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Integral aided method for material selection based on quality function deployment and comprehensive VIKOR algorithm
Materials & Design v47, May 2013; Carlo Cavallinia, Alessandro Giorgettia, Paolo Cittia, Francois Nicolaieb
In engineering design, the selection of material alternatives usually depends of different criteria based on the specific problem. Due to the different units of this criteria, a normalization process is needed in the selection model. A lot of normalization approach can be found in literature and at the same time many algorithms have been developed to ensure the optimal material selection for a certain industrial application. Two elements of reflection can be drawn from the analysis of these. The first is the absence of an aided support to the selection of the correct engineering criteria by whom operate the selection process. The second is the need to define a weighting method that at the same time can be user-friendly to use and representative of the project’s needs. A new selection model based on the integration between House of Quality and the Comprehensive Vikor Algorithm is presented in this paper. This approach, called Integral Aided Material Selection (IAMS), can overcome the main lack of traditional material selection model and provide a real support tool to the project team. That way the project team can optimally choose the selection criteria and assign to these the correct priority coherently with the project needs. A case study is presented to illustrate and justify the proposed method. The topic of the case study concerns the identification of the best coating for the protection of an aluminum alloy substrate (Al-7075) from the effects of abrasive wear against an alternating counterpart made by a high-strength cast iron. The study focused on the process of material selection in a typical industrial context. A supported model to the project team is needed when there are no experts in material selection. Weighting method for the engineering characteristics must be chosen correctly. More friendly multi-attribute decision-making with target-based criteria.
Experimental Design for Engineering Dimensional Analysis
Technometrics, Mark C. Albrechta, Christopher J. Nachtsheimb, Thomas A. Albrechtc & R. Dennis Coo
Dimensional Analysis (DA) is a fundamental method in the engineering and physical sciences for analytically reducing the number of experimental variables affecting a given phenomenon prior to experimentation. Two powerful advantages associated with the method, relative to standard design of experiment (DOE) approaches are: (1) a priori dimension reduction, (2) scalability of results. The latter advantage permits the experimenter to effectively extrapolate results to similar experimental systems of differing scale. Unfortunately, DA experiments are underutilized because very few statisticians are familiar with them. In this paper, we first provide an overview of DA and give basic recommendations for designing DA experiments. Next we consider various risks associated with the DA approach, the foremost among them is the possibility that the analyst might omit a key explanatory variable, leading to an incorrect DA model. When this happens, the DA model will fail and experimentation will be largely wasted. To protect against this possibility, we develop a robust-DA design approach, that combines the best of the standard empirical DOE approach with our suggested design strategy. Results are illustrated with some straightforward applications of DA. A Matlab code for computing robust-DA designs is available as supplementary material online.
Concurrent treatment of parametric uncertainty and metamodeling uncertainty in robust design
Structural and Multidisciplinary Optimization Volume 47, Number 1 (January 2013) , Siliang Zhang, Ping Zhu, Wei Chen, Paul Arend
Robust design is an effective approach to design under uncertainty. Many works exist on mitigating the influence of parametric uncertainty associated with design or noise variables. However, simulation models are often computationally expensive and need to be replaced by metamodels created using limited samples. This introduces the so-called metamodeling uncertainty. Previous metamodel-based robust designs often treat a metamodel as the real model and ignore the influence of metamodeling uncertainty. In this study, we introduce a new uncertainty quantification method to evaluate the compound effect of both parametric uncertainty and metamodeling uncertainty. Then the new uncertainty quantification method is used for robust design. Simplified expressions of the response mean and variance is derived for a Kriging metamodel. Furthermore, the concept of robust design is extended for metamodel-based robust design accounting for both sources of uncertainty. To validate the benefits of our method, two mathematical examples without constraints are first illustrated. Results show that a robust design solution can be misleading without considering the metamodeling uncertainty. The proposed uncertainty quantification method for robust design is shown to be effective in mitigating the effect of metamodeling uncertainty, and the obtained solution is found to be more “robust” compared to the conventional approach. An automotive crashworthiness example, a highly expensive and non-linear problem, is used to