Reducing the quantity of reworked parts in a robotic arc welding process

Today, the competitive environment of the knowledge age has been replaced with the competitive environment of the industrial age as the rules of the business world are changing completely. The focused issue is to reach to the knowledge as soon as possible, and to process and apply it rapidly. In this context, one of the important goals that the companies try to reach in order to achieve acceleration is the six sigma philosophy. Six sigma is a fundamental continuous improvement methodology aimed at reducing the variation and waste on processes by utilizing statistical methods and techniques efficiently. Global companies are making noteworthy profits by using this method in their processes. The six sigma methodology focuses on process excellence advantages for companies that apply it, to reach profit, to increase productivity and to have larger market share. In this paper, an application of six sigma methodology for reducing the quantity of rework parts for robotic arc welding process is given. The phases of six sigma and their results are indicated in detail. Furthermore, it is also shown how various techniques of six sigma methodology are applied to achieve financial benefits. Copyright © 2008 John Wiley & Sons, Ltd.     

Parametric and non-parametric modelling of earing and hardness of deep drawn cups

Abstract

This study compares different approaches in modelling the earing phenomenon and hardness of cups in deep drawing process. The blank holder force (BHF), annealing temperature and annealing time of blanks before deep drawing process have been chosen as the three influential parameters on the earing and hardness. To obtain mathematical models for the earing and hardness of the deep drawn cups, the methodology of artificial neural networks have been used. Bayesian network, radial basis function network, Gaussian processes and multilayer perceptron are four different ANN approaches that have been used for the modelling. The research has been conducted on a cold rolled Al–Fe–Si (AA8011A) aluminium sheet. After obtaining the mathematical models describing the influence of BHF and annealing on hardness and earing, a comparison of the proposed models has been performed. A search for the optimal parameters of deep drawing process has been carried out.     

Six sigma,absorptive capacity and organisational learning orientation

The importance of the six sigma methodology in industry is growing constantly. However, there are few empirical studies that analyse the advantages of this methodology and its positive effects on organisational performance. The purpose of this paper is to extend understanding of the success of six sigma quality management initiatives by investigating the effects of six sigma teamwork and process management on absorptive capacity. It also seeks to understand the relation between absorptive capacity and organisational learning as two sources of sustainable competitive advantage. The information used comes from a larger study, the data for which was collected from a random sample of 237 European firms. Of these 237 organisations, 58 are six sigma organisations. Structural equation modelling (SEM) was used to test the hypotheses. The main findings show that six sigma teamwork and process management positively affect the development of absorptive capacity. A positive and significant relationship is also observed between absorptive capacity and organisational learning orientation. The findings of this study justify six sigma implementation in firms. This study provides us with an in-depth understanding of some structural elements that characterise the six sigma methodology, enabling us to provide an explanation for its success.     

6σ方法的选择与分析

6σ改进主要应用于现有的过程或产品, 而6σ设计主要应用于设计或重新设计过程或产品;何时优先采用6σ设计方法一直是一个备受争议的问题.为解决这一问题,比较了6σ改进和6σ设计方法的差异;进而从系统论的观点,分析了选择6σ方法时需要考虑的要素并建立了相应的指标体系;在此基础上,引入了多目标决策的层次分析方法,并对一个6σ项目进行了分析.结果表明,选择6σ方法受到许多因素的影响,从而替代了6σ文献中"只有达到4.8σ,才能实施6σ设计"的准则.结论是任何实施6σ管理的组织可以根据技术、顾客需求、成本、复杂性、风险等因素,选择适应于自身发展的6σ方法.     

Multi-objective Optimization of Sheet Metal Forming Die Using Genetic Algorithm Coupled with RSM and FEA

Present study describes the approach of applying response surface methodology (RSM) with a Pareto-based multi-objective genetic algorithm to assist engineers in optimization of sheet metal forming. In many studies, finite element analysis and optimization technique have been integrated to solve the optimal process parameters of sheet metal forming by transforming multi-objective problem into a single-objective problem. This paper aims to minimize objective functions of fracture and wrinkle simultaneously. Design variables are blank-holding force and draw-bead geometrical parameters (length and diameter). RSM has been used for design of experiment and finding relationship between variables and objective functions. Forming limit diagram has been used to define objective functions. Finite element analysis applied for simulating the process. Proposed approach has been investigated on a fuel tank drawing part and it has been observed that it is more effective and accurate than traditional finite element analysis method and the “trial and error” procedure.     

