The quality of a product is given by the extent, to which the product meets customer requirements. It is generally
accepted that the extent, to which the product meets such customer requirements, and, consequently, the resulting
quality of the product itself, substantially depend on the early stages of the product lifecycle, i.e. on the design and
development stages. Appropriate means for eff ective product quality planning can be found among quality management
methods and tools. These methods are typically employed in engineering production and automotive industry.
This paper focuses on exploring the potential of Quality Function Deployment (QFD) and Failure Mode and
Eff ect Analysis (FMEA) methods for use in metallurgical production, an industrial branch where they have not been
commonly employed as yet.Web of Science52453252

Fabík, Richard

Halfarová, Petra

Klaput, Pavel

Plura, Jiří

Vykydal, David

DSpace at VSB Technical University of Ostrava

529METALURGIJA 52 (2013) 4, 529-532D. VYKYDAL, J. PLURA, P. HALFAROVÁ, R. FABÍK, P. KLAPUTUSE OF QUALITY PLANNING METHODS IN OPTIMIZING WELDING WIRE QUALITY CHARACTERISTICS Received – Prispjelo: 2012-09-12Accepted – Prihvaćeno: 2013-02-20Review Paper – Pregledni radISSN 0543-5846METABK 52(4) 529-532 (2013)UDC – UDK 621.791.052:621.778:658.562.001.1=111D. Vykydal, J. Plura, P. Halfarova, R. Fabík, P. Klaput, VŠB – TU Os-trava, FMME, Ostrava, Czech RepublicThe quality of a product is given by the extent, to which the product meets customer requirements. It is generally accepted that the extent, to which the product meets such customer requirements, and, consequently, the resulting quality of the product itself, substantially depend on the early stages of the product lifecycle, i.e. on the design and development stages. Appropriate means for eff ective product quality planning can be found among quality man-agement methods and tools. These methods are typically employed in engineering production and automotive in-dustry. This paper focuses on exploring the potential of Quality Function Deployment (QFD) and Failure Mode and Eff ect Analysis (FMEA) methods for use in metallurgical production, an industrial branch where they have not been commonly employed as yet.Key words: welding, wire, quality, planningINTRODUCTIONProduct quality is one of the most important factors of organizational competitiveness and sustainable eco-nomic growth. For high quality of products and the re-sulting customer satisfaction to be achieved, a quality management system must be implemented and continu-ally improved by the organization. Customer satisfac-tion is largely dependent on this part of the quality man-agement system, which deals with pre-production stag-es. These consist of quality planning centered on the design and development of the product and product re-alization processes. Product and process quality plan-ning can be performed effectively with the aid of ap-propriate methods and tools. Various methods are used in different stages of product realization. Their close in-teraction is an advantage in this process.EXTENT OF USE OF VARIOUS METHODS AND TOOLS IN INDUSTRYMethods employed in product quality planning in-clude, for instance [1]: QFD (Quality Function Deploy-ment), Design and Process FMEA (Failure Mode and Effect Analysis), process capability analysis, measure-ment system analysis, seven basic and seven new qual-ity management tools, design of experiments and oth-ers. A survey of organizations based in the Czech Re-public was conducted to determine the scope, in which individual methods of this kind are employed. It was found that some quality planning tools and methods are not used to adequate extent in Czech companies. The survey of 230 organizations revealed that with many methods the extent of their use strongly depends on the state of the organization’s quality management system (QMS) certiﬁ cation. Figure 1 shows the use of QFD, Design FMEA and Process FMEA in organizations at various levels of QMS certiﬁ cation. The ﬁ gure suggests that the proportion of organizations that used QFD is very small. On the other hand, FMEA is used by a much larger proportion of organizations. Its dependence on the level of QMS certiﬁ cation is very strong. This re-ﬂ ects the fact that standards of automotive industry, as well as some customers from other branches, strictly re-quire application of this method. Lower using of Design FMEA is a consequence of the fact that a number of suppliers do not develop the products and manufacture them only.Results of the survey and the data on using of the methods are conﬁ rmed in part by the literature search concerning quality planning applications. A number of publications have dealt with QFD and FMEA applica-tions [2-5]. Despite that, QFD is relatively rare in metal-lurgical industry, although some sources mention its use [6]. For instance, statistical process control in metallur-gical production is not unusual. It was explored by Noskievičová [7-8]. At the same time, virtually no pub-lications can be found on deployment of QFD in metal-lurgical industry. The above summary may suggest that QFD is only effective in automotive industry and me-chanical engineering. However, correct understanding and application of this method may greatly beneﬁ t or-ganizations in metallurgical industry.In order to prove this statement, QFD was employed by an organization operating in metallurgical industry for identifying and optimizing quality characteristics of cold-drawn welding wire. 530  METALURGIJA 52 (2013) 4, 529-532D. VYKYDAL et al.: USE OF QUALITY PLANNING METHODS IN OPTIMIZING WELDING WIRE QUALITY...QUALITY FUNCTION DEPLOYMENT (QFD) QFD is teamwork and matrix diagram-based method used primarily during the product and process design and development. Its purpose is to transform stakehold-er requirements (particularly those raised by customers) into next phases of product and process design and de-velopment. There are several approaches to QFD ap-plication. They differ primarily in the number of matrix diagrams used in the transformation of requirements into product and process features. QFD is typically used for converting customer re-quirements into basic technical parameters (quality characteristics) of the product. The resulting matrix dia-gram is termed the “House of Quality” (HOQ). It is de-veloped by a team composed of employees of various specialized departments, namely marketing, develop-ment, engineering design and quality control. Figure 2 shows the basic structure of the HOQ diagram. In the ﬁ rst step, the requirements for the product – WHAT – are identiﬁ ed. Then, a customer evaluation is used to assign every requirement an importance level and to as-sess the customer satisfaction with how the require-ments are met in comparison with competing products. The output of this stage is a set of relative weights of individual requirements. At the next stage, each identiﬁ ed requirement is as-signed certain quality characteristics that have impact on its fulﬁ llment – HOW. The central part of the HOQ is a matrix of interrelationships. It contains an analysis of relationships between product quality characteristics and the customer requirements. The analysis of these interrelationships provides data for identifying possible missing quality characteristics and allows to identify key quality characteristics (those with most substantial impact on fulﬁ lling customer requirements). The HOQ roof contains an analysis of the interrelationships be-tween those quality characteristics, the knowledge of which is important to optimizing the target values of quality characteristics. The output is a set of measurable target values of quality characteristics and their permis-sible tolerances for meeting customer requirements and achieving customer satisfaction.FAILURE MODE AND EFFECT ANALYSIS (FMEA) FMEA is a tool for optimizing a product or process design from the perspective of potential failures. De-sign FMEA includes an analysis of the product’s ﬁ tness for given purpose. It allows the risks of potential fail-ures to be minimized. A Process FMEA explores how well the production process (or any other process) is designed. It allows the risks of failures that might occur during production to be minimized. FMEA represents a systematic approach to preventing poor product quality and to minimizing the risk of problems. When used cor-rectly, it can make the product and process quality plan-ning substantially more effective.APPLICATION OF QFD IN WELDING WIRE DESIGNAt the ﬁ rst stage of QFD application it was neces-sary to identify requirements for the welding wire. The ﬁ nal product was packaged welding wire wound on a coil. With using afﬁ nity and systematic diagrams they were processed information about customer require-ments, requirements derived from complaints of cus-tomer, product requirements stipulated by the producer but omitted by the customer who believed them to be self-evident, legal requirements and speciﬁ cations based on technical standards. A total of 24 requirements have been identiﬁ ed.Figure 1  Proportion of companies using QFD and FMEA methods vs. their QMS certifi cation level0102030405060708090100QFD DFMEA PFMEAproporon of companies using method, %No QMS cerﬁcate ISO 9001 ISO/TS 16949Figure 2 House of Quality 531METALURGIJA 52 (2013) 4, 529-532D. VYKYDAL et al.: USE OF QUALITY PLANNING METHODS IN OPTIMIZING WELDING WIRE QUALITY...With using methodology described in they were per-formed assessment of requirements importance, a com-parison of producer and competitor ability to meet these requirements and assessment of the impact of meeting individual requirements on the product saleability. On the basis of these data, relative weights of individual requirements were determined as percentage values. The entire process of constructing the House of Quality diagram was carried out using MS Excel environment. The highest weights for a particular situation with the producer were found to be associated with the follow-ing requirements [9]:- nice appearance of the package- cohesion of the protective copper layer- suitability of the package in protecting the wire dur-ing transport and storage- ﬁ tness of the coil for its purpose- secure attachment of wire ends to the coil- effective corrosion protection provided by the pack-age- clean surface free from dirt and defects- smooth and continuous feeding of the wire - adequate identiﬁ cation of each smallest packaging unit- correct chemical composition of the wire - adequate elongation of the weld metal- adequate tensile strength of the weld metal- adequate yield strength of the weld metal- adequate impact energy at the speciﬁ ed temperature- the surface and type of ﬁ nish of the wire must not impair the properties of the weld metal, and othersA surprisingly high relative weight was found with the nice appearance of the package of the coiled wire. This was connected with the fact that the packaging ap-pearance was one of the weaknesses of the manufac-turer’s production.  