illustrate the benefits of considering both sources of uncertainty in robust design with constraints. Results indicate that the proposed method can reduce the risk of constraint violation due to metamodel uncertainty and results in a “safer” robust solution.Robust design is an effective approach to design under uncertainty. Many works exist on mitigating the influence of parametric uncertainty associated with design or noise variables. However, simulation models are often computationally expensive and need to be replaced by metamodels created using limited samples. This introduces the so-called metamodeling uncertainty. Previous metamodel-based robust designs often treat a metamodel as the real model and ignore the influence of metamodeling uncertainty. In this study, we introduce a new uncertainty quantification method to evaluate the compound effect of both parametric uncertainty and metamodeling uncertainty. Then the new uncertainty quantification method is used for robust design. Simplified expressions of the response mean and variance is derived for a Kriging metamodel. Furthermore, the concept of robust design is extended for metamodel-based robust design accounting for both sources of uncertainty. To validate the benefits of our method, two mathematical examples without constraints are first illustrated. Results show that a robust design solution can be misleading without considering the metamodeling uncertainty. The proposed uncertainty quantification method for robust design is shown to be effective in mitigating the effect of metamodeling uncertainty, and the obtained solution is found to be more “robust” compared to the conventional approach. An automotive crashworthiness example, a highly expensive and non-linear problem, is used to illustrate the benefits of considering both sources of uncertainty in robust design with constraints. Results indicate that the proposed method can reduce the risk of constraint violation due to metamodel uncertainty and results in a “safer” robust solution.
How to turn data into a strategic asset
Over the past decade, companies across a number of industries have gone even further. Netflix, Capital One, Amazon.com, Tesco and Progressive Casualty Insurance Co., just to name a few, have learned how to win with analytics - that is, how to use data and sophisticated quantitative models as strategic tools in their efforts to achieve high performance.
Engineering talent for a new era
Accenture
Visionary companies are working today to define the next generation of engineering innovations, and outsourcing will play a critical role. Outsourcing gives companies scalable, flexible access to engineering talent across geographies - professionals with deep domain experience, local language skills and an understanding of specific market needs.
Policies concerning decisions related to quality level
International Journal of Production Economics, vol 125, issue 1, p. 146-152, May 2010 Sanjay Sharma
In a production environment, quality levels vary owing to various operational constraints. In an operational environment, the total relevant cost includes (i) production time cost, (ii) manufacturing facility setup cost and (iii) inventory carrying cost. Production time cost relates to the time for which a manufacturing facility runs. Setup cost is concerned with the expenditure incurred in arranging a facility to produce certain item. This is irrespective of the production quantity in one setup. Inventory carrying cost is estimated on the basis of an average inventory held in the system. Additionally a shortage cost is also incorporated in cases where the shortages are allowed. Variation in the quality level will affect the costs. Sometimes a decision has to be taken by either manufacturer or supplier to allow for high quality defects. These should be on the basis of certain principles/policies. Modifications are often made in the design of products so that the quality problems are less. Such design/development project costs the company and a decision is to be taken whether it is justified. The present paper develops certain policies which will be useful in supporting the decisions related to quality level. A strategic framework concerning supplier categorization/ selection is also discussed. Few relevant propositions are further developed after providing a basic structure for their generation.
Failure analysis of an automobile damper spring tower
Engineering Failure Analysis, Volume 17, Issue 2, p. 498-505, March 2010 Bai-yan He, Shu-xin Wang, Feng Gao
The cause of a passenger car’s damper spring tower early failure is investigated in this paper. Inspection of the road surface, tire inflation pressure, suspension, and service load are firstly done in order to determine the further test procedures and analysis methods. The static stress of the spring tower caused by the body weight is calculated by finite element model. Public road tests with an equipped car are carried out to simulate the real usage by the customers. With the measured strain signals of different test conditions and local strain–life method, fatigue life prediction is made. The calculated fatigue life coincides with the actual failure mileage, and it turns out that the broken spring damper causes the early failure of the spring tower. It is suggested that more emphasis should be taken on the durability design and test of the spring damper.