Application of six sigma methodology to reduce defects of a grinding process

Six Sigma is a data‐driven leadership approach using specific tools and methodologies that lead to fact‐based decision making. This paper deals with the application of the Six Sigma methodology in reducing defects in a fine grinding process of an automotive company in India. The DMAIC (Define–Measure–Analyse–Improve–Control) approach has been followed here to solve the underlying problem of reducing process variation and improving the process yield. This paper explores how a manufacturing process can use a systematic methodology to move towards world‐class quality level. The application of the Six Sigma methodology resulted in reduction of defects in the fine grinding process from 16.6 to 1.19%. The DMAIC methodology has had a significant financial impact on the profitability of the company in terms of reduction in scrap cost, man‐hour saving on rework and increased output. A saving of approximately US$2.4 million per annum was reported from this project. Copyright © 2011 John Wiley & Sons, Ltd.     

Forming Process of Strong Anisotropic Material Based on the Hydrodynamic Deep Drawing with Radial Pressure

1.IntroductionA sketch of the conventional hydrodynamic deepdrawing(HDD)process is shown in Fig.1(a)[1~5].Ini-tially,the flange of the blank is in contact with the drawdie.When the punch is moved down,the liquid pres-sure in the die cavity increases and when the pressurereaches a certain magnitude,the flange is lifted from thedraw die and the liquid starts to flow out through thegap between the draw die and the flange[6~9].In FEMsimulation of the conventional HDD,it is very difficult todet…     

An alternative desirability function for achieving ‘six sigma’ quality

To achieve high process yields or ‘six sigma’ quality, engineers often need to evaluate and optimize processes that are characterized by multiple quality characteristics. Existing desirability functions weigh together multiple objectives but they have a number of limitations. Most importantly, available desirability functions do not explicitly account for the combined effect of the mean and the dispersion of the quality characteristic. Therefore, it is easy to incur excessive expenditures or unknowingly to fail to achieve targeted yields. In this paper, a desirability function is proposed that addresses these limitations. This function conservatively estimates the ‘effective yield’ under assumptions described in the ‘six sigma’ literature. We use an arc‐welding application to illustrate how the proposed desirability function can yield a substantially higher level of quality as well as a more accurate assessment of the process capability than available alternatives. We suggest that the proposed desirability function should be used to facilitate multicriterion optimization when dispersion data are available. Copyright © 2003 John Wiley & Sons, Ltd.     

Experimental validation of numerical sensitivities in a deep drawing simulation

Deep drawing of a benchmark B-pillar is numerically modelled and experimentally performed with varying blankholder force and several blank shape parameters. The most influential parameters are selected for optimisation. Direct application of Autoform sigma software was used to determine sensitivities, as well as indirect application using response surfaces. Interesting nonlinear sensitivities were found that will be missed with simple linear screening techniques.     

Analytical and finite element simulation studies on earing profile of Ti-6Al-4V deep drawn cups at elevated temperatures

Rolled sheet metal alloys exhibit plastic anisotropy, which leads to the formation of ears during the deep drawing process. An analytical function proposed by Yoon et al. (Int J Plast 27(8):1165–1184, 2011) predicts earing profile based on yield stress and r value directionalities for circular cup drawing. In this study, this analytical approach is applied for a deep drawing of Ti-6Al-4V at elevated temperatures up to 400 °C. Three yield criteria namely, Hill 1948, Barlat 1989 and Barlat Yld2000-2d are used to obtain the directionality inputs for the analytical formula. The analytical model is validated using experimental results and FE simulations and is found to be closely matched while requiring very less CPU time. FE simulation has been also conducted with various yield functions. Barlat Yld2000-2d is considered to be the most suitable yield criterion for very accurate earing prediction in deep drawing of Ti-6Al-4V as the inputs for both the analytical and FEM models.     

