A New Model Supporting Stability Quality of Materials and Industrial Products

Abstract

Stabilizing the quality of industrial product materials remains a challenge. This applies mainly to new or significantly modified materials. It also refers to special processes. The tests of product quality can stabilize the quality of industrial product materials. The popular method for this is using the non-destructive testing (NDT). The NDT identifies incompatibility but does not determine the cause of its occurrence. Hence, it was necessary to support the process of identifying causes of incompatibilities in products. The purpose of the article was to develop a model based on a new approach to determine the ranking of actions that are possible as part of the process of stabilizing the quality of industrial products. The model was developed to improve quality through sequential and systematic methods of identification (and reduce) and incompatibility. The quality management techniques and decision method were applied and combined in this model, i.e., SMART(-ER) the method, method of selecting a team of experts, brainstorming (BM), Ishikawa diagram with the 5M rule, Likert scale validation technique, arithmetic average, and Grey Relational Analysis (GRA). The test of this model was carried out to find cracks in the outer hull of 418 alloy four-point bearing (CPW-S 5616), which was identified by NDT (magnetic-powder method). As a result, a ranking of activities was obtained to stabilize the quality of the product and the main cause of incompatibility was indicated, i.e., the cause which can influence to the most degree influence on occurrence the incompatibility. The originality of the proposed model is an application in the right order of specially selected and combined qualitative methods and supporting decision methods. The finding of causes of incompatibility of products is the basis of product improvement in the area of stabilizing the quality of materials, mainly by the occurrence of special processes. The universality of the model refers to the possibility of its application for any material, processes of its formation, and processes of products, and any incompatibilities where the model can be integrated with quality control.

Method of Fuzzy Analysis of Qualitative-Environmental Threat in Improving Products and Processes (Fuzzy QE-FMEA)

“…The assumptions were assumed based on the FMEA methodology, assumptions for decision making based on multiple criteria in a fuzzy decision environment, and also based on the literature review. The assumptions for the modified FMEA method, i.e., for fuzzy QE-FMEA method were following: products or processes to be analysed are arbitrary with the use of adequate priority number selection tables [ 1 , 3 , 37 ]; quality is expressed by the Z index (i.e., the importance of the defect for the customer) and refers to the effect of the defect on the use of the product/or the functioning of the process, the impact on customer satisfaction and possible repair costs) [ 7 , 29 , 38 ]; the impact on the natural environment (indicator E) is the negative impact of a defect (product or process) on the natural environment [ 4 , 12 , 38 ]; the threat priority value (RQE) is calculated in a fuzzy decision-making environment and is a quotient of the ratings assigned to the indicators, i.e.,: P—probability of a defect, W—possibility of detecting a defect, Z—effect (significance) of the defect (so-called quality), E—impact on the natural environment, where Z and E are combined and created qualitative environmental indicator (QE) and are evaluated simultaneously in the pairwise comparison matrix [ 2 , 30 , 39 , 40 ]; when determining the number Z (significance of the defect), only the effect of the defect should be considered; determining the number P (the probability of a defect) may refer to the defect but also to the cause of the defect, it is necessary to consistently comply with the adopted rule; determining the number W (the possibility of detecting a defect) refers to the cause of the defect; criteria for assessing indicators P (probability of a defect), W (possibility of detecting defects), and QE (qualitative-environmental indicator) result from the subject of the analysis and are selected individually by a team of experts, considering, for example, the frequency of incompatibilities, their effects, and causes; the QE indicator results from the relationships occurring in a fuzzy decision-making environment and the nine-point Saaty scale [ 3 , 12 , 33 ]. …”

“…products or processes to be analysed are arbitrary with the use of adequate priority number selection tables [ 1 , 3 , 37 ];…”

“…the QE indicator results from the relationships occurring in a fuzzy decision-making environment and the nine-point Saaty scale [ 3 , 12 , 33 ].…”

“…These values are determined on a scale of 1 to 10 ( Table 3 and Table 4 ). Following the assumption of the authors of the work [ 3 , 12 , 33 ] assumed, that sum of values ) are noted in the pairwise comparison matrix as shown formula (1): where: Q—quality, E—impact on the natural environment, i, j = 1, 2, …, 10.…”

“…It is a necessary action for all prosperous enterprises [ 1 , 2 ]. In order to stabilise actions made by enterprises, it is important to effectively analyse hazards in the area of projects, processes, and products [ 3 , 4 ]. These thoughtful actions could reduce the incompatibilities of products or processes; hence, its quality is more sufficient.…”