In the next step, those wire quality characteristics were identiﬁ ed which affect the meeting of individual requirements. The strengths of their interrelationships were evaluated and relative weights of quality charac-teristics were calculated. A total of 44 quality character-istics were identiﬁ ed, which have impact on compliance with requirements for the product. The highest relative weight was found for following quality characteristics:- copper uptake- manganese content- carbon content- adhesion of copper layer- occurrence of surface defects- packaging strength- phosphorus content- sulphur content- surface cleanness- variance in copper layer thickness - graphic design of the package- coil shape integrity- molybdenum content, and othersIn the next phase of construction of the House of Quality, the data on quality characteristics were com-pared with those for competing products. An analysis of the interrelationships between quality characteristics was conducted and embodied in the roof of the HOQ. Finally, target values of welding wire quality character-istics were proposed on the basis of all data contained in processed House of Quality. Target values for quality characteristics with the highest relative weights are summarized in Table 1.Table 1  List of proposed target values for quality characteristics of the welding wireWelding wire quality characteristicTarget value (incl. tolerances) Relative weight/%Copper uptake 2±1 g/m2 7,07Mn content 1,4 – 1,55 % (wt.) 6,54Carbon content 0,06 – 0,09 % (wt.) 6,45Adhesion of copper layerno adhesive failure 4,76Occurrence of surface defectsno surface defects 4,04Packaging strength no failure during handling and transport3,64Phosphorus content ≤ 0,02 % (wt.) 2,95Sulphur content ≤ 0,02 % (wt.) 2,95Surface cleanness no dirt 2,86Variance in Cu layer thickness 1,1 – 3,4 μm 2,77Graphic design of the package attractive appearance, setting it apart from competing products2,53Coil shape integrity no shape and dimensional deviations2,40Mo content ≤ 0,05 % (wt.) 2,39APPLICATION OF FMEA IN DESIGN OPTIMIZATION Design FMEA was used for optimizing the design of the welding wire. One-by-one, all potential failures of welding wire have been identiﬁ ed, which may occur during its use, their possible effects and possible causes. The severity, probability of occurrence and likelihood of detection were evaluated for each failure. A risk pri-ority number (RPN) was assigned to each failure and compared with critical value. Where this critical value is exceeded, appropriate preventive actions should be proposed, particularly in terms of the welding wire de-sign, to reduce the risk of the failure in question. The application of FMEA led to the conclusion that the risk priority number for none of the potential failures ex-ceeds the critical value. The analysis, however, revealed several opportunities for design improvements. For in-stance, it was proposed to add a note to the operation manual that the customer should use protective equip-ment while handling the wire to prevent wire contami-nation as a direct contact between the skin and wire sur-face may lead to corrosion. In addition, it was recom-mended to alert the customer to the fact that a poor condition of the welding machine may cause the copper layer to peel, leading to nozzle clogging and welding tip fusion.532  METALURGIJA 52 (2013) 4, 529-532D. VYKYDAL et al.: USE OF QUALITY PLANNING METHODS IN OPTIMIZING WELDING WIRE QUALITY...CONCLUSION AND BENEFITSThe research showed that application of QFD and FMEA methods in metallurgical production may lead to a number of new and useful ﬁ ndings that substantially enhance the product design and development process. This, in turn, may improve the quality of ﬁ nal products, customer satisfaction and the organization’s competi-tiveness. For quality planning methods to be deployed in a broader context, organizations should eliminate ob-stacles to their application. In particular, the awareness of appropriate quality planning methods should be im-proved and their use should be supported by the man-agement.AcknowledgementThis paper was prepared under the speciﬁ c research project No. SP2012/37 conducted at the Faculty of Met-allurgy and Materials Engineering, VŠB-TU Ostrava with the support of Ministry of Education of the Czech Republic.REFERENCES[1] J. Plura, Plánování a neustálé zlepšování jakosti, Compu-ter Press, Praha, 2001, 244 p.[2] S. Vinodh, Gopinath Rathod, Application of ECQFD for enabling environmentally conscious design and sustaina-ble development in an electric vehicle, Clean Technologies and Environmental Policy, 13 (2011) 2, 381-396.[3] I. Erikkson, F. McFadden, Quality function deployment: a tool to improve software quality, Information and Software Technology, 35 (1993) 9, 491-498.[4] M. H. Korayem, A. Iravani, Improvement of 3P and 6R mechanical robots reliability and quality applying FMEA and QFD approaches, Robotics and Computer-Integrated Manufacturing 24 (2008), 472-487.[5] L. Lengyel, K. Zgodavova, P. Bober, Modeling and Simu-lation of Relocation of a Production in SIMPRO-Q Web Based Educational Environment, International Journal of Advanced Corporate Learning, 5 (2012) 1, 26-31.[6] Z. Hajduová, E. Weiss, L. Mixtaj, Application of statistical methods at copper wire manufacturing, Metalurgija, 48 (2009), 55-58.[7] D. Noskievičová, R. Kucharczyk, Effective application of statistical process control (SPC) on the lengthwise tonsure rolled plates process, Metalurgija, 51 (2012), pp. 137-140.[8] D. Noskievičová, Statistical analysis of the blast furnace process output parameter using ARIMA control chart with proposed methodology of control limits setting, Metalurgi-ja, 48 (2009), 281-284.[9] J. Plura, Quantitative Evaluation of House of Quality at QFD Application, Quality, Innovation, Prosperity, 5 (2001) 2, 59-64.Note:  The responsible translator for English language is J. Drnek, Ostrava, Czech Republic

Use of quality planning methods in optimizing welding wire quality characteristics

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Use of quality planning methods in optimizing welding wire quality characteristics

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