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1 – 10 of over 15000Oscar E Ruiz, Camilo Cortes, Diego A Acosta and Mauricio Aristizabal
Curve fitting from unordered noisy point samples is needed for surface reconstruction in many applications. In the literature, several approaches have been proposed to…
Abstract
Purpose
Curve fitting from unordered noisy point samples is needed for surface reconstruction in many applications. In the literature, several approaches have been proposed to solve this problem. However, previous works lack formal characterization of the curve fitting problem and assessment on the effect of several parameters (i.e. scalars that remain constant in the optimization problem), such as control points number (m), curve degree (b), knot vector composition (U), norm degree (k), and point sample size (r) on the optimized curve reconstruction measured by a penalty function (f). The paper aims to discuss these issues.
Design/methodology/approach
A numerical sensitivity analysis of the effect of m, b, k and r on f and a characterization of the fitting procedure from the mathematical viewpoint are performed. Also, the spectral (frequency) analysis of the derivative of the angle of the fitted curve with respect to u as a means to detect spurious curls and peaks is explored.
Findings
It is more effective to find optimum values for m than k or b in order to obtain good results because the topological faithfulness of the resulting curve strongly depends on m. Furthermore, when an exaggerate number of control points is used the resulting curve presents spurious curls and peaks. The authors were able to detect the presence of such spurious features with spectral analysis. Also, the authors found that the method for curve fitting is robust to significant decimation of the point sample.
Research limitations/implications
The authors have addressed important voids of previous works in this field. The authors determined, among the curve fitting parameters m, b and k, which of them influenced the most the results and how. Also, the authors performed a characterization of the curve fitting problem from the optimization perspective. And finally, the authors devised a method to detect spurious features in the fitting curve.
Practical implications
This paper provides a methodology to select the important tuning parameters in a formal manner.
Originality/value
Up to the best of the knowledge, no previous work has been conducted in the formal mathematical evaluation of the sensitivity of the goodness of the curve fit with respect to different possible tuning parameters (curve degree, number of control points, norm degree, etc.).
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Bahattin Koc, Yawei Ma and Yuan‐Shin Lee
Presents a method of Max‐Fit biarc curve fitting technique to improve the accuracy of STL files and to reduce the file size for rapid prototyping. STL file has been widely…
Abstract
Presents a method of Max‐Fit biarc curve fitting technique to improve the accuracy of STL files and to reduce the file size for rapid prototyping. STL file has been widely accepted as a de facto standard file format for the rapid prototyping industry. However, STL format is an approximated representation of a true solid/surface model, and a huge amount of STL data is needed to provide sufficient accuracy for rapid prototyping. Presents a Max‐Fit biarc curve fitting technique to reconstruct STL slicing data for rapid prototyping. The Max‐Fit algorithm progresses through the STL slicing intersection points to find the most efficient biarc curve fitting, while improving the accuracy. Our results show that the proposed biarc curve‐fitting technique can significantly improve the accuracy of poorly generated STL files by smoothing the intersection points for rapid prototyping. Therefore, less strict requirements (i.e. loose triangle tolerances) can be used while generating the STL files.
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Hsien‐Yu Tseng and Chang‐Ching Lin
This research aims to develop an effective and efficient algorithm for solving the curve fitting problem arising in automated manufacturing systems.
Abstract
Purpose
This research aims to develop an effective and efficient algorithm for solving the curve fitting problem arising in automated manufacturing systems.
Design/methodology/approach
This paper takes curve fitting as an optimization problem of a set of data points. Expressing the data as a function will be very effective to the data analysis and application. This paper will develop the stochastic optimization method to apply to curve fitting. The proposed method is a combination optimization method based on pattern search (PS) and simulated annealing algorithm (SA).
Findings
The proposed method is used to solve a nonlinear optimization problem and then to implement it to solve three circular arc‐fitting problems of curve fitting. Based on the analysis performed in the experimental study, the proposed algorithm has been found to be suitable for curve fitting.
Practical implications
Curve fitting is one of the basic form errors encountered in circular features. The proposed algorithm is tested and implemented by using nonlinear problem and circular data to determine the circular parameters.
Originality/value
The developed machine vision‐based approach can be an online tool for measurement of circular components in automated manufacturing systems.