 
 
Using Quality Function Deployment and Analytical Hierarchy Process for material selection of Body-In-White
Materials & Design, v32, Issue 5, 2011; Abdelraoof Mayyas, Qin Shen, Ahmad Mayyas, Mahmoud abdelhamid, Dongri Shan, Ala Qattawi, Mohammed Omar
Presented manuscript discusses the usage of multi-attribute decision making tools to assist in the material selection for vehicular structures; mainly the automotive Body-In-White (BiW) panels at the conceptual design stage using Quality Function Deployment (QFD) and Analytical Hierarchy Process (AHP). The main advantage of using QFD and AHP is their abilities to rank choices in the order of their effectiveness in meeting the functional objective. AHP discriminates between competing options where interrelated objectives need to be met; AHP is based on straightforward mathematical formulations. QFD on the other side is a customer focused method that usually starts by collecting customer needs and tries to integrate these needs into the product. In this study, following classes of engineering materials are analyzed; forming grade Bake Harden-able steel (BH), Dual Phase steel (DP), High Strength Low Alloy Steel (HSLA), Martenistic steel, Aluminum 5xxx, 6xxx sheets, Magnesium sheets, Titanium sheets, Carbon Fiber Reinforced Plastic (CFRP) and High Density Polyethylene (HDPE). The presented study showed that the different grades of steel gained the first ranks in the selection process for almost most of the BiW panels; however other alternatives could work in trade-off with cost and manufacturability..
An Optimal Solution Through Lean Manufacturing Using Value Stream Mapping Towards Efficient Production for a World-Class Automobile Brakes Manufacturing Company
2009-28-0054, International Mobility Engineering Congress and Exposition, Chennai, India, 12/13/09 - 12/15/09 Renald, C. J. Thomas; Satheeshkumar, V.; Sathiyamanoj, G. K.; Thiagarajan, M.
Today, numerous companies have a major opportunity to reduce their costs and customer lead time and cycle time through the application of Lean Manufacturing processes. In recent years, almost every manufacturing industry has been trying to get 'lean.' Lean thinking represents a set of principles and techniques for the identification and elimination of wastages in manufacturing and administrative processes. Value Stream Mapping (VSM) which is one of the lean manufacturing approaches has emerged as the preferred way to support and implement the lean approach. VSM is a helpful tool to identify the waste and improvement areas. VSM enables a company to see the entire process in both its current and desired future state, and develop the road map that prioritizes the projects or tasks to bridge the gap between the current state and the future (lean) state. Improvements in processes, facility layouts, and managerial spans of control encourage redefining an organizations hierarchy and adopting a value stream management system. In this work, the Information flow, Current state, Value added time and Non-value added time across a leading automobile brakes manufacturing company and its Subcontractors have been studied to achieve value stream management or lean organizational structures. A future state has been proposed in which the reduction of raw material inventory, WIP inventory, finished goods inventory and Lead Time. This paper describes the value stream analysis from current state mapping to future state mapping including the lean concepts, metrics and methods that provide improvement. Eventually the current state and future state have been compared to propose an optimal manufacturing system.
RTR's Involvement in Continuous Improvement of Quality and Reliability of Renault Group's Vehicles
Fuel Economy, Safety and Reliability of Motor Vehicles (ESFA 2009)
As the biggest Renault engineering centre outside France, RTR contributes actively in the quality and reliability continuous improvement of Renault Group’s vehicles. The present document shows one of the most recent actions implemented by RTR and applied in several commercial launching operations. Representative sets of vehicles are chosen on a statistical basis and are tested following confirmed running procedures. The operation is implemented just after the production approval milestone, so the test concerns serial vehicles with the final definition for clients (no prototypes or pre-serial vehicles). During the tests, all the information resulted is analyzed and the client effects solved, in order to anticipate the manifestation of problems during the launching operation and confirm the reliability level of the new vehicle.
