A key ingredient to underpin process improvement is a robust, reliable, repeatablemeasurement system. Process improvement activity needs to be supported by accurate andprecise data because effective decision making, within process improvement activity,demands the use of “hard” data. One of the oldest and most established process improvementmethods is Deming’s Plan-Do-Check-Act (PDCA) cycle which is reliant on the check phase, ameasurement activity where data is being gathered and evaluated. Recent expansions of thePDCA such as the Six-Sigma Define-Measure-Analyse-Improve-Control (DMAIC) methodologyplace significant importance upon measurement. The DMAIC cycle incorporates the regimentedrequirement for the inclusion of measurement system analysis (MSA) into the breakthroughstrategy. The call for MSA within the DMAIC cycle is to provide the improvement activitywith a robust measurement system that will ensure a pertinent level of data during anyvalidation process. The Lean methodology is heavily centred on the removal of the sevenMudas (wastes) from a manufacturing process: defects, overproduction, transportation,waiting, inventory, motion and processing. The application of lean, particularly withinthe manufacturing industry, has led to a perception that measurement is a waste within amanufacturing process because measurement processes identify defective products. Themetrologists’ pursuit for measurement excellence could be construed as a hindrance by the“cost down” demands being perpetrated from the same organisation’s lean policy. So whatpossible benefits does enforcing the regimes of the lean and quality philosophies upon themeasurement process have and how does this ultimately enhance the process improvementactivity? The key fundamental to embed with any process improvement is the removal ofwaste. The process improvement techniques embedded within lean and quality concepts areextremely powerful practices in the drive to eradicate waste but there are numerouscontextual problems with the application of the process improvement activity and itsassociated measurement system. The application demands of the organisation may bedependent on a number of financial and resource constraints which may introduce reasonsnot to apply stringent measurement methodology and practice. The failure of variousprocess improvement activities due to poorly managed measurement activity has arguablynever been comprehensively analysed. Process improvement activity theory fully embellishesthe need for applied robust measurement systems, so how can process improvement andmeasurement be systematically aligned to gain benefits? The aim of this paper is toconsider whether lean philosophies can be integrated and applied within measurementsystems. The discussion seeks to identify if seven Muda exist within measurement systemsand whether in doing so this will lead to benefits for process improvement activities.

Classic methodologies of DOE are widely applied in design, manufacture, qualitymanagement and related fields. The resulting data can be analysed with linear modelingmethods such as multiple regression which generates a set of equations, Y = F(X), thatenable us to understand how varying the mean of one or more inputs changes the mean of oneof more responses. To develop, scale-up and transfer robust processes to manufacturing wealso need to set the control tolerances of each critical X and understand the extentto which variation in the critical X’s propagate through to variation in theY’s and howthis may impact performance relative to requirements (or specifications). Visual toleranceanalysis provides a simple way to understand and reduce propagation of variation fromX’s toY’s usingmodels developed from DOE’s or historical data. This paper briefly introduces the conceptof tolerance analysis and extents this to visual tolerance analysis through defectprofiles and defect parametric profiles. With the help of visual tolerance analysis,engineering and statistical analysts can work together to find the key factors responsiblefor propagating undesired variation into responses and how to reduce these effects todeliver a robust and cost effective process. A case study approach is used to aidexplanation and understanding.

Manufactured parts are affected by size and form errors, which need to be assessedagainst geometrical and dimensional tolerances in order to meet the functionalrequirements they have been conceived for. The compliance assessment of workpieces withspecifications depends on measurements and is unavoidably affected by several uncertaintycontributions. According to the geometrical product specifications and verification (GPS),measurement uncertainty consists of method and implementation uncertainty. Literatureproposes different approaches for the evaluation of implementation uncertainty, however astandardized method has not been yet achieved. This paper makes an analysis of the variouselements to be considered when choosing a specific approach: in the implementationuncertainty of circular features. The most common manufacturing signatures affectingcircular profiles were considered, together with the number of points necessary for areliable estimation of implementation uncertainty.

This paper presents a joint use of the T2 chart and total inertial tolerancingfor process control. Here, we will show an application of these approaches in the case ofthe machining of mechanical workpieces using a cutting tool. When a cutting tool inmachining impacts different manufactured dimensions of the workpiece, there is acorrelation between these parameters when the cutting tool has maladjustment due to badsettings. Thanks to total inertial steering, the correlation structure is known. Thispaper shows how T2 charts allow one to take thiscorrelation into account when detecting the maladjustment of the cutting tool. Then thetotal inertial steering approach allows one to calculate the value of tool offsets inorder to correct this maladjustment. We will present this approach using a simpletheoretical example for ease of explanation.

Manufacturing of high-quality components and assemblies is clearly recognised byindustrialised nations as an important means of wealth generation. A “right first time”paradigm to producing finished components is the desirable goal to maximise economicbenefits and reduce environmental impact. Such an ambition is only achievable through anaccurate model of the machinery used to shape the finished article. In the first analysis,computer aided design (CAD) and computer aided manufacturing (CAM) can be used to producean instruction list of three-dimensional coordinates and intervening tool paths totranslate the intent of a design engineer into an unambiguous set of commands for amanufacturing machine. However, in order for the resultant manufacturing program toproduce the desired output within the specified tolerance, the model of the machine has tobe sufficiently accurate. In this paper, the spatial and temporal sources of error andvarious contemporary means of modelling are discussed. Limitations and assumptions in themodels are highlighted and an estimate of their impact is made. Measurement of machinetools plays a vital role in establishing the accuracy of a particular machine andcalibrating its unique model, but is an often misunderstood and misapplied discipline.Typically, the individual errors of the machine will be quantified at a given moment intime, but without sufficient consideration either for the uncertainty of individualmeasurements or a full appreciation of the complex interaction between each independentlymeasured error. This paper draws on the concept of a “conformance zone”, as specified inthe ISO 230:1 – 2012, to emphasise the need for a fuller understanding of the complexuncertainty of measurement model for a machine tool. Work towards closing the gap in thisunderstanding is described and limitations are noted.

Treatment of experimental data often entails fitting frequency functions, in order todraw inferences on the population underlying the sample at hand, and/or identify plausiblemechanistic models. Several families of functions are currently resorted to, providing abroad range of forms; an overview is given in the light of historical developments, andsome issues in identification and fitting procedure are considered. But for the case offairly large, well behaved data sets, empirical identification of underlying distributionamong a number of plausible candidates may turn out to be somehow arbitrary, entailing asubstantial uncertainty component. A pragmatic approach to estimation of an approximateconfidence region is proposed, based upon identification of a representative subset ofdistributions marginally compatible at a given level with the data at hand. Acomprehensive confidence region is defined by the envelope of the subset of distributionsconsidered, and indications are given to allow first order estimation of uncertaintycomponent inherent in empirical distribution fitting.

The geometric measurement of parts using a coordinate measuring machine (CMM) has beengenerally adapted to the advanced automotive and aerospace industries. However, for thegeometric inspection of deformable free-form parts, special inspection fixtures, incombination with CMM’s and/or optical data acquisition devices (scanners), are used. As aresult, the geometric inspection of flexible parts is a consuming process in terms of timeand money. The general procedure to eliminate the use of inspection fixtures based ondistance preserving nonlinear dimensionality reduction (NLDR) technique was developed inour previous works. We sought out geometric properties that are invariant to inelasticdeformations. In this paper we will only present a systematic comparison of somewell-known dimensionality reduction techniques in order to evaluate their accuracy andpotential for non-rigid metrology. We will demonstrate that even though these techniquesmay provide acceptable results through artificial data on certain fields like patternrecognition and machine learning, this performance cannot be extended to all realengineering metrology problems where high accuracy is needed.

Quality assurance is an integrated part of any calibration facility. The calibrationfacility as well as its customers are interested in the facility production outgoingquality. In most calibration labs the inspection of calibrated items is performedaccording to a suitable sampling inspection policy. Some of these policies are very goodin assuring the quality of the calibration services they offer, but do not provide a clearassessment of the outgoing quality of the entire production of the facility. This paperhas developed two methods of calculating the average outgoing quality (AOQ) of acalibration lab that uses a multistage sampling inspection policy. The policy structure ispresented first along with the exact procedure of how to perform it by the inspectors andthe methods to calculate the AOQ. The two methods differ from each another in the type ofdata required to calculate the AOQ. The first method requires the technicians’ production,the number of items subject to inspections and the number of failing items found. Thesecond method requires only the number of technicians at each level of the multistageinspection policy. The verifications of the performances of two methods are accomplishedby building a simulation model on an Excel worksheet. The model simulates the calibrationfacility with the right parameters, and then compares the two methods with the actual AOQ.The paper further discusses the advantages and disadvantages of each method in a broadercontext of quality assurance.

MEMS gyroscopes as a kind of angular rate sensor have been widely used, but theiraccuracy tends to be low in practical applications, especially under temperatureinfluence, and they generally require error compensation. Based on the analysis ofgyroscope operating principle, this paper has shown that the resonant frequency andmeasuring precision of the gyroscope are dependent on temperature and temperaturegradients. The paper has thus proposed a compensation model based on temperature andtemperature gradients. The experimental results have demonstrated that the thermal driftof zero bias can be effectively suppressed, and the accuracy can be improved by one orderof magnitude after compensation. Compared with compensation methods only based ontemperature, the new method gives significantly better performance. The new errorcompensation model has not only integrated the differences under different temperatureconditions, but also reduced the repeatability errors. It provides a theoretical basis foraccurate compensation of the gyroscope thermal error in practical applications and isapplicable to other MEMS gyroscopes.

Product yield reflects the potential product quality and reliability, which means thathigh yield corresponds to good quality and high reliability. Yet consumers usuallycouldn’t know the actual yield of the products they purchase. Generally, the products thatconsumers get from suppliers are all eligible. Since the quality characteristic of theeligible products is covered by the specifications, then the observations of qualitycharacteristic follow truncated normal distribution. In the light of maximum likelihoodestimation, this paper proposes an algorithm for calculating the parameters of fullGaussian distribution before truncation based on truncated data and estimating productyield. The confidence interval of the yield result is derived, and the effect of samplesize on the precision of the calculation result is also analyzed. Finally, theeffectiveness of this algorithm is verified by an actual instance.