Taguchi Loss Function Pada Ketidakseimbangan Beban Transformator 3 Phasa (Studi Kasus Transformator 3 Phase PT Smart Tbk)

Ketidakseimbangan beban listrik 3φ, 50Hz, 380/220 V dari pasokan transformator dapat menyebabkan penurunan kinerja transformator, gangguan pada setiap penghantar fasa (R, S, T), dan timbulnya arus mengalir pada penghantar netral (Arus Netral) dan penghantar tanah (Arus Grounding). Arus yang mengalir pada penghantar netral dan grounding menyebabkan kerugian (losses) biaya arus netral dan arus grounding, sedangkan ketidakseimbangan beban pada setiap fasanya dapat menyebabkan kerugian biaya arus fasa dari transformator menuju beban. Penelitian ini adalah mengkaji seberapa besar kerugian akibat ketidakseimbangan beban dan kerugian kualitas (Quality Loss Function) pada jaringan listrik dari transformator ke beban. Metode yang digunakan untuk menentukan kerugian kualitas listrik pada ketidakseimbangan beban adalah Taguchi Loss Function, perhitungan ini dilakukan sebagai upaya untuk mengetahui besar kerugian kualitas dengan adanya arus yang mengalir akibat ketidakseimbangan beban. Diperoleh besara biaya kerugian kualitas akibat ketidakseimbangan beban adalah Rp 97.690.717,31 per tahun dan total besar biaya kerugian (losses) listrik adalah Rp 104.729.414,40 per tahun

OPTIMUM MANAGEMENT IN ACHIEVING THE INDUSTRIAL QUALITY GOALS

A mathematical model for targeted control in the field of quality, which is a system of ordinary differential equations with constant coefficients, is considered. The solutions of this system are given for the most common stepwise and linear control laws in practice. An example of optimization based on the classical quadratic functional corresponding to the Taguti quality loss function is considered, using actual data based on the results of an industrial enterprise.

OPTIMIZING THE TLD-100H READOUT SYSTEM UNDER VARIOUS RADIOACTIVE I-131 DOSES VIA THE REVISED TAGUCHI DYNAMIC QUALITY LOSS FUNCTION

The TLD-100H readout system performance under various radioactive I-131 exposure doses was optimized by four key factors via the revised Taguchi dynamic quality loss function. Taguchi dynamic analysis and the orthogonal array reorganizing the essential factors are crucial for the optimization of the thermoluminescent dosimeter (TLD) readout system given strict criteria of multiple irradiated environments and long-term exposure for calibrated TLDs. Accordingly, 96 TLD-100H chips were selected and randomly categorized into three batches with eight groups (four TLD chips in each group). Four factors, namely (1) initial temperature, (2) heating rate, (3) maximal temperature, and (4) TLD preheat time before reading were organized into eight combinations according to Taguchi suggestion, whereas each factor was preset at two levels. All 96 [Formula: see text] chips were put in three concentric circles with 30, 60, and 90 cm radii for 48 h, surrounding the radioactive 150[Formula: see text]mCi ([Formula: see text][Formula: see text]MBq) I-131 capsule and exposed to the cumulative doses of 88.2, 18.6, and 8.6[Formula: see text]mSv for the respective radii, accordingly. The TLD readings obtained from each group were analyzed to derive the sensitivity, coincidence, and reproducibility, then those were reorganized to draw four fish-bone-plots for the optimization. The optimal option for the TLD readout system implied the combination of A1 (a [Formula: see text]C initial temperature), B1 (a [Formula: see text]C/s heating rate), C1 (a [Formula: see text]C maximal temperature), and D2 (a 15[Formula: see text]s preheat time), which was further verified by the follow-up measurements. The dominant factors were A (initial temperature) and B (heating rate), whereas C (maximal temperature) and D (preheat time) were minor and provided negligible contributions to the system performance optimization.

Robust Product Design: A Modern View of Quality Engineering in Manufacturing Systems

One of the main technological and economic challenges for an engineer is designing high-quality products in manufacturing processes. Most of these processes involve a large number of variables included the setting of controllable (design) and uncontrollable (noise) variables. Robust Design (RD) method uses a collection of mathematical and statistical tools to study a large number of variables in the process with a minimum value of computational cost. Robust design method tries to make high-quality products according to customers’ viewpoints with an acceptable profit margin. This paper aims to provide a brief up-to-date review of the latest development of RD method particularly applied in manufacturing systems. The basic concepts of the quality loss function, orthogonal array, and crossed array design are explained. According to robust design approach, two classifications are presented, first for different types of factors, and second for different types of data. This classification plays an important role in determining the number of necessity replications for experiments and choose the best method for analyzing data. In addition, the combination of RD method with some other optimization methods applied in designing and optimizing of processes are discussed.