Design and Development of An Optimized Automatic Flour Dosing Machine for SME Bakeries

Abstract

This study presents the design and development of an optimized automatic flour dosing machine to improve dosing accuracy, operational usability, and repeatability for small and medium-sized bakery enterprises (SMEs). The research integrates product design and development principles with mechanical flow optimization to address the limitations of conventional manual and semi-automated dosing systems. A six-stage Engineering Design Process (EDP) was employed, encompassing user needs identification, quality function deployment (QFD)-based specification development, concept generation and selection using a Pugh matrix, prototype fabrication, and experimental validation. The proposed system integrates an HX711-coupled load cell sensor, an HC-SR04 ultrasonic proximity sensor, an Arduino Uno microcontroller, and an optimized funnel–gate dispensing mechanism to regulate powder flow. Performance was evaluated using a multi-setpoint approach (10 g, 30 g, and 50 g) with 30 independent trials per setpoint. Results demonstrate that the system achieved a mean absolute percentage error (MAPE) of 1.54%, 1.32%, and 1.83% at 10 g, 30 g, and 50 g setpoints, respectively, all below the 2% threshold and well within the 5% design specification. The coefficient of variation (CV) remained below 2.3% across all conditions, confirming stable repeatability. The modular, DfA-compliant architecture enhances manufacturability and maintainability while preserving cost-effectiveness for SME adoption. These findings indicate that dosing performance in SME-scale food production systems is influenced not only by electronic control accuracy but also by the optimization of mechanical flow behavior. The study contributes theoretically by demonstrating the role of mechanical dispensing design as a complementary determinant of dosing precision, while providing SME practitioners with a low-cost and maintainable automation framework suitable for resource-constrained production environments.