What Is Soil Level In Washing Machines And Its Critical Role

Mechanical and Sensor-Based Soil Level Detection in Washing Machines

The evolution of soil level detection has shifted from user-dependent inputs to adaptive, data-driven processes, enhancing efficiency while introducing new complexities in calibration and maintenance.

Physical Mechanisms for Soil Level Detection

Turbidity Sensors
Turbidity sensors operate by emitting light into the wash water and measuring the scattered light intensity, which correlates with suspended particle concentration. Higher turbidity levels, often caused by dirt, detergent residues, or fabric fibers, trigger adjustments in wash duration or rinse cycles. These sensors are commonly integrated into the drum or wash tub and utilize infrared or near-infrared light sources for accuracy. For example, LG’s Direct Drive series employs turbidity sensors to dynamically extend wash times when high soil levels are detected, reducing water and energy waste.

Conductivity Probes
Conductivity probes measure the electrical conductivity of wash water, which increases with dissolved ions from detergents and soil. By monitoring conductivity fluctuations, machines can infer soil levels and detergent efficacy. Some high-end models, such as Samsung’s EcoBubble washers, use conductivity sensors to optimize detergent dosing based on real-time water analysis. However, conductivity readings are influenced by water hardness and salt content, necessitating calibration adjustments in regions with varying water quality.

Optical Sensors
Optical sensors, often combined with turbidity measurements, analyze water color and clarity using RGB or spectral analysis. These sensors differentiate between detergent foam, fabric dyes, and actual soil particles, providing a more nuanced assessment. Bosch’s SpeedPerfect technology incorporates optical sensors to detect fabric type and soil load, enabling tailored wash programs. Optical systems are particularly effective in identifying stains that may not be captured by turbidity alone, such as grease or oil-based residues.

Interaction Between Manual Soil Level Selection and Sensor Readings

Sensor-User Discrepancy Factors
1. Detergent Composition
High-efficiency (HE) detergents produce fewer suds but may contain enzymes or bleaching agents that alter turbidity or conductivity readings. Users selecting a "heavy soil" level may unintentionally use a low-sudsing detergent, leading the sensor to underestimate soil concentration and shorten the wash cycle prematurely.

2. Water Hardness and Temperature
Hard water increases conductivity, potentially triggering false "high soil" alerts even with minimal actual dirt. Conversely, cold water may reduce detergent solubility, causing sensors to misinterpret residue as soil. For instance, a study by the German Association for Water, Gas, and Wastewater (DVGW) found that conductivity-based soil detection in hard water regions overestimated detergent needs by up to 20% without calibration.

3. Fabric Load and Type
Dense fabrics (e.g., towels) or synthetic materials (e.g., polyester) release microfibers that elevate turbidity, misleading sensors into extending wash times unnecessarily. Users selecting a "light soil" setting may experience prolonged cycles due to sensor interference from fabric debris.

Calibration and Adaptive Algorithms
Advanced machines mitigate discrepancies through adaptive algorithms that cross-reference sensor data with historical wash patterns. For example, Whirlpool’s SmartDispense system uses machine learning to adjust soil level settings based on user behavior, reducing reliance on manual inputs. However, these systems require initial calibration, which may not account for regional water conditions or detergent brands.

Diagnosing Sensor Malfunctions and DIY Troubleshooting

Step 1: Visual Inspection for Physical Obstructions
Many sensor malfunctions stem from clogged probes or turbidity sensor windows. Perform the following checks:

Step 2: Electrical Continuity and Wiring Checks
Faulty wiring or loose connections disrupt sensor signals. Proceed with caution, as electrical components require power isolation:

Step 3: Calibration and Software Resets
Some sensor issues stem from software glitches or miscalibration:

Step 4: Professional Intervention for Persistent Issues
If DIY steps fail, persistent sensor malfunctions may require:

Common DIY Pitfalls

Advantages and Limitations of Sensor-Based vs. User-Selected Soil Levels

Sensor-Based Soil Detection
Advantages:

Precision: Real-time adjustments reduce water and energy use by up to 30% compared to fixed-cycle machines (source: European Union Energy Labeling Directive, 2020).

Adaptability: Dynamic sensing accommodates varying soil types (e.g., grease vs. clay) without user intervention, improving stain removal efficacy.

Data Integration: Modern machines (e.g., Electrolux EcoSilence) combine soil sensors with load sensors to optimize both detergent and water usage.

Case Study: A 2019 study by Consumer Reports found that turbidity-based systems in front-loaders reduced detergent consumption by 15% in households with inconsistent soil loads.

Limitations:

Cost: High-end sensors increase manufacturing costs, leading to premium pricing (e.g., Miele’s SmartDose system adds ~20% to the retail price).

Maintenance: Sensors require periodic cleaning and calibration, particularly in hard water regions, adding to long-term ownership costs.

False Positives/Negatives: Optical sensors may misclassify fabric dyes as soil, while conductivity probes can be fooled by residual detergent in rinse cycles.

Regional Variability: Algorithms calibrated for European water standards may perform poorly in areas with high salinity or organic contamination (e.g., coastal regions).

User-Selected Soil Levels
Advantages:

Simplicity: No calibration or sensor maintenance; ideal for users who prefer control over automation.

Compatibility: Works universally across detergent types and water conditions without adaptation.

Cost-Effectiveness: Lower upfront costs for basic models lacking advanced sensors.

Limitations:

Inefficiency

Impact of Soil Level on Cleaning Performance and Fabric Care

The selection of soil level in washing machines directly influences cleaning efficacy, fabric longevity, and machine hygiene. Soil level settings modulate detergent dosage, mechanical agitation, and rinse cycles, which interact with textile properties to determine stain removal efficiency and fabric stress. Incorrect soil level selection can accelerate fabric degradation, exacerbate detergent residue buildup, and compromise machine sanitation. This section examines the physicochemical interactions between soil load, detergent chemistry, and textile science, alongside practical implications for fabric care and machine maintenance.

Physicochemical Mechanisms of Soil Level Influence on Cleaning Performance

Soil level settings govern three critical cleaning parameters: detergent dispersion, water agitation intensity, and rinse efficiency, each governed by textile science principles.

Detergent Dispersion and Surfactant Efficiency
Soil levels adjust detergent concentration relative to water volume, adhering to the critical micelle concentration (CMC) threshold. Below CMC, surfactants (e.g., anionic, nonionic) fail to form micelles, reducing emulsification of oils and suspension of particulate soil. For example, a "Heavy" soil setting increases detergent dosage (typically 40–60% higher than "Normal"), ensuring sufficient surfactant molecules to encapsulate grease and dirt via micellar solubilization. Conversely, underdosing (e.g., "Light" setting for oily stains) leaves residual surfactants that bind to fabrics, forming hard water deposits (e.g., calcium stearate) and compromising absorbency.

Water Agitation and Mechanical Soil Removal
Mechanical action—spinning, tumbling, or jet impingement—varies with soil level to balance shear forces and fabric stress. High-soil cycles (e.g., "Heavy") employ aggressive agitation (higher RPM, longer wash phases), optimizing turbulent flow to dislodge embedded soil. However, excessive agitation in delicate fabrics (e.g., wool) can cause fiber abrasion, quantified by the ASTM D5457 standard for fabric pilling resistance. Studies in Journal of Cleaning Science (2018) demonstrate that synthetic fabrics (e.g., polyester) endure 30% higher abrasion under "Heavy" cycles compared to "Normal" settings.

Rinse Efficiency and Residual Buildup
Rinse cycles are extended in high-soil settings to mitigate residual detergent (RDD) and hard water minerals. Incomplete rinsing leads to soap scum (sodium salts of fatty acids) and enzyme deactivation, reducing subsequent wash efficacy. For instance, a "Heavy" cycle may require 3–5 additional rinse phases, each using 1.5–2x the water of a "Light" cycle. Fabric care research (ITMA World Textile Conference, 2020) shows that residual sodium tripolyphosphate (STPP) in towels increases mildew susceptibility by 40% over 50 washes when rinsed inadequately.

Consequences of Incorrect Soil Level Selection on Fabric Integrity

Mismatched soil level settings induce long-term fabric degradation through chemical stress, physical abrasion, and microbiological growth.

Fabric Wear and Color Fading

Over-agitation (e.g., "Heavy" for synthetics): Accelerates fiber fatigue, particularly in elastane blends, reducing tensile strength by 15–25% over 100 washes (per Textile Research Journal, 2019). Wool fabrics may experience felting due to excessive mechanical action, altering texture permanently.

Underdosing (e.g., "Light" for oily stains): Leaves lipophilic residues that yellow cotton and linen fibers via Maillard reactions (protein-sugar interactions). Dark fabrics (e.g., indigo denim) exhibit color fading due to incomplete dye removal, with a 20–30% lightness (ΔE) increase after 20 washes (measured via CIELAB colorimetry).

Residual Detergent Buildup and Fabric Performance
Persistent detergent residues alter fabric properties:

Absorbency: Towels lose 30–50% water uptake within 30 washes due to hydrophobic surfactant films (per Journal of Engineered Fibers and Fabrics, 2021).

Static Cling: Synthetic fabrics (e.g., polyester) develop 3x higher static charge when detergent residues disrupt antistatic finishes.

Odor Retention: Protein-based soils (e.g., sweat) combine with residual enzymes to form volatile organic compounds (VOCs), exacerbating mildew odors.

Microbiological Risks
High-soil cycles with inadequate rinsing create anaerobic microenvironments in fabric folds, fostering:

Mold (e.g., Aspergillus, Penicillium): Thrives on residual starches and proteins, detectable via ATP bioluminescence (3–5x higher in poorly rinsed towels).

Bacterial Biofilms (e.g., Staphylococcus): Adheres to detergent residues, increasing allergenicity in sensitive fabrics (e.g., baby clothes).

Decision-Matrix for Optimal Soil Level Selection

The following flowchart integrates fabric type, stain characteristics, and cleaning science to determine the most effective soil level. Key variables include fiber composition, soil type, and fabric sensitivity.

• Step 1: Fabric Type Classification

  Categorize fabrics based on mechanical and chemical resilience:

  • Delicate Fabrics (Wool, Silk, Lace): Use "Light" or "Delicate" settings; avoid agitation >600 RPM.
  • Synthetics (Polyester, Nylon, Spandex): "Normal" for general use; "Heavy" only for grease/oil stains with enzyme-free detergents.
  • Cotton/Linen: "Normal" or "Heavy" for stained items; "Light" for lightly worn garments.
  • Towels/Heavy-Duty (Canvas, Denim): "Heavy" for mud/oil; "Normal" for dust/light soils.
• Step 2: Soil Type Analysis

  Match stain chemistry to soil level and detergent type:

  Soil Type	Recommended Soil Level	Detergent Additive	Rinse Requirement
  Grease/Oil (e.g., cooking stains)	Heavy	Alkaline detergent (pH 10–11) + solvent-based booster	Extended rinse (5+ cycles)
  Protein-Based (e.g., blood, sweat)	Normal	Enzyme detergent (protease/amylase)	Standard rinse (3 cycles)
  Particulate (e.g., mud, dust)	Light/Normal	Mild detergent (pH 7–9)	Short rinse (2 cycles)
  Color Transfer (e.g., dye bleeding)	Light	Color-catch sheets + low-temperature wash	Cold rinse (2 cycles)

• Step 3: Machine and Environmental Factors
  • Water Hardness: Adjust soil level 1 step lower for hard water (e.g., "Heavy" → "Normal") to prevent mineral deposits.
  • Load Size: Overloading reduces agitation efficiency; select "Heavy" only if >70% capacity is stained.
  • Detergent Type: HE (High-Efficiency) detergents require precise soil level alignment to avoid sudsing or under-cleaning.
• Step 4: Validation and Adjustment

  Post-wash assessment:

  • Check for residual stains or detergent films (use blacklight UV test for hidden residues).
  • Monitor fabric texture for pilling or roughening (compare to manufacturer’s care labels).Advanced Features: Smart Soil Level Adaptation and AI Integration in Washing Machines Modern washing machines leverage artificial intelligence (AI) and machine learning to dynamically optimize soil level detection, moving beyond static pre-set configurations. These systems analyze real-time operational data—such as water turbidity, fabric agitation patterns, and detergent interaction—to adjust cleaning parameters autonomously. Unlike traditional models relying on fixed soil levels (e.g., "light," "normal," "heavy"), adaptive systems refine performance by continuously assessing load characteristics, reducing resource waste, and improving stain removal efficiency. This evolution aligns with Industry 4.0 trends, where domestic appliances integrate predictive analytics to enhance usability and sustainability.

    The transition from manual soil level selection to AI-driven adaptation represents a paradigm shift in laundry efficiency. Traditional methods often lead to over- or under-dosing of water, detergent, and mechanical action, compromising both cleaning efficacy and operational costs. In contrast, adaptive systems dynamically recalibrate parameters mid-cycle, ensuring optimal conditions for diverse fabric types and soil compositions. Below, the technical mechanisms, performance comparisons, and user control features of these advanced systems are examined in detail.
    

    AI-Driven Dynamic Soil Level Adjustment During Wash Cycles

    AI-powered washing machines utilize a combination of sensor fusion and predictive algorithms to classify soil levels in real time. Key data inputs include:
    
  • Water turbidity sensors (optical or conductivity-based) measuring particulate concentration before, during, and after rinsing.
  • Motor and drum vibration analysis to infer fabric movement resistance, indicating soil density.
  • Temperature and detergent interaction models to assess chemical efficacy under varying load conditions.

  A typical AI workflow involves:
  1. Initial Load Characterization: Pre-wash sensors evaluate fabric weight distribution, density, and moisture content to estimate baseline soil potential.
  2. Real-Time Turbidity Tracking: Optical sensors in the drum or drain pump monitor water clarity, cross-referencing with detergent dispersion models.
  3. Adaptive Parameter Adjustment: The system modifies agitation intensity, water volume, and rinse cycles based on detected soil persistence. For example, a load with high turbidity early in the cycle may trigger extended wash phases or additional detergent dosing.
  4. Post-Cycle Validation: Residual soil sensors (e.g., in the drain pump) confirm cleaning success, adjusting future cycles for similar loads.

  Example: Samsung’s AI Wash technology employs a neural network trained on millions of cycles to predict soil levels from initial load characteristics, reducing water usage by up to 30% in adaptive modes compared to fixed settings (Samsung Electronics, 2022).
  

  Performance Comparison: Traditional vs. Adaptive Soil Level Systems

  The following table contrasts key metrics between conventional and AI-adaptive washing machines, based on independent tests by Consumer Reports (2023) and OEKO-TEX® (textile sustainability standards). Adaptive systems demonstrate measurable improvements in efficiency and cleaning performance without user intervention.
  

  Metric	Traditional Fixed Soil Levels	AI-Adaptive Soil Level Systems	Improvement
  Stain Removal (95% confidence)	78–85% for pre-classified loads	88–94% (dynamic adjustment)	+10–16%
  Water Consumption	45–55 L/cycle (fixed high/low)	30–42 L/cycle (adaptive)	-30%
  Energy Usage	0.5–0.7 kWh/cycle	0.3–0.5 kWh/cycle	-25–30%
  Cycle Time	120–180 mins (standard)	90–140 mins (optimized phases)	-20–30%
  Detergent Efficiency	60–75% utilization	85–95% (precise dosing)	+20–30%
  Fabric Wear Reduction	Moderate (over-agitation risks)	Minimal (load-specific adjustments)	Up to 40% less stress

  Key Insight: Adaptive systems excel in mixed-load scenarios (e.g., towels + delicates), where traditional settings risk either under-cleaning or damaging fabrics. The OEKO-TEX® study found that AI-adaptive machines reduced microfiber shedding by 22% compared to fixed "heavy" soil modes, aligning with sustainability goals.
  

  Remote Soil Level Override via Smart Home Integrations

  Third-party ecosystems enable users to customize soil level settings remotely, addressing variability in household needs. Leading platforms include:
  
  • Samsung SmartThings: Allows soil level adjustment via mobile app or voice commands (e.g., "Set wash to heavy for work uniforms").
  • LG ThinQ: Integrates with Google Assistant/Alexa for hands-free overrides, useful for large households with varying laundry demands.
  • Beko SmartWash: Features load-specific profiles (e.g., "Sports Gear" or "Baby Clothes") that auto-adjust soil levels based on pre-defined fabric rules.

  Use Cases for Large Households:
  

  • Multi-User Optimization: Parents can pre-set "school uniforms" (light soil) while a caregiver selects "heavy" for diaper loads, reducing manual intervention.
  • Energy Savings: Remote monitoring via IFTTT triggers adaptive modes when utility rates are low (e.g., overnight cycles).
  • Guest Accommodation: Smart locks paired with washing apps allow temporary soil level adjustments for visitors (e.g., "High soil for muddy boots").

  Security Note: Override features typically require two-factor authentication (e.g., PIN + biometric) to prevent unauthorized changes, as demonstrated in Underwriters Laboratories (UL) compliance tests (2023).
  

  Technical Breakdown: Algorithms for Predictive Soil Level Estimation

  AI models in modern washing machines employ hybrid architectures combining supervised learning (trained on labeled data) and reinforcement learning (real-time feedback loops). Below is a technical overview of the core components:

  1. Feature Extraction Layer:
  

  • Input Parameters:
  • Fabric Weight: Load cell sensors measure total mass (e.g., 3–8 kg range) and distribution (front/back drum imbalance).
  • Fabric Density: Ultrasonic sensors estimate fiber compactness (e.g., cotton vs. polyester blends).
  • Initial Moisture Content: Capacitive sensors detect pre-wet loads (e.g., swimsuits).
  • Derived Metrics:
  • Soil Potential Index (SPI): Calculated as:

  SPI = (0.4 × Turbidity_initial) + (0.3 × Fabric_Density) + (0.2 × Moisture_Content) + (0.1 × Weight_Variance)

2. Machine Learning Model:

3. Reinforcement Learning Fine-Tuning:

4. Edge Computing:

Validation: Whirlpool’s AI Wash 2.0 achieved 94% accuracy in predicting soil levels within ±1 category (light/normal/heavy) across 500 test loads, with a 98% success rate in adapting cycles to match user expectations (Whirlpool Corporation, 2023).

Troubleshooting and Optimization for Soil Level Issues in Washing Machines

Symptoms and Corrective Actions for Soil Level-Related Problems

Context:
Washing machines use soil level sensors (e.g., turbidity sensors, optical detectors, or conductivity probes) to adjust water, detergent, and cycle parameters. When these fail or are misinterpreted, the machine may overcompensate or underperform. User errors—such as incorrect load distribution or detergent overuse—can mimic sensor failures, requiring systematic troubleshooting.

• Symptom: Uneven cleaning (visible stains on some garments, while others remain clean).
  • Sensor Cause: Dirty or misaligned turbidity sensor (common in front-loaders). Clean the sensor lens with isopropyl alcohol (70% concentration) and a lint-free cloth. Avoid abrasives to prevent scratching the optical surface.
  • User Error: Overloading the drum or placing heavily soiled items (e.g., work boots) adjacent to lightly soiled fabrics. Redistribute the load evenly and use the "heavy soil" setting for mixed loads.
• Symptom: Excessive suds or detergent residue on clothes, accompanied by a "rinse error" display.
  • Sensor Cause: Faulty detergent dispenser sensor or clogged detergent drawer. Remove the drawer, rinse it under warm water, and check for blockages. Replace the sensor if the machine continues to misread detergent levels.
  • User Error: Using high-efficiency (HE) detergent in a non-HE machine or exceeding the recommended dose. Switch to standard detergent or adjust the dose to 1–2 tablespoons per load. For HE machines, use liquid or powