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Programming, Automation and Scripting
Published in Ionuţ Gabriel Ghionea, Cristian Ioan Tarbă, Saša Ćuković, CATIA v5, 2023
Ionuţ Gabriel Ghionea, Cristian Ioan Tarbă, Saša Ćuković
Once the sketch is created, two 3D operations follow – creating a solid using the Pad tool and drilling a hole on the solid’s upper face, having the coordinates of the centre in the origin. Both 3D entities, Pad and Hole, are created using an object called , which belongs to another Factory abstract object, as in Figure 5.11. This is how an object-type variable is declared. This object is used to make the Pad, , using the method, which has as parameters the and the extrusion height of 20.000000. Once the Pad is created, an update is run on the object, at . 142 : Dim shapeFactory1 As Factory 143 : Set shapeFactory1 = part1.ShapeFactory 144 : Dim pad1 As Pad 145 : Set pad1 = shapeFactory1. AddNewPad (sketch1, 20.000000) 146 : part1.Update
Real-Time Alarm Clock Using Arduino
Published in Anudeep Juluru, Shriram K. Vasudevan, T. S. Murugesh, fied!, 2023
Anudeep Juluru, Shriram K. Vasudevan, T. S. Murugesh
is used to change the pointer and is used to change the pointer. Initially, and pointers will be pointing to the first character of string. If the CHANGE push button is pressed once, then becomes and the pointer will be incremented by 1. If the CHANGE push button is pressed continuously, then the pointer also gets incremented continuously until it becomes 9. After that, the pointer is reset to 0 and the increment starts again from 0. The character in to which is pointing must be replaced with the modified pointer. But a character in cannot be directly replaced with the pointer as it is an type variable. Only a character can replace a character in the string. So, is used to convert the pointer to a character array and then replace the character in to which is pointing.
The Elements of Programming Style in Design Calculations
Published in Takushi Tanaka, Setsuo Ohsuga, Moonis Ali, Industrial and Engineering Applications of Artificial Intelligence and Expert Systems, 2022
Masanobu Umeda, Isao Nagasawa, Tatsuji Higuchi
Let’s consider a problem for computing the factorial of positive integer N in Figure 7 as an example of recursion. Figure 8 shows the definition of the module fact with the vector {N} of integer type variable N as the input and the vector {M} of integer type variable M as the output. The expression N=0 is evaluated. If this expression is true, then the value of M is the result.The value of M is set to 1. Therefore, if the expression in (a) is true, {1} will be the value of the module fact.Integer type variable M1 is declared.The expression N>0 is evaluated. If this expression is true, then the value of M is the result.The module fact is called with {N-1} as the input and {M1} as the output.The value of M is set to that of N*;M1. If the ex1 pression is true, then {M} will be the value of the module fact.
A comparison between the Bayesian network model and the logistic regression model in prevention of the defects on ceramic tiles
Published in Journal of Experimental & Theoretical Artificial Intelligence, 2022
Volkan Sevinç, Meryem Merve Kırca
The data were taken from a private ceramic factory. The data were derived from the quality control records kept in the factory by the quality control department. A data set including 1000 cases, which belong to the defective ceramics, were used. Among these defective ceramics, 70 burr, 114 crack, 64 drip, 74 deformation, 134 pinhole, 67 pitting, 79 glaze blistering, 122 colour tone, 64 black spot, 72 plucking, 58 tear, and 82 surface defects were observed. The research includes 14 variables determined by the expert opinion as the factors of production that are likely to affect the defect occurrences on ceramic tiles. These variables are size, glaze density, band speed, press surface moisture, glaze weight, engobe weight, engobe density, clay type, printing colour, drying temperature, firing time, firing temperature, post-drying moisture, and shift variables. In addition to these variables, there is another variable named defect type, which represents the 12 different defect types observed on ceramic tiles. These defect types are burr, crack, drip, deformation, pinhole, pitting, glaze blistering, colour tone, black spot, plucking, tear, and surface defect. In this study, the effects of the 14 variables on the defect type variable were examined.
ANN-based dynamic modulus models of asphalt mixtures with similar input variables as Hirsch and Witczak models
Published in International Journal of Pavement Engineering, 2022
Javilla Barugahare, Armen N. Amirkhanian, Feipeng Xiao, Serji N. Amirkhanian
The screened database contained 1656 |E*| values. This database was randomly divided into two datasets, the training and testing datasets in the ratio of 85:15. Table 1 shows a statistical description of all the variables used in this study. The mixture type variable was not added to Table 1. It was assumed that the input variables, i.e. VMA, VFA, ρ200, ρ4, ρ38, ρ34, Va & Vbeff captured the influence of the binders’ rheological properties, mixtures’ volumetric properties, and RAP on |E*| of HMA mixtures. This assumption enabled us to predict |E*| of all the asphalt mixtures (with or without RAP) using the same input variables. The performances of the investigated models were assessed using the coefficient of determination (R2) and the root mean square error (RMSE). Pellinen’s performance criteria was considered for ranking the performance of the models. This criterion labels excellent models as having an R2 greater than 0.90, good models with 0.70<R2 <0.89, fair models with 0.40<R2 <0.69, poor models with 0.20<R2 <0.39 and very poor models with R2 less than 0.19 (Pellinen 2002).
Optimizing mean and variance of multiresponse in a multistage manufacturing process using operational data
Published in Quality Engineering, 2020
Dong-Hee Lee, Jin-Kyung Yang, So-Hee Kim, Kwang-Jae Kim
A steel manufacturing process in Figure 2 clearly shows these two properties. It consists of four stages to produce reinforcing bars. Among the four stages, the steel making and continuous casting stages are optimized in this article and they are denoted as Stages 1 and 2, respectively. In Stage 1, iron scraps are melted to create molten iron. In Stage 2, the molten iron takes a solid shape, referred to as billets. The response variables of Stage 1 are operating time ( min) and molten iron temperature ( °C). Operating time is a smaller-the-better (STB) type variable, and molten iron temperature is a nominal-the-best (NTB) type variable. If the molten iron temperature is too high or too low, the equipment can be damaged, or billets are solidified prematurely. Thus, it is important to achieve the target temperature. The target value of is 1580 °C.