About A Mathematical Model Development for the Quality Costs Evaluation in the Automotive Manufacturing
Fuel Economy, Safety and Reliability of Motor Vehicles (ESFA 2009)
This paper presents some aspects regarding the evaluation of the quality management in the automotive industry. A practical method for the efficiency evaluation of the quality management systems is the audit of the automotive quality systems. But this audit isn’t a precise process. The method revealed in this paper represents an objectively tool in this process. The main principle of this method is a systemic approach. This approach identifies the characteristics of the automotive manufacturing process and presents a calculation model for the quality management level.
A Systematic Approach to Sustainable Design of Car Seat Assembly Using the Modified Qualitiy Function Deployment Method
FISITA F2008-10-002
The automotive industry uses approximately 15% of the world´s steel, 40% of the world´s rubber and 25% of the world´s glass, with the consumption of raw materials and other resources further growing due to the rapid development of the automotive sector in China and India. In addition, transportation accounts for around 25% of greenhouse emissions worldwide, whereby 90% of transport related emissions come from road vehicles, predominantly cars. This paper will present a systematic approach to sustainable design of a car seat assembly using the modified Quality Function Deployment method as an integrating medium for stakeholder requirements and technical targets. Car seats represent a critical element of the vehicle system, due to the wide range of functions and requirements that they are required to meet. These include adjustability of driver position, ergonomics/comfort, aesthetics, safety, etc. Additionally, there are increasing demands by OEM´s for higher quality and better performance at reduced cost, weight, time to market etc.
Role of explicit and tacit knowledge in Six Sigma projects: An empirical examination of differential project success
Journal of Operations Management, Volume 28, Issue 4, p. 303-315, July 2010 Gopesh Anand, Peter T. Ward, Mohan V. Tatikonda
This research develops a conceptual model for predicting success of process improvement projects as a result of knowledge-creation practices employed in the projects. The model is empirically examined in the context of Six Sigma black belt projects. New scales are developed to measure explicit- and tacit-knowledge-creation practices in process improvement. Data is gathered via a cross-sectional sample, and the hypotheses are tested using hierarchical regression. Our results support the notion that knowledge-creation practices influence the success of process improvement projects. Specifically, the inclusion of softer, people-oriented practices for capturing tacit knowledge explains a significant amount of variance in project success, as much as the more analytically focused practices that capture explicit knowledge. This research offers practical insights about the influence of practices that project managers use to create new knowledge by capturing explicit and tacit knowledge, and seeks to advance theoretical understanding of process improvement.
Where Process-Improvement Projects Go Wrong
Satya S. Chakravorty The Wall Street Journal January 25, 2010
Many companies have embraced Six Sigma, a quality-control system designed to tackle problems such as production defects, and lean manufacturing, which aims to remove all processes that don't add value to the final product. But many of those companies have come away less than happy. Recent studies, for example, suggest that nearly 60% of all corporate Six Sigma initiatives fail to yield the desire results. We studied process-improvement programs at large companies over a five-year period to gain insight into how and why so many of them fail. We found that when confronted with increasing stress over time, these programs react in much the same way a metal spring does when it is pulled with increasing force—that is, they progress though "stretching" and "yielding" phases before failing entirely. In engineering, this is known as the "stress-strain curve," and the length of each stage varies widely by material. A closer look at the characteristics of improvement projects at each of the three stages of the stress-strain curve - stretching, yielding and failing - offers lessons for executives seeking to avoid Six Sigma failures. The discussion that follows is based on what happened at one aerospace company that implemented more than 100 improvement projects, only to determine less than two years later that more than half had failed to generate lasting gains.
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