FPGA-Based Fused Smart-Sensor for Tool-Wear Area Quantitative Estimation in CNC Machine Inserts

Manufacturing processes are of great relevance nowadays, when there is a constant claim for better productivity with high quality at low cost. The contribution of this work is the development of a fused smart-sensor, based on FPGA to improve the online quantitative estimation of flank-wear area in CNC machine inserts from the information provided by two primary sensors: the monitoring current output of a servoamplifier, and a 3-axis accelerometer. Results from experimentation show that the fusion of both parameters makes it possible to obtain three times better accuracy when compared with the accuracy obtained from current and vibration signals, individually used

APLIKASI MODEL FUZZY WAVELET UNTUK MEMPREDIKSI NILAI TUKAR RUPIAH TERHADAP DOLLAR AMERIKA

Nilai tukar mempunyai implikasi yang luas, baik dalam konteks ekonomi domestik maupun internasional, mengingat hampir semua negara di dunia melakukan transaksi internasional. Mata uang Dollar Amerika merupakan mata uang yang dominan (hard currency) terutama untuk negara berkembang seperti Indonesia. Nilai tukar Rupiah terhadap Dollar Amerika Serikat merupakan salah satu indikator penting dalam menganalisis perekonomian Indonesia. Tujuan dari penelitian ini adalah menggunakan model fuzzy wavelet untuk memprediksi nilai tukar IDR/USD sehingga diharapkan mempermudah pelaku pasar dalam melakukan aksi jual-beli IDR/USD dan mengetahui keakuratan model fuzzy wavelet dalam memprediksi nilai tukar IDR/USD. Pemodelan fuzzy wavelet diawali dengan transformasi wavelet menggunakan Discrete Wavelet Transform (DWT) mother haar level 7 dan hasil DWT digunakan sebagai input fuzzy. Selanjutnya pemilihan input-output berdasarkan plot ACF dan membaginya menjadi data training sebanyak 120 data dan data testing sebanyak 30 data. Terdapat 5 himpunan fuzzy di setiap variabel input dan output. Metode inferensi yang digunakan dalam model ini yaitu Metode Mamdani dengan menggunakan defuzzifikasi centroid. Selanjutnya menentukan output akhir dengan menghitung nilai MAPE dan MSE yang diujikan pada data training dan data testing untuk mengetahui keakuratan model. Hasil penelitian menunjukkan bahwa model fuzzy wavelet memiliki nilai MAPE sebesar 2,79% untuk data training, sedangkan pada data testing sebesar 1,82%. Model fuzzy wavelet merupakan model terbaik untuk meramalkan data training, jika dibandingkan dengan model ARIMA dan Wavelet Double Exponential Smoothing. Kata Kunci: fuzzy wavelet, IDR/USD, nilai tukar, prediksi

Tool Condition Monitoring for Ball Nose Milling - A Model Based Approach

Ph.DDOCTOR OF PHILOSOPH

Tool condition monitoring - An intelligent integrated sensor approach

Ph.DDOCTOR OF PHILOSOPH

Characterisation of surface and sub-surface discontinuities in metals using pulsed eddy current sensors

Due primarily to today's rigorous safety standards the focus of non-destructive testing (NDT) has shifted from flaw detection to quantitative NIDT, where characterisation of flaws is the objective. This means information such as the type of flaw and its size is desired. The Pulsed Eddy Current (PEC) technique has been acknowledged as one of the potential contenders for providing this additional functionality, due to the potential richness of the information that it provides. The parameters mainly used to obtain information about the detected flaws are the signal's peak height and arrival time. However, it has been recognised that these features are not sufficient for defect classification. In this research, based on a comprehensive literature survey, the design of PEC systems and the interpretation of PEC signals, mainly for flaw classification, are studied. A PEC system consisting of both hardware and software components has been designed and constructed to facilitate the research work on PEC signal interpretation. After a comparative study of several magnetic sensing devices, probes using Hall device magnetic sensors have also been constructed. Some aspects related to probe design, such as coil dimensions and the use of ferrite core and shielding have also been studied. A new interpretation technique that uses the whole part of PEC responses and is able to produce more features has been proposed. The technique uses Principal Component Analysis (PCA) and Wavelet Transforms, and attempts to find the best features for discrimination from extracted time and frequency domain data. The simultaneous use of both temporal and spectral data is a logically promising extension to the use of time domain only with the signal-peak-based technique. Experiments show that the new 1 technique is promising as it performs significantly better than the conventional technique using peak value and peak time of PEC signals in the classification of flaws. A hierarchical structure for defect classification and quantification has been presented. Experiments in the project have also shown that the signal-peak-based technique cannot be used for flaw detection and characterisation in steels, both with and without magnetisation. The new proposed technique has shown to have potential for this purpose when magnetisation is used. The new technique proposed in the report has been successfully used for ferromagnetic and non-ferromagnetic materials. It has also been demonstrated that the new proposed technique performs better in dynamic behaviour tests, which shows its better potential for on-line dynamic NDT inspection which is required in many industrial applications. In addition to testing calibrated samples with different discontinuities, a study case using an aircraft lap joint sample from industry has further supported the statement regarding the potential of the new technique.EThOS - Electronic Theses Online ServiceGBUnited Kingdo