A Preliminary Study of Applying Lean Six Sigma Methods to Machine Tool Measurement

Many manufacturers aim to increase their levels of high-quality production in order to improve their market competitiveness. Continuous improvement of maintenance strategies is a key factor to be capable of delivering high quality products and services on-time with minimal operating costs. However, the cost of maintaining quality is often perceived as a non-added-value task. Improving the efficiency and effectiveness of the measurement procedures necessary to guarantee accuracy of production is a more complex task than many other maintenance functions and so deserves particular analysis. This paper investigates the feasibility of producing a concise yet effective framework that will provide a preliminary approach for integrating Lean and Six Sigma philosophies to the specific goal of reducing unnecessary downtime on manufacturing machines while maintaining its ability to machine to the required tolerance. The purpose of this study is to show how a Six Sigma infrastructure is used to investigate the root causes of complication occurring during the machine tool measurement. This work recognises issues of the uncertainty of data, and the measurement procedures in parallel with the main tools of Six Sigma’s Define-Measure-Analyse-Improve-Control (DMAIC). The significance of this work is that machine tool accuracy is critical for high value manufacturing. Over-measuring the machine to ensure accuracy potentially reduces production volume. However, not measuring them or ignoring accuracy aspects possibly lead to production waste. This piece of work aims to present a lean guidance to lessen measurement uncertainties and optimise the machine tool benchmarking procedures, while adopting the DMAIC strategy to reduce unnecessary downtime

Derivation of a cost model to aid management of CNC machine tool accuracy maintenance

Manufacturing industries strive to produce improved component accuracy while not reducing machine tool availability or production throughput. The accuracy of CNC production machines is one of the critical factors in determining the quality of these components. Maintaining the capability of the machine to produce in-tolerance parts can be approached in one of two ways: run to failure or periodic calibration and monitoring. The problem is analogous to general machine tool maintenance, but with the clear distinction that the failure mode of general machine tool components results in a loss of production, whereas that of accuracy allows parts to be produced, which are only later detected as non-conforming as part of the quality control processes. This distinction creates problems of cost-justification, since at this point in the manufacturing chain, any responsibility of the machine is not directly evident. Studies in the field of maintenance have resulted in cost calculations for the downtime associated with machine failure. This paper addresses the analogous, unanswered problem of maintaining the accuracy of CNC machine tools. A mathematical cost function is derived that can form the basis of a strategy for either running until non-conforming parts are detected or scheduling predictive CNC machine tool calibrations. This is sufficiently generic that it can consider that this decision will be based upon different scales of production, different values of components etc. Therefore, the model is broken down to a level where these variables for the different inputs can be tailored to the individual manufacturer

Maintenance Strategies to Reduce Downtime Due to Machine Positional Errors

Manufacturing strives to reduce waste and increase Overall Equipment Effectiveness (OEE). When managing machine tool maintenance a manufacturer must apply an appropriate decision technique in order to reveal hidden costs associated with production losses, reduce equipment downtime competently and similarly identify the machines’ performance. Total productive maintenance (TPM) is a maintenance program that involves concepts for maintaining plant and equipment effectively. OEE is a powerful metric of manufacturing performance incorporating measures of the utilisation, yield and efficiency of a given process, machine or manufacturing line. It supports TPM initiatives by accurately tracking progress towards achieving “perfect production.” This paper presents a review of maintenance management methodologies and their application to positional error calibration decision-making. The purpose of this review is to evaluate the contribution of maintenance strategies, in particular TPM, towards improving manufacturing performance, and how they could be applied to reduce downtime due to inaccuracy of the machine. This is to find a balance between predictive calibration, on-machine checking and lost production due to inaccuracy. This work redefines the role of maintenance management techniques and develops a framework to support the process of implementing a predictive calibration program as a prime method to supporting the change of philosophy for machine tool calibration decision making. Keywords—maintenance strategies, down time, OEE, TPM, decision making, predictive calibration

Adaptive decision support for suggesting a machine tool maintenance strategy: from reactive to preventative