面向产品生命周期的全面质量管理

综述了全面质量管理所面临的挑战,提出了在新形势下整合TQM运作模式的必要性,构建了融合标准化、定量化、持续改进与追求突变等先进管理思想的TQM运作模式的总体框架,并从活动单元、管理模式和过程方式等不同的角度对其进行了详细的分析与讨论.     

Unification of robust design and goal programming for multiresponse optimization—a case study

Fixing the levels of input process parameters to meet a required specification of output is a common process quality control problem. Especially when the output has many quality characteristics, and each of these quality characteristics has to satisfy a given specification, difficulties may arise. One such problem was encountered in an injection moulding process. This process was optimized using Taguchi's Robust Design methodology. Details of the process, problems encountered and outcome of optimization are presented in this paper. The optimization study using Taguchi's methodology revealed that the optimum conditions obtained for one response are not completely compatible with those of other responses. So trade-offs were made in selection of levels for factors using engineering judgement. This increases the uncertainty in the decision making process. In this paper, an approach is presented to optimize multiresponses simultaneously using goal programming in conjunction with Taguchi's methodology. Details of modelling, analysis and inferences obtained with relevance to the case are presented. This study revealed that the optimum conditions obtained using goal programming in conjuction with Taguchi's methodology have better goal attainment properties compared to Robust design. To understand goal attainment behaviour of output characteristics for various process conditions, a detailed sensitivity analysis was also conducted. The outcome of this analysis is also discussed in this paper. © 1997 John Wiley & Sons, Ltd.     

Numerical assessment of springback for the deep drawing process by level set interpolation using shape manifolds

In this paper, we introduce an original shape representation approach for post-springback characterization based on the automatic generation of parameterized level set functions. The central idea is the concept of the shape manifold representing the design domain in the reduced-order shape-space. Performing Proper Orthogonal Decomposition on the shapes followed by using the Diffuse Approximation allows us to efficiently reduce the problem dimensionality and to interpolate uniquely between admissible input shapes, while also determining the smallest number of parameters needed to characterize the final formed shape. We apply this methodology to the problem of springback assessment for the deep drawing operation of metal sheets.     

Forming sheet metals by means of multi-point deep drawing method

Multi-point deep drawing (MPDD) is an advanced manufacturing technology for 3D sheet metal parts and it can form a variety of part shapes without the need for solid dies. In this study, a test set has been prepared for multi-point deep drawing process utilizing the multi-point forming technology. Drawability attributes of gradually rectangular shaped container have been observed using a sheet, which has the quality of Erdemir 7114 and is suitable for deep drawing process, and also using multi-pointed punch with a given tool geometry and a draw velocity. The blank shape to be drawn without wrinkling and tearing has been determined. Wrinkles and dimples are the major forming defects in the MPDD process. In conventional deep drawing, the method to form sheet metal with a blank holder is an effective way to suppress wrinkling; and the same is true in MPDD. The process of multi-point forming technology decreases production cost of die, provides flexible usage, and it is convenient to achieve the most even deformation distribution.     

Robust optimisation of Nd: YLF laser beam micro-drilling process using Bayesian probabilistic approach

Nd: YLF laser beam machining (LBM) process has a great potential to manufacture intricate shaped microproducts with its unique characteristics. Continuous improvement (CI) effort for LBM process is usually realised by response surface methodology, which is an important tool in Design of Six Sigma. However, when determining the optimal machining parameters in CI for LBM process, model parameter uncertainty is typically neglected. Performing worst case analysis in CI, this paper presents a new loss function method that takes model parameter uncertainty into account via Bayesian credible region. Unlike existing CI methods in LBM process, the proposed Bayesian probabilistic approach is based on seemingly unrelated regression which can produce more precise estimations of the model parameters than ordinary least squares in correlated multiple responses problems. An Nd: YLF laser beam micro-drilling process is used to demonstrate the effectiveness of the proposed approach. The comparison results show that micro-holes produced by the proposed approach have better quality than those of existing approaches in terms of robustness and process capability.     

