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Thermal error plays a deterministic role in the machining precision of computer-numericalcontrol (CNC) tool machinery. Previously, three ways had been proposed to overcome thermal error problems: prevention, restraint, and compensation. The first two ways may be performed in the initial design stage. The last one includes the challengeable features of caseby-case simulation of cutting paths, searching for characteristic temperature points, thermal deformation measurement, and establishing an accurate thermal model. Different from most of the previous studies concerning mathematical thermal models, which have many restrictions and disadvantages, in this study, we propose a novel hybrid thermal error modelling scheme of the Grey system theorem and deep-learning neural network. Specifically, a linear-guideway grinding machine, never seen in previous thermal-error-compensation-related studies, was chosen as the target to identify the usefulness of our proposed scheme. Results show that the proposed hybrid model has a comprehensive prediction ability of thermal behavior for the target CNC grinding machine.

Generally, vehicles driven by fuels, such as gasoline, diesel, and aviation oil, are equipped with water tanks. These vehicles include vans, cars, heavy locomotives, fire engines, ambulances, aircraft, and boats. The cooling water in these water tanks absorbs the heat generated by engines and then gradually heats up. Nowadays, the heated cooling water is usually channeled into a radiator and returned to the water tank until the heat is dissipated. This has caused the problem of energy waste in the cold-hot conversion process, which increases the environmental impact of the vechicles. Therefore, in this research, a thermal power generation device is designed with a simple structure, composed of a Pt100 thermocouple thermometer and a thermistor with an LM358 operational amplifier as two different sensing devices. The designed device is expected to increase the power generation capacity without using additional energy for cooling thermoelectric power generation chips. Therefore, it is of great importance to cool the surface of the chips. Thermoelectric power generation chips are installed at the four corners of the front and rear of a water tank. The thermal energy generated by the water tank is used to generate electric energy, which achieves the purpose of energy recycling. The designed device is cost-effective and can achieve the purpose of reducing environmental impact and energy conservation. This research can be used as a reference for vocational education from the aspects of technology, management, design, technicalization, and research and development.

For tool machinery, the most crucial factor affecting the machining precision is thermal deformation. Thus far, the most popular method of reducing thermal deformation has been considered as the compensation method, and many mathematical compensation methods have been proposed. However, attempts to develop a more comprehensive model are continuing. To improve the prediction accuracy, in this study, we propose a two-stage integrated data-mining scheme. The first stage, using rough set theory, focuses on how to manipulate the measured problem-dependent temperature and deformation data. The second stage, using a deep-learning neural network scheme, models the relationship between the temperature increase and the thermal error. Comparisons of the proposed method with other methods are also made. Results show that marked improvements are obtained using our proposed integrated data mining scheme.

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