Purpose -- To produce a decision support aid for machine tool owners to utilise while deciding upon a maintenance strategy. Furthermore, the decision support tool is adaptive and capable of suggesting different strategies by monitoring for any change in machine tool manufacturing accuracy. Design/methodology/approach -- A maintenance cost estimation model is utilised within the research and development of this decision support system. An empirical-based methodology is pursued and validated through case study analysis. Findings -- A case study is provided where a schedule of preventative maintenance actions is produced to reduce the need for the future occurrences of reactive maintenance actions based on historical machine tool accuracy information. In the case-study, a 28% reduction in predicted accuracy-related expenditure is presented, equating to a saving of £14k per machine over a five year period. Research limitations/implications -- The emphasis on improving machine tool accuracy and reducing production costs is increasing. The presented research is pioneering in the development of a software-based tool to help reduce the requirement on domain-specific expert knowledge. Originality/value -- The paper presents an adaptive decision support system to assist with maintenance strategy selection. This is the first of its kind and is able to suggest a preventative strategy for those undertaking only reactive maintenance. This is of value for both manufacturers and researchers alike. Manufacturers will benefit from reducing maintenance costs, and researchers will benefit from the development and application of a novel decision support technique

Multi-objective optimisation of machine tool error mapping using automated planning

Error mapping of machine tools is a multi-measurement task that is planned based on expert knowledge. There are no intelligent tools aiding the production of optimal measurement plans. In previous work, a method of intelligently constructing measurement plans demonstrated that it is feasible to optimise the plans either to reduce machine tool downtime or the estimated uncertainty of measurement due to the plan schedule. However, production scheduling and a continuously changing environment can impose conflicting constraints on downtime and the uncertainty of measurement. In this paper, the use of the produced measurement model to minimise machine tool downtime, the uncertainty of measurement and the arithmetic mean of both is investigated and discussed through the use of twelve different error mapping instances. The multi-objective search plans on average have a 3% reduction in the time metric when compared to the downtime of the uncertainty optimised plan and a 23% improvement in estimated uncertainty of measurement metric when compared to the uncertainty of the temporally optimised plan. Further experiments on a High Performance Computing (HPC) architecture demonstrated that there is on average a 3% improvement in optimality when compared with the experiments performed on the PC architecture. This demonstrates that even though a 4% improvement is beneficial, in most applications a standard PC architecture will result in valid error mapping plan

Improved Machine Tool Linear Axis Calibration Through Continuous Motion Data Capture

Machine tool calibration is becoming recognised as an important part of the manufacturing process. The current international standards for machine tool linear axes calibration support the use of quasi-static calibration techniques. These techniques can be time consuming but more importantly a compromise in quality due to the practical restriction on the spatial resolution of target positions on the axis under test. Continuous motion calibration techniques have the potential to dramatically increase calibration quality. Through taking several measurement values per second while the axis under test is in motion, it is possible to measure in far greater detail. Furthermore, since machine tools normally operate in dynamic mode, the calibration data can be more representative if it is captured while the machine is in motion. The drawback to measuring the axis while in motion is the potential increase in measurement uncertainty. In the following paper, different methods of continuous motion calibration are discussed. A time-based continuous motion solution is proposed as well as a novel optimisation and correlation algorithm to accurately fuse the data taken from quasi-static and continuous motion measurements. The measurement method allows for minimal quasi-static measurements to be taken while using a continuous motion measurement to enhance the calibration process with virtually no additional time constraints. The proposed method does not require any additional machine interfacing, making it a more readily accessible solution for widespread machine tool use than other techniques which require hardware links to the CNC. The result of which means a shorter calibration routine and enhanced results. The quasi-static and continuous motion measurements showed correlation to within one micrometre at the quasi-static measurement targets. An error of 13 μm was detailed on the continuous motion, but was missed using the standard test. On a larger, less accurate machine, the quasi-static and continuous motion measurements were on average within 3 μm of each other however, showed a standard deviation of 4 μm which is less than 1% of the overall error. Finally, a high frequency cyclic error was detected in the continuous motion measurement but was missed in the quasi-static measuremen

Predictive Calibration-Based Tolerance Boundaries For Arresting Deterioration of Machine Tool Accuracy