Probabilistic design of aluminum sheet drawing for reduced risk of wrinkling and fracture

Often, sheet drawing processes are designed to provide the geometry of the final part, and then the process parameters such as blank dimensions, blank holder forces (BHFs), press strokes and interface friction are designed and controlled to provide the greatest drawability (largest depth of draw without violating the wrinkling and thinning constraints). The exclusion of inherent process variations in this design can often lead to process designs that are unreliable and uncontrollable. In this paper, a general multi-criteria design approach is presented to quantify the uncertainties and to incorporate them into the response surface method (RSM) based model so as to conduct probabilistic optimization. A surrogate RSM model of the process mechanics is generated using FEM-based high-fidelity models and design of experiments (DOEs), and a simple linear weighted approach is used to formulate the objective function or the quality index (QI). To demonstrate this approach, deep drawing of an aluminum Hishida part is analyzed. With the predetermined blank shape, tooling design and fixed drawing depth, a probabilistic design (PD) is successfully carried out to find the optimal combination of BHF and friction coefficient under variation of material properties. The results show that with the probabilistic approach, the QI improved by 42% over the traditional deterministic design (DD). It also shows that by further reducing the variation of friction coefficient to 2%, the QI will improve further to 98.97%.     

Experimental and numerical investigation of anisotropic yield criteria for warm deep drawing of Ti–6Al–4V alloy

Accuracy of the finite element simulation of sheet metal forming is significantly dependent on the correctness of input properties and appropriate selection of material models. In this work, anisotropic yield criteria namely, Hill 1948, Barlat 1989, Barlat 1996, Barlat 2000 and Cazacu Barlat have been implemented for Ti–6Al–4V alloy at 400 °C. Material constants required for the yield criteria have been determined and deformation behavior in deep drawing process has been analyzed in finite element software. Also, deep drawing experiments on Ti–6Al–4V alloy have been performed at 400 °C to validate finite element simulation results. Further, comparison of yield criteria based on thickness distribution, earing profile, complexity in material parameter identification and computational time has shown Cazacu-Barlat to be well suited for deep drawing of Ti–6Al–4V alloy.     

Optimization of multiple responses using a fuzzy-rule based inference system

The optimization of multiple responses (or performance characteristics) has received increasing attention over the last few years in many manufacturing organizations. Many Taguchi practitioners have employed past experience and engineering knowledge or judgement when dealing with multiple responses. This approach brings an element of uncertainty to the decision-making process and therefore is not recommended for optimization of multiple responses. The approach presented in this paper takes advantage of both the Taguchi method and a fuzzy-rule based inference system, which forms a robust and practical methodology in tackling multiple response optimization problems. The paper also presents a case study to illustrate the potential of this powerful integrated approach for tackling multiple response optimization problems. The variance analysis is also an integral part of the study, which identifies the most critical and statistically significant parameters.     

Optimization of aluminium sheet hot stamping process using a multi-objective stochastic approach

This article aims to investigate the means to obtain optimal hot stamping process parameters and the influence of the stochastic variability of these parameters on forming quality. A multi-objective stochastic approach, integrating response surface methodology (RSM), multi-objective genetic algorithm optimization non-dominated sorting genetic algorithm II (NSGA-II) and the Monte Carlo simulation (MCS) method is proposed in this article to achieve this goal. RSM was used to establish the relationship between the process parameters and forming quality indices. NSGA-II was utilized to obtain a Pareto frontier, which consists of a series of optimal process parameters. The MCS method was employed to study and reduce the influence of a stochastic property of these process parameters on forming quality. The results confirmed the efficiency of the proposed multi-objective stochastic approach during optimization of the hot stamping process. Robust optimal process parameters guaranteeing good forming quality were also obtained using this approach.     

Statistical Control of a Six Sigma Process

Six Sigma as a methodology for quality improvement is often presented and deployed in terms of the dpmo metric, i.e., defects per million opportunities. As the sigma level of a process improves beyond three, practical interpretation problems could arise when conventional Shewhart control charts are applied during the Control phase of the define-measure-analyze-improve-control framework. In this article, some alternative techniques are described for the monitoring and control of a process that has been successfully improved; the techniques are particularly useful to Six Sigma Black Belts in dealing with high-quality processes. The approach used would thus ensure a smooth transition from a low-sigma process management to maintenance of a high-sigma performance in the closing phase of a Six Sigma project.