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The methods used in long‐range prediction include causal models and regression on leading variables, but one method which is generally worth considering is the fitting of…
Abstract
The methods used in long‐range prediction include causal models and regression on leading variables, but one method which is generally worth considering is the fitting of trend lines. It is the object of this paper to present simple techniques for fitting these lines.
Abstract
Standard value/cost flow models (often referred to as S‐curves) are widely used in cash flow forecasting, particularly at the tender stage. A substantial amount of research has concentrated on improving the accuracy of these curves. Categorizing construction projects into groups and subgroups has helped, but the fact remains that different construction projects possess different profiles of cost flow. This paper is an attempt at assessing the extent of influence of planning and programming the work on the cost flow curves. One real project was used as a case study and four planners were independently asked to produce programmes for executing the project. These programmes were analysed and converted to cost flow curves using one database of productivity and unit cost rates. Results confirmed that the variations in programmes produced less variations in cost flow curves than the errors to be expected from the use of average curves derived from project groups (mean SDY 2.88 compared to previous studies of 5.5, 8.5 and 10.67). The results suggest, taking due account of the limitations of the scope of the study, that further effort at the categorisation of projects into subgroups will result in a reasonable improvement in accuracy.
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Yan Yin, Xingming Xiao, Jiusheng Bao, Jinge Liu, Yuhao Lu and Yangyang Ji
The purpose of this study is to establish a new temperature set for characterizing the frictional temperature rise (FTR) of disc brakes. The FTR produced by braking is an…
Abstract
Purpose
The purpose of this study is to establish a new temperature set for characterizing the frictional temperature rise (FTR) of disc brakes. The FTR produced by braking is an important factor which directly affects the tribological properties of disc brakes. Presently, most existing researches characterize the FTR only by several static parameters such as average temperature or maximum temperature, which cannot reflect accurately the dynamic characteristics of temperature variation in the process of braking. In this paper, a new temperature parameter set was extracted and the influences of braking conditions on these parameters were investigated by experiments.
Design/methodology/approach
First, several simulated braking experiments of disc brakes were conducted to reveal the dynamic variation rules and mechanisms of the FTR in braking. Second, the characteristic parameter subset of the FTR was extracted with five significant parameters, namely, initial temperature, average temperature, end temperature, maximum temperature and the ratio of maximum temperature time. Furthermore, the fitting parameter subset of the FTR was constructed based on the temperature rise curve. Finally, the influence and mechanisms of initial braking velocity and braking pressure on the new temperature parameter set were investigated through braking experiments.
Findings
This paper extracted a new temperature parameter set including a characteristic parameter subset and a fitting parameter subset and revealed the influences of braking conditions on it by experiments.
Originality/value
The results showed that the new temperature parameter set extracted in this paper can characterize the dynamic characteristics of disc brake’s FTR variations more objectively and comprehensively. The research results will provide a theoretical basis for extracting the fault feature of friction properties.
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In this work, minimum energy based interpolation has been done for an optimal well path and effects of energy and fitting coefficients have been studied. An optimal well…
Abstract
In this work, minimum energy based interpolation has been done for an optimal well path and effects of energy and fitting coefficients have been studied. An optimal well path also maintains the given radius of curvature and satisfies drilling requirements. Interactive incremental design concept has been used in this work which, controls length and other geometric properties of well path. In the first part of the research, whole well segment has been taken for optimization. Geometric constraints are put to satisfy the drilling requirements. In the second part of the research only the curved portion of well segment has been taken for optimization. Geometric perspective of the well has been considered in this formulation.
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Xingyuan Wang, Zhifeng Lou, Xiaodong Wang, Yue Wang, Xiupeng Hao and Zhize Wang
The purpose of this paper is to design an automatic press-fit instrument to realize precision assembly and connection quality assessment of a small interference fitting…
Abstract
Purpose
The purpose of this paper is to design an automatic press-fit instrument to realize precision assembly and connection quality assessment of a small interference fitting parts, armature.
Design/methodology/approach
In this paper, an automatic press-fit instrument was developed for the technical problems of reliable clamping and positioning of the armature, automatic measurement and adjustment of the attitude and evaluation of the connection quality. To compensate for the installation error of the equipment, corresponding calibration method was proposed for each module of the instrument. Assembly strategies of axial displacement and perpendicularity were also proposed to ensure the assembly accuracy. A theoretical model was built to calculate the resistant force generated by the non-contact regions and then combined with the thick-walled cylinder theory to predict the press-fit curve.
Findings
The calibration method and assembly strategy proposed in this paper enable the press-fit instrument to achieve good alignment and assembly accuracy. A reasonable range of press-fit curve obtained from theoretical model can achieve the connection quality assessment.
Practical implications
This instrument has been used in an armature assembly project. The practical results show that this instrument can assemble the armature components with complex structures automatically, accurately, in high-efficiency and in high quality.
Originality/value
This paper provides a technical method to improve the assembly quality of small precision interference fitting parts and provides certain methodological guidelines for precision peg-in-hole assembly.
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Brijesh Upadhaya, Paavo Rasilo, Lauri Perkkiö, Paul Handgruber, Anouar Belahcen and Antero Arkkio
Improperly fitted parameters for the Jiles–Atherton (JA) hysteresis model can lead to non-physical hysteresis loops when ferromagnetic materials are simulated. This can be…
Abstract
Purpose
Improperly fitted parameters for the Jiles–Atherton (JA) hysteresis model can lead to non-physical hysteresis loops when ferromagnetic materials are simulated. This can be remedied by including a proper physical constraint in the parameter-fitting optimization algorithm. This paper aims to implement the constraint in the meta-heuristic simulated annealing (SA) optimization and Nelder–Mead simplex (NMS) algorithms to find JA model parameters that yield a physical hysteresis loop. The quasi-static B(H)-characteristics of a non-oriented (NO) silicon steel sheet are simulated, using existing measurements from a single sheet tester. Hysteresis loops received from the JA model under modified logistic function and piecewise cubic spline fitted to the average M(H) curve are compared against the measured minor and major hysteresis loops.
Design/methodology/approach
A physical constraint takes into account the anhysteretic susceptibility at the origin. This helps in the optimization decision-making, whether to accept or reject randomly generated parameters at a given iteration step. A combination of global and local heuristic optimization methods is used to determine the parameters of the JA hysteresis model. First, the SA method is applied and after that the NMS method is used in the process.
Findings
The implementation of a physical constraint improves the robustness of the parameter fitting and leads to more physical hysteresis loops. Modeling the anhysteretic magnetization by a spline fitted to the average of a measured major hysteresis loop provides a significantly better fit with the data than using analytical functions for the purpose. The results show that a modified logistic function can be considered a suitable anhysteretic (analytical) function for the NO silicon steel used in this paper. At high magnitude excitations, the average M(H) curve yields the proper fitting with the measured hysteresis loop. However, the parameters valid for the major hysteresis loop do not produce proper fitting for minor hysteresis loops.
Originality/value
The physical constraint is added in the SA and NMS optimization algorithms. The optimization algorithms are taken from the GNU Scientific Library, which is available from the GNU project. The methods described in this paper can be applied to estimate the physical parameters of the JA hysteresis model, particularly for the unidirectional alternating B(H) characteristics of NO silicon steel.
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Eric H. Grosse and Christoph H. Glock
The purpose of this paper is to study the prevalence of human learning in the order picking process in an experimental study. Further, it aims to compare alternative…
Abstract
Purpose
The purpose of this paper is to study the prevalence of human learning in the order picking process in an experimental study. Further, it aims to compare alternative learning curves from the literature and to assess which learning curves are most suitable to describe learning in order picking.
Design/methodology/approach
An experimental study was conducted at a manufacturer of household products. Empirical data was collected in the order picking process, and six learning curves were fitted to the data in a regression analysis.
Findings
It is shown that learning occurs in order picking, and that the learning curves of Wright, De Jong and Dar‐El et al. and the three‐parameter hyperbolic model are suitable to approximate the learning effect. The Stanford B model and the time constant model led to unrealistic results.
Practical implications
The results imply that human learning should be considered in planning the order picking process, for example in designing the layout of the warehouse or in setting up work schedules.
Originality/value
The paper is the first to study learning effects in order picking systems, and one of the few papers that use empirical data from an industrial application to study learning effects.
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