response approach may take advantages of optimization modeling for finding optimum setting of input factors. A sufficient number of experimentations are required to improve the precision of estimations. This research recommended an alternative dual response approach without performing experiments. A hybrid neural network-genetic algorithm has been applied to model relationships between responses and input factors. Mean and variance responses conform to output nodes while input factors are used for input nodes. Using empirical process data, process parameter can be predicted without performing real experimentations. A genetic algorithm has been applied to obtai...</p><div class="ds-related-work--ctas"><button class="ds2-5-text-link ds2-5-text-link--inline js-swp-download-button" data-signup-modal="{"location":"wsj-grid-card-download-pdf-modal","work_title":"Development of Dual Response Approach using Artificial Intelligence for Robust Parameter Design","attachmentId":100060676,"attachmentType":"pdf","work_url":"https://www.academia.edu/98809723/Development_of_Dual_Response_Approach_using_Artificial_Intelligence_for_Robust_Parameter_Design","alternativeTracking":true}"><span class="material-symbols-outlined" style="font-size: 18px" translate="no">download</span><span class="ds2-5-text-link__content">Download free PDF</span></button><a class="ds2-5-text-link ds2-5-text-link--inline js-wsj-grid-card-view-pdf" href="https://www.academia.edu/98809723/Development_of_Dual_Response_Approach_using_Artificial_Intelligence_for_Robust_Parameter_Design"><span class="ds2-5-text-link__content">View PDF</span><span class="material-symbols-outlined" style="font-size: 18px" translate="no">chevron_right</span></a></div></div></div></div><div class="ds-sticky-ctas--wrapper js-loswp-sticky-ctas hidden"><div class="ds-sticky-ctas--grid-container"><div class="ds-sticky-ctas--container"><button class="ds2-5-button js-swp-download-button" data-signup-modal="{"location":"continue-reading-button--sticky-ctas","attachmentId":28593425,"attachmentType":"pdf","workUrl":null}">See full PDF</button><button class="ds2-5-button ds2-5-button--secondary js-swp-download-button" data-signup-modal="{"location":"download-pdf-button--sticky-ctas","attachmentId":28593425,"attachmentType":"pdf","workUrl":null}"><span class="material-symbols-outlined" style="font-size: 20px" translate="no">download</span>Download PDF</button></div></div></div><div class="ds-below-fold--grid-container"><div class="ds-work--container js-loswp-embedded-document"><div class="attachment_preview" data-attachment="Attachment_28593425" style="display: none"><div class="js-scribd-document-container"><div class="scribd--document-loading js-scribd-document-loader" style="display: block;"><img alt="Loading..." src="//a.academia-assets.com/images/loaders/paper-load.gif" /><p>Loading Preview</p></div></div><div style="text-align: center;"><div class="scribd--no-preview-alert js-preview-unavailable"><p>Sorry, preview is currently unavailable. 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international, 2000</p><div class="ds-related-work--ctas"><button class="ds2-5-text-link ds2-5-text-link--inline js-swp-download-button" data-signup-modal="{"location":"wsj-grid-card-download-pdf-modal","work_title":"Multi‐response optimization in industrial experiments using Taguchi's quality loss function and principal component analysis","attachmentId":46569004,"attachmentType":"pdf","work_url":"https://www.academia.edu/11699967/Multi_response_optimization_in_industrial_experiments_using_Taguchis_quality_loss_function_and_principal_component_analysis","alternativeTracking":true}"><span class="material-symbols-outlined" style="font-size: 18px" translate="no">download</span><span class="ds2-5-text-link__content">Download free PDF</span></button><a class="ds2-5-text-link ds2-5-text-link--inline js-related-work-grid-card-view-pdf" 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