AZAPA Quality Engineering tool helps streamline experiments and measurements, and assists in creating statistical models.
【Statistical and Quality Engineering Based MATLAB Data Analysis Toolset】 〇AZP-T method The T method (Taguchi Method) is a type of regression analysis in machine learning and is an effective analytical technique for prediction problems and calculating contribution rates. The analysis flow involves calculating the contribution degree (SN ratio) and sensitivity of each item from the training data, and using these to determine the estimation formula for the signal. This estimation formula is then used to predict the values of unknown signals. 〇AZP-MT method The MT method (MT-method) is a type of multivariate analysis and is an effective analytical technique for classification problems. The analysis flow begins by loading good product data as unit data to create a unit space. Next, defective product data is loaded as signal data to calculate the MD value. This MD value is used to distinguish between good and defective products. 〇AZP-PD (Parameter Design) Parameter design is a design methodology aimed at optimizing the functionality of a system. To prevent issues in the market, each design parameter that constitutes the system is determined using statistical thinking to ensure robustness against disturbance factors and to maximize performance.
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"Utilization Methods of Measurement Data for Model-Based Development Using AZP-QE" As part of efforts to improve development efficiency and quality, the introduction of cyber-physical systems, including model-based development, is progressing. This system can be realized by unraveling physical phenomena, formalizing them into equations, and reproducing them in a virtual space (Cyber). However, there are phenomena that are difficult to formalize and those that are understood through subjective and intuitive behavior based on past measurement data. Therefore, AZAPA promotes the effective use of measurement data tailored to development objectives by developing equivalent 1D models of physical phenomena from measurement data through statistical analysis.
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〇Efficiency of Experiments and Measurements In system development, experiments and measurements are necessary to confirm whether the product functions as intended. However, we often hear questions like, "Which items should we measure, at what range/resolution, in what patterns, and how many times should we measure?" With past data and a simple model, this tool enables the creation of a logical testing plan. 〇Creation of Statistical Models In departments conducting experiments and measurements, we often hear concerns such as, "We are doing many retests every time there is a design change, but can't we predict test results from past measurement data?" If sufficient experiments and measurements have been conducted to ensure accurate prediction of results, retesting may not be necessary. Why not try to see if your available data can be utilized?
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AZAPA Corporation considers the question, "What should Japan's existence be in light of economic issues?" and aims to lead industries towards independence and a departure from zero resources through the provision of technology, thereby contributing to the formation of economic markets. With "advanced technological capabilities and rich creativity," we will initiate continuous innovation. Since July 2020, we have implemented an internal company system and provide extensive support across four business axes: "Control," "Model-Based," "Measurement," and "Service," ranging from product development support to service deployment, which connects us with end users. #ModelBasedDevelopment #ModelBased #MBD #ControlModel #Control #AutonomousDriving #ADAS #Education #Seminars #MATLAB #Simulink #SensoryEvaluation #Sensitivity #Emotion #MaaS
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