ارتباط بین ابزارهای تضمین کیفیت و معیارهای ماشینکاری

This paper discusses a practical example of machining crank shafts, which are the central part of compressors made in mass production, in order to point out the importance of selecting proper machining parameters and presenting their further relation to product quality. As the shaft’s dimensions varied significantly after machining in comparison to the prescribed one, an analysis of the relationship between the machining parameters and data obtained from quality assurance tools was performed. These tools are frequently used in everyday process controls, however, they are often incorrectly used, or else not all the information is turned to advantage. All the possible causes that may influence machining accuracy are presented with the aim of determining the magnitude of each factor. Special attention is focused on the influence of variations caused by machine inaccuracies. This paper concludes with a proposal for the proper use and interpretation of statistical tools in quality assurance procedures in order to detect disturbances in the process caused by the excessive wear of the cutting tools.

In practical machining applications, one may distinguish between two different approaches for evaluating the efficiency of cutting tools with respect to their wear resistance; the first is tool life obtained on the basis of experimental tests, and the second is the process identification procedure, which considers a tool as a part of the monitoring equipment. For nearly a decade, we have believed that the basic mechanisms of tool wear and different kinds of wear produced at the tool tip can be understood (see [1]). On the basis of experimental measurements of different tool wears, and the application of proper statistical techniques, it was possible to predict the tool life and therefore the intervals of changing the tools. This was the period of intensive work on the so-called “databases on machining parameters” (some basic contributions regarding the author’s participation in such efforts can be found in [2] and [3]). However, in daily production practice, the situation is different: the tool changing interval is usually prescribed in advance with a high degree of safety, which increases tool costs. Consequently, the use of expensive monitoring equipment remains a distant desire. On the other hand, the quality assurance tools are widely used in workshop production as a necessary procedure in assuring product quality. By analysing the given benefits, one can easily note that all the potentials of usage of these tools are not exploited; moreover, the reality is that the main efforts in many companies are directed towards satisfying the ISO 9000 standard requirements and towards eliminating the unsuitable products. Therefore, we will usually find many possibilities for improvements if we are able to understand and explain all particular sources of variations. Such an approach will also be the easiest way to control processes and improve them.

Statistical process control is a methodology for monitoring a process to identify special causes of variation and to signal the need to take corrective action when appropriate. When special causes are present, the process is deemed to be out of control. Among different tools, the process capability and control analysis and control charts are very simple to use. Although, on one hand, the basic tools for the process control are well known and widely introduced in the workshop environment, on the other hand, the disadvantages in their applications lies in the difficulties or the lack of knowledge and skills to interpret the results. In machining, one of the disadvantages is that the users of the data usually do not have sufficient knowledge about the process, and therefore cannot identify all the causes which might influence the machining accuracy. Therefore, their most common action when the process is out of control is to stop the operation and call for specialists, which takes time and causes additional costs due to production delays. Disturbances resulting from tool wear or other inaccuracies from the machine tool can be easily identified from the statistical process control tools. Any sudden shift in the process average is the result of an external influence that has effected the process such as tool wear. Especially, in machining examples, such as turning with ceramic tools — where reliable prediction of tool life is rather difficult — the charts can be a very useful tool for the machine operator. This paper presents particular examples of machining crank shafts. From the results obtained from the statistical process control, we identify tool wear, and machine inaccuracies; our special intention, however, was to point out the importance of proper interpretation of the data at the workshop level.