Machine tool failures in industrial organisations disturb production operations and cause production loss. Predictive maintenance is one approach which has been successfully applied in some circumstances to allow scheduled production stoppages. It is an approach that reduces the need for reactive maintenance. Predictive maintenance is a tool that has been adopted in some industries to improve operational efficiency and reduce maintenance cost. As a result, monitoring equipment providing information about the systems conditions have evolved rapidly over the last years. Machine tools can change or drift over time and usage in both their mechanical and electrical performance and so reduce in accuracy. This paper proposes a new method for maintaining machine tool accuracy that is complimentary to the predictive maintenance paradigm. This strategy, called predictive calibration, is a methodology that depends on the prediction of the degradation in machine tool accuracy based upon regular data capture. Although introducing such a strategy will introduce a new cost, the aim is to offset this investment by optimising the operational efficiency and reduce the downtime cost. The main objective is achieved by monitoring the condition of the machine tool by collecting data using quick check measurement techniques or post-process quality data. Calibration should, therefore, be driven by the data measured from either the machine or the part. Building a database of inspection history by measuring the machine on a regular basis with relatively non-invasive methods will make the decision of scheduling extensive calibration accurate better informed process. The project presents a new method of identifying new boundaries of machine tool working tolerance. These boundaries of tolerance reflect the degradation level corresponding to production capacities and the quality of the part produced. The significance of this work is that machine tool accuracy is critical for high value manufacturing. Over-measuring the machine to ensure accuracy reduces productivity. This piece of work seeks to optimise the frequency of calibration to reduce unnecessary downtime while maintaining the machine at the required tolerance

The importance of assessing downtime cost related factors towards an optimised machine tool calibration schedule

Reducing downtime and assuring a high degree of accuracy of production machines, especially machine tools, have become increasingly important as the demand for higher production rates and closer tolerance continues to grow. The growing understanding of the importance of both calibration and maintenance in the evolving industrial scenario and the technological advancements of recent years has yielded the development of advanced metrology equipment and predictive maintenance programs. Predictive maintenance and similar programmes are tools that have been designed to reduce downtime by avoiding unpredictable machine failures. These programmes have been adopted in some industries to improve operational efficiency and reduce machine breakdown. However, extensive diagnostic procedures can take machines out of service for longer periods than are acceptable for some manufacturers. Studies in the field of predictive maintenance have resulted in cost calculations for the downtime associated with machine failure. Models have been presented to determine optimal intervals between repairs by minimising global maintenance costs. However, very little work has concentrated on optimising the frequency of machine tool calibration by assessing the downtime cost considering the contribution of both technical and commercial factors. This paper give an introduction to causes of machine tools failures with respect to production of non-conforming parts and the importance of calibration and then it addresses the key factors to a cost function parameters that forms the basis of a strategy for scheduling machine tool calibration which takes into account these influences on part tolerance

Derivation of a Cost Model to Aid Management of CNC Machine Tool Accuracy Maintenance

Manufacturing industries strive to produce improved component accuracy while not reducing machine tool availability or production throughput. The accuracy of CNC production machines is one of the critical factors in determining the quality of these components. Maintaining the capability of the machine to produce in-tolerance parts can be approached in one of two ways: run to failure or periodic calibration and monitoring. The problem is analogous to general machine tool maintenance, but with the clear distinction that the failure mode of general machine tool components results in a loss of production, whereas that of accuracy allows parts to be produced, which are only later detected as non-conforming as part of the quality control processes. This distinction creates problems of cost-justification, since at this point in the manufacturing chain, any responsibility of the machine is not directly evident. Studies in the field of maintenance have resulted in cost calculations for the downtime associated with machine failure. This paper addresses the analogous, unanswered problem of maintaining the accuracy of CNC machine tools. A mathematical cost function is derived that can form the basis of a strategy for either running until non-conforming parts are detected or scheduling predictive CNC machine tool calibrations. This is sufficiently generic that it can consider that this decision will be based upon different scales of production, different values of components etc. Therefore, the model is broken down to a level where these variables for the different inputs can be tailored to the individual manufacturer