Abstract
The nucleus of this concept and system is directly focused on a 'computer numerical control' (CNC) turret lathe and milling machine tool systems. These concepts focus specifically to this category of engineered systems. Quality design review for quality service systems is a unique concept. Standard product service systems are qualitative and subjective in nature. A quantitative system identifies Key Predictive Attributes (KPA’s), which identifies a new concept application technique and applies quantitative methods to these attributes to develop a systemic process of analyzing and monitoring the system. This research is reviewing the specific projection of service outcomes for Machine tool CNC machining centers (Lathes and Milling Machines). The specific key predictive attributes are the elements being utilized in the newly created modular function in this research, to assess the potential impact of discrete elements of these attributes as it affects the occurrence of equipment down time for a system which will work to quantify the service quality of the maintenance process. This project is unique in that currently there is no system which utilizes methods or tools, that proactively gather, analyze, assess, and project outcomes of equipment “Down Time” of the Service Quality process. The innovative position of this analysis is one of actual variable tolerances, versus a more traditional nominal referenced variable reference. What makes this research unique additionally is the system is pre-service and not post service reporting of actual down time of the equipment. This research is much more than pro-forma estimate of service outcomes. Another unique aspect of this method is that it will establish tangible tolerances to assess the performance of the Design Review and Service Quality process and not just rely on subjective nominal values. Mathematical Upper Control Limits (UCL) and Lower Control Limits (LCL) will be programmatically developed based upon the system data. This system tool will develop programming algorithms which will propel this current process from a subjective qualitative process to become a robust quantitative projection tool. The novelty in this research is the development of a quality index through the creation of the new Moriarty/Ranky Transform approach.
Highlights
The purpose of this research article is to present a set of quantifiable measures for the prediction and performance of quality maintenance of computer numerical control' (CNC) manufacturing equipment for improving productivity and reduce machine down time due to unscheduled and scheduled maintenance functions that can be applied to this equipment
In this paper, End of life (EoL) is used as the measure of the product productivity [1] used as a gauge to quantify the productivity impact of a given data set for CNC service maintenance
An approach was developed utilizing integer programming to select key alternatives and optimize CNC machines configuration of components, composed of large number of components. This method of option determination analysis utilizes a number of attributes: material selection, supplier selection, manufacturing process selection, assembly process selection and EoL for components of a CNC machine are integrated and solved simultaneously
Summary
The purpose of this research article is to present a set of quantifiable measures for the prediction and performance of quality maintenance of CNC manufacturing equipment for improving productivity and reduce machine down time due to unscheduled and scheduled maintenance functions that can be applied to this equipment. Production/Assembly, Use/Operation (which is not component of this research study), Quantified Service Quality Maintenance and End of Life (EoL) steps. The focus is on the preliminary materials suppliers, manufacturing /production/assembly method, Quantifiable Service Quality maintenance, and EoL analysis. The overall objectives are to minimize the service costs and productivity interruptions through quantifiable service quality system methods and equipment life cycle management This model is designed to assess different issues simultaneously: namely: material alternative selection, supplier selection, manufacturing and assembling process selection as well as the actual service functions and equipment EoL options. The M/R T algorithm can be used for solving multiple causes simultaneously e.g.: component alternatives, supplier selection, production and assembly processes and service options and the EoL option determination of the equipment
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