Investigation of the effect of temperature and other significant factors on systematic error and measurement uncertainty in CMM measurements by applying design of experiments

Štrbac, Branko; Ačko, Bojan; Havrlišan, Sara; Matin, Ivan; Savković, Borislav; Hadžistević, Miodrag

doi:10.1016/j.measurement.2020.107692

Cited by 23 publications

(12 citation statements)

References 26 publications

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“…Through their method, optimized 3-axis sculptured surface CNC machining can be used in the milling process. Strbac et al [54] researched the significance of particular factors (temperature drift, systematic error, asymmetric error) in coordinate measuring machine (CMM) measurements. They proposed a design of experiments (DoE) method for measurement uncertainty in ultra-precision machining.…”

Section: Processing-measuring Integrated Control Systemmentioning

confidence: 99%

Key Technologies and Development Trends in Advanced Intelligent Sawing Equipments

Wang

Zhang

Tan

2021

Chin. J. Mech. Eng.

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As a starting point in equipment manufacturing, sawing plays an important role in industrial production. Intelligent manufacturing equipment is an important carrier of intelligent manufacturing technologies. Due to the backwardness of intelligent technology, the comprehensive performance of sawing equipments in China is obviously different from that in foreign countries. State of the art of advanced sawing equipments is investigated along with the technical bottleneck of sawing machine tool manufacturing, and a new industrial scheme of replacing turning-milling by sawing is described. The key technologies of processing-measuring integrated control, multi-body dynamic optimization, the collaborative sawing network framework, the distributed cloud sawing platform, and the self-adapting service method are analyzed; with consideration of the problems of poor processing control stableness, low single machine intelligence level, no on-line processing data service and active flutter suppression of sawing with wide-width and heavy-load working conditions. Suggested directions for further research, industry implementation, and industry-research collaboration are provided.

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Section: Processing-measuring Integrated Control Systemmentioning

confidence: 99%

Key Technologies and Development Trends in Advanced Intelligent Sawing Equipments

Wang

Zhang

Tan

2021

Chin. J. Mech. Eng.

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“…Layer 5 is the output layer. In this layer, known as the defuzzification layer, the output is obtained using equation (7). Parameters in this layer are referred to as consequent parameters:…”

Section: Adaptive Neuro-fuzzy Inference System (Anfis)mentioning

confidence: 99%

“…Additionally, verification procedures have to perform almost ideal measurements because tight tolerances do not leave much room for measurement error or measurement uncertainty although these two parameters are always present in the measurement result [7]. Different types of coordinate measuring systems (CMS) are used to measure different kinds of deviation although there are measuring instruments, too, which can measure both types of deviation, such as the micro-coordinate measuring machine (µCMM) by Bruker Alicone.…”

Section: Introductionmentioning

confidence: 99%

Investigation of Functional Dependency between the Characteristics of the Machining Process and Flatness Error Measured on a CMM

Štrbac

Rodić

Delić

et al. 2021

Measurement Science Review

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Numerous studies have shown that the choice of measurement strategy (number and position of measurement points) when measuring form error on a coordinate-measuring machine (CMM) depends on the characteristics of the machining process which was used to machine the examined surface. The accuracy of form error assessment is the primary goal of verification procedures and accuracy is considered perfect only in the case of the ideal verification operator. Since the ideal verification operator in the “point-by-point” measuring mode is almost never used in practice, the aim of this study was to examine a relationship which had not been examined in earlier studies, namely how the machining process, surface roughness and a reduced number of points in the measurement strategy affect the accuracy of flatness error assessment. The research included four most common cutting processes applied to flat surfaces divided into nine different classes of roughness. In order to determine functional dependency between the observed input variables and the output, statistical regression models and neuro-fuzzy logic (artificial intelligence tool) were used. The analyses confirmed the significance of all three input parameters, with surface roughness being the most significant one. Both the statistical regression models and neuro-fuzzy models proved to be adequate, matching the experimental results. The use of these models makes it possible to determine flatness error measured on a CMM if input variables considered in the paper are known.

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“…Finally, when observing the application of the factor plan in production systems, the range is endless. Thus, we find application in the field of metallurgical characteristics such as identification of corrosion intensity [15], optimization of tool trajectory in order to increase productivity [16], selection of the most favourable tool angles from the aspect of precision [17], determining the measurement uncertainty of the coordinate measuring machine [18], selection of the most favourable processing regimes from the aspect of machinability [19][20][21][22][23], determining the state of construction systems, in terms of damage [24], testing the influence of operating parameters on the rotating elements of the machine [25].…”

Section: Introductionmentioning

confidence: 99%

Decoding of Factorial Experimental Design Models Implemented in Production Process

Alnadish¹,

Aman²,

Katman³

et al. 2022

Computers, Materials &Amp; Continua

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The paper deals with factorial experimental design models decoding. For the ease of calculation of the experimental mathematical models, it is convenient first to code the independent variables. When selecting independent variables, it is necessary to take into account the range covered by each. A wide range of choices of different variables is presented in this paper. After calculating the regression model, its variables must be returned to their original values for the model to be easy recognized and represented. In the paper, the procedures of simple first order models, with interactions and with second order models, are presented, which could be a very complicated process. Models without and with the mutual influence of independent variables differ. The encoding and decoding procedure on a model with two independent first-order parameters is presented in details. Also, the procedure of model decoding is presented in the experimental surface roughness parameters models' determination, in the face milling machining process, using the first and second order model central compositional experimental design. The simple calculation procedure is recommended in the case study. Also, a large number of examples using mathematical models obtained on the basis of the presented methodology are presented throughout the paper.

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Investigation of the effect of temperature and other significant factors on systematic error and measurement uncertainty in CMM measurements by applying design of experiments

Cited by 23 publications

References 26 publications

Key Technologies and Development Trends in Advanced Intelligent Sawing Equipments

Key Technologies and Development Trends in Advanced Intelligent Sawing Equipments

Investigation of Functional Dependency between the Characteristics of the Machining Process and Flatness Error Measured on a CMM

Decoding of Factorial Experimental Design Models Implemented in Production Process

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