<![CDATA[Dry powder sieving is a crucial process for micro, small, and medium enterprises (MSMEs), where particle uniformity directly impacts product quality and production efficiency. Traditional vibrating sieving machines in local markets are typically evaluated through visual inspection, resulting in subjective assessments without quantitative evidence of energy efficiency or vibration stability. High humidity often causes powder clumping, reducing consistency and reliability. To address these limitations, this study introduces an Arduino based embedded system for quantitative performance and energy evaluation of a vibrating dry powder sieving process. System integrates an Atmega328P microcontroller (Arduino Uno) with infrared and DHT11 sensors, an L298N motor driver, a DC motor, and an LCD display. Electrical parameters (voltage and current) and vibration signals (acceleration along the X, Y, and Z axes) were acquired in real time at a sampling frequency of 10 Hz and recorded to an SD card for 60–90 seconds per cycle. Metrics included electrical power, energy consumption, vibration RMS, peak amplitude, dominant frequency, and energy efficiency expressed as the mass of powder sifted per joule of energy consumed. Experimental results, conducted using rice flour as a representative dry powder, showed that high humidity increased agglomeration, while a reciprocating motor at 210 RPM improved particle distribution across the sieve. The infrared sensor reduced energy consumption by activating the motor only when material was present. Overall, the system achieved an efficiency improvement exceeding 85% compared to manual sieving. This study demonstrates that embedded sensing and data acquisition can transform traditional sieving machines into objective, transparent, and reproducible systems for MSMEs, with potential application to various dry powders]]>
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