The present disclosure provides a method for operating a molding system. The molding system includes a molding machine and a mold disposed on the molding machine, wherein the mold has a mold cavity for being filled with a molding material from the molding machine. The method comprises a step of obtaining a predetermined state waveform expressing a predetermined volumetric variation of the molding material. Next, the method further comprises obtaining a measured pressure and a measured temperature of the molding material in the mold cavity while performing a molding process for filling the molding material into the mold cavity. Next, the method includes obtaining a detected volumetric property of the molding material corresponding to the measured pressure and the measured temperature. Subsequently, the method comprises displaying the detected volumetric property of the molding material with the predetermined state waveform.

An automatic system level testing (ASLT) system for testing smart devices is disclosed. The system comprises a system controller coupled to a smart device in an enclosure, wherein the system controller comprises a memory comprising test logic and a processor. The enclosure comprises a plurality of components, wherein the processor is configured to automatically control the smart device and the plurality of components in accordance with the test logic. The plurality of components comprises: (a) a robotic arm comprising a stylus affixed thereto; and (b) a platform comprising a device holder affixed thereto, wherein the smart device is inserted into the device holder; and (c) a wireless access point. The processor is further configured to: (a) control the smart device to activate wireless mode; (b) receive wireless signals from the wireless access point using the smart device; (c) retrieve wireless scan results from the smart device; and (d) analyze the wireless scan results.

A method for calculating a real-time effective temperature of a solar photovoltaic module at any operating time during power generation period between a daily wake-up time and sleep time of said solar photovoltaic module is provided. The method is based on, in part, measuring the solar photovoltaic module open-circuit voltage at said daily wake-up time and measuring the solar photovoltaic module open-circuit voltage at said operating time.

An automatic system level testing (ASLT) system for testing smart devices is disclosed. The system comprises a system controller coupled to and operable to stress a smart device in an enclosure, wherein the enclosure comprises a plurality of components, and wherein the system controller comprises: (a) a memory comprising test logic; and (b) a processor configured to automatically control the plurality of components and test the smart device in accordance with the test logic. Further, the plurality of components comprises: (a) a robotic arm comprising a stylus affixed thereto, wherein the stylus is operable to manipulate the smart device; and (b) a platform comprising a device holder affixed thereto, wherein the device holder is operable to receive the smart device, and wherein the platform and the robotic arm are robotically controlled to move by the processor.

A machine learning device includes a model data selection unit to select, under a change in ambient temperature of a manufacturing machine, a learned model for additional learning of a thermal displacement compensation amount in each axis included in the manufacturing machine with respect to an operation state of the manufacturing machine, and a learned model storage unit to associate and store a pattern of an ambient temperature change curve indicating a transition of a change in the ambient temperature of the manufacturing machine and the learned model that is learned under the change in the ambient temperature. Based on the ambient temperature change curve stored in the learned model storage unit, the model data selection unit selects a learned model suitable for the additional learning of the thermal displacement compensation amount in each axis included in the manufacturing machine with respect to the operation state of the manufacturing machine.

An automatic system level testing (ASLT) system for testing smart devices is disclosed. The system comprises a system controller operable to be coupled with a smart device in an enclosure, wherein the system controller comprises a memory comprising test logic and a processor. The system also comprises the enclosure, wherein the enclosure comprises a plurality of components, the plurality of components comprising: (i) a robotic arm comprising a stylus, wherein the stylus is operable to manipulate the smart device to simulate human interaction therewith; and (ii) a platform comprising a device holder, wherein the device holder is operable to receive a smart device inserted there into. The processor is configured to automatically control the smart device and the plurality of components in accordance with the test logic.

A temperature-controlled electronic apparatus, comprises: a circuit board; a plurality of electronic components, mounted on the circuit board in an arrangement to form at least one electronic circuit; a temperature sensor, configured to measure a temperature of the at least one electronic circuit; and a heat-generating component, configured to be controlled by a temperature control circuit, the temperature control circuit being configured to control an amount of heat generated by the heat-generating component in response to the temperature measured by the temperature sensor. The plurality of electronic components are arranged on the circuit board to lie on one of one or more paths, each path of the one or more paths being defined by a respective circle having a radius.

To provide an abnormality determination apparatus, a computer readable medium, an abnormality determination system and an abnormality determination method which can simply detect an abnormality of a temperature in a machine tool without incurring cost. An abnormality determination apparatus which determines abnormality of a temperature sensor in a plurality of machine tools, in which the plurality of machine tools is equivalent machine type, is arranged in equivalent environments, are provided with a temperature sensor at equivalent positions, the abnormality determination apparatus including: a temperature data acquisition unit which acquires temperature data outputted by the temperature sensors from each of the plurality of machine tools; a comparison unit which compares the temperature data acquired by the temperature data acquisition unit; and an abnormality determination unit which determines abnormality of the temperature sensor based on a comparison result by the comparison unit.

An abnormality determination apparatus, which determines abnormality of a temperature sensor in a machine tool, includes: a temperature pattern storage unit which stores a normal pattern indicating temperature change relative to machining time for a machining classification of the machine tool; a temperature data acquisition unit which acquires temperature data outputted by the temperature sensor provided to the machine tool; a machining classification determination unit which determines a machining classification of the machine tool; a comparison data acquisition unit which extracts the normal pattern relative to a determined machining classification from the temperature pattern storage unit; a comparison unit which compares the extracted normal pattern, and as acquired pattern indicating temperature change relative to machining time according to the acquired temperature data; and an abnormality determination unit which determines abnormality of a temperature sensor based on a comparison result.

An accuracy diagnostic device that diagnoses an influence on an accuracy of a machine tool due to a thermal deformation includes a temperature change rate calculation unit, an accuracy influence degree calculation unit, and an accuracy stabilization time period calculation unit. The temperature change rate calculation unit calculates a rate of a temperature change in a predetermined portion of the machine tool as a temperature change rate. The accuracy influence degree calculation unit calculates an influence degree on the machine tool accuracy due to the thermal deformation as an accuracy influence degree, based on the temperature change rate. The accuracy stabilization time period calculation unit calculates a time period until the machine tool accuracy is stabilized as an accuracy stabilization time period, based on the temperature change rate.