HX711 Load Cell Calibration with Arduino for Accurate Weight Measurement

Digital signal filtering operates in two fundamental domains: time domain filtering removes noise and smooths signals, while frequency domain filtering separates or eliminates specific frequencies. Digital filters are categorized as feedforward (FIR) or feedback (IIR) types. Time domain signals like ECGs and accelerometer outputs contain information in amplitude and time variables, evaluated using step response analysis. Good time domain filters exhibit linear phase response with symmetric step response. The moving average filter is a classic FIR filter defined as convolution with a rectangular kernel of area 1, where hdn = 1/n for n between 0 and n-1. Computationally, it uses multiply-accumulate operations creating a sliding window that averages samples to reduce random noise through cancellation. Higher order filters reduce noise more effectively but attenuate high-frequency signal components.

A moving average filter is a fundamental DSP technique that calculates the average of data points to analyze and smooth signals. The filter can be implemented through convolution using the formula Y_a(1) = (1/m) × Σ(j=0 to M-1) of X(I+J), where m is the number of points. For symmetric implementation, limits change to J = -M/2 to M/2, requiring M to be odd. The filter effectively reduces signal distortion: an 11-point filter reduces distortion, while a 51-point filter can eliminate it completely. The frequency response is H(F) = sin(πFM) / (M × sin(πF)), describing how the filter affects different frequency components.

Digital filtering eliminates noise from sensor signals or smooths trends. Moving average calculates input averages over time intervals: fixed averages compute results after collecting N points, while dynamic averages recalculate at each sample by including new and excluding oldest points. A key trade-off exists: more points improve smoothing but increase delay. Memory requirements grow with window size, potentially causing resource issues.

Two filtering approaches are used: simple averaging (summing 16 samples and dividing by 16) and filtered averaging using a coefficient K=20. The filtered approach removes noise spikes and sudden drops by only accepting values within a certain range of the average. This reduces noise while maintaining responsiveness to actual signal changes.

Two primary software methods reduce noise: averaging and filtering. Averaging takes multiple readings (e.g., 10) in quick succession and calculates the mean, causing random high and low spikes to cancel out. Low-pass filters allow slow actual changes to pass through while blocking high-frequency noise, acting like a bouncer at a club. These techniques smooth data without requiring hardware changes.

This tutorial demonstrates how to send sensor data from an ESP32 microcontroller to the TagoIO cloud platform using the MQTT protocol. The process involves configuring WiFi and MQTT broker settings, packaging sensor data (such as acceleration values from an MPU-6050 sensor) into JSON format, and transmitting it through MQTT topics. The platform provides tools for monitoring data streams and creating interactive dashboards to visualize the received information in real-time.

This tutorial demonstrates how to publish sensor data from an ESP32 microcontroller to the cloud using the MQTT (Message Queuing Telemetry Transport) protocol, enabling remote monitoring and control of IoT devices such as lamps and electrical loads through mobile applications.

This video demonstrates how to connect various ESP8266-based modules (ESP-01, ESP-07, ESP-12E, ESP-12F, WeMos D1 mini, Goouuu Mini-S1, NodeMCU V2) and the ESP32 WROOM DevKit v1 to an IoT cloud platform using the Arduino IDE. The process involves setting up the Arduino IDE with the appropriate board manager and MQTT library, configuring WiFi credentials and MQTT broker settings in the code, creating a device object on the cloud platform with a matching client identifier, and programming the modules to transmit sensor data (such as temperature and humidity) to the platform. Different modules require different programming approaches: some need manual pin 0 manipulation to enter programming mode, while others feature built-in buttons or USB ports for easier programming.

AWS IoT Core is a managed cloud service enabling secure device-to-cloud communication. This tutorial demonstrates connecting an ESP32 microcontroller with a DHT11 sensor to AWS IoT Core. The ESP32 connects to local Wi-Fi and publishes sensor data (humidity/temperature) to the cloud. Data can also be received from the AWS dashboard. Required hardware includes an ESP32 board and DHT11 sensor. Software requirements include Arduino IDE with specific libraries: DHT library for sensor reading, WiFi client secure library, PubSubClient library for MQTT communication, and Arduino JSON library. The project involves creating an AWS account, setting up the development environment, and preparing hardware connections before proceeding to AWS configuration.

This tutorial demonstrates how to connect an ESP32 microcontroller to a cloud MQTT broker (using Primus Cloud) for bidirectional IoT communication. The process involves configuring WiFi connectivity, establishing MQTT client connections with proper server credentials, creating JSON payloads for data transmission, and implementing acknowledgment mechanisms to ensure reliable communication. The system enables ESP32 to send sensor data to Node-Red for visualization and receive control commands back, with separate topics for sending and receiving data to prevent conflicts.

Temperature compensation in strain gauge measurements is essential because temperature changes cause apparent strain through resistivity changes and dimensional expansion of the gauge material. The total change in resistance per actual resistance is given by ΔR/R = (αs - αg) × g + β × ΔT, where αs is the coefficient of linear expansion of the specimen, αg is the coefficient of linear expansion of the gauge material, g is the gauge factor, β is the coefficient of resistivity, and ΔT is the change in ambient temperature. Compensation can be achieved through temperature compensated gauges using two elements of different materials (like constantan and nickel) with opposite temperature coefficients, or through bridge arrangement by placing an identical compensating gauge in an adjacent arm of the bridge circuit. For semiconductor strain gauges, constant current excitation is required to maintain linearity in output voltage with respect to strain.

Strain gauge technique allows direct strain reading but is widely abused due to subtle factors affecting accuracy. Zero drift can reach 270 microstrain but can be reduced to 30 microstrain through proper material selection. Temperature affects strain gauges through two mechanisms: differential thermal expansion (ΔR/R = SG × (αs - αg) × ΔT) and direct resistance changes (TCR). These effects combine to produce total temperature-induced resistance changes that must be compensated for accurate measurements.

Temperature affects strain gauge measurements because resistance changes with temperature. In voltage divider configurations, temperature causes both offset and sensitivity errors. Compensation is achieved by adding a passive strain gauge in series with the active gauge, placed in a location experiencing the same temperature but no deformation. In Wheatstone bridge configurations, passive gauges are placed in positions where they experience identical temperature changes as the active gauges. When both configurations are properly compensated, temperature-dependent terms cancel out in the equations, leaving output voltage dependent only on deformation. This ensures stable measurements across temperature variations.

Temperature changes cause unwanted resistance changes in strain gauges that must be compensated. The temperature-induced change is ΔR_temp = R₀ × α × ΔT, where α is the temperature coefficient. For a 120Ω gauge with α = 0.004/°C, a 1°C change causes 0.48Ω change. The Wheatstone bridge converts resistance changes to measurable voltage. In a quarter bridge, one active gauge and one dummy gauge (temperature compensation) are used. Output: ΔV = -(V_s/4) × GF × strain. In a half bridge, two active gauges (one tensile, one compressive) double sensitivity: ΔV = -(V_s/2) × GF × strain. In a full bridge, four active gauges provide maximum sensitivity: ΔV = V_s × GF × strain. Dummy gauges compensate for temperature effects.

Creep is the time-dependent output drift under constant load, decaying exponentially with most drift occurring in the first 20 minutes. Compensation is achieved through longer end loops matching gauge response to flexure. Strain gauges consist of grid lines (for strain detection), end loops (anchoring), solder pads, fiducials, backing, and optional encapsulation. Grid alignment with principal strains is critical—misalignment causes nonlinearity. Moment compensation uses multiple gauges in the bridge to self-cancel off-axis effects. Strain gauge materials significantly impact performance: Constantan (copper-nickel) is easy to solder but has lower gauge factor and isn't suitable for high temperatures; Karma (nickel-chromium) enables self-modulus compensation without external trims, offers higher gauge factor, and performs reliably at elevated temperatures.

When multiple load cells of the same type and capacity need to be connected to a single digital indicator, they should be connected through a summing junction box, which combines all signal leads into a single output cable that connects to the instrumentation, making the system appear as if only one load cell is being used.

A summing box (jay box) combines multiple load cells into a single weighing system. It has 4 channels for connecting load cells, with two connection points—one for the indicator and another for expansion to create larger systems like 8-channel truck scales. The box collects signals from all load cells and sends them to the indicator, which calculates an average weight. Load cells have slight variations in output (e.g., 2.886 vs 3.010 mV/V), which the box's potentiometers calibrate to ensure consistent readings regardless of weight placement.

A load cell summing box (zero point calibration box) is an essential device for platforms, truck scales, and hopper scales that use multiple load cells, ensuring consistent measurement values regardless of load position. The box typically includes terminal blocks for load cell connections, indicator terminals, and trimming potentiometers for calibration. There are two main calibration methods: input voltage adjustment (most common, with wide adjustment range but temperature sensitivity) and output voltage adjustment (better temperature stability but narrower range). Calibration involves measuring each load cell's output voltage or indicator reading, identifying the lowest value, and adjusting other cells' potentiometers counterclockwise until all outputs match the lowest value.

A center of gravity scale can be constructed using four USB load cells mounted at the corners of a rigid aluminum plate, with cables routed through structural beams to a central USB hub; the system connects to PC/tablet software that calculates and displays the center of gravity coordinates in real-time based on force measurements from each load cell.

An industrial floor scale consists of four load cells mounted at each corner of the platform, each connected via cables to a summing board that aggregates the weight measurements, which then transmit data to a digital weight indicator; the load cells are mounted with a gap between the platform and the cell to allow slight bending under load, enabling accurate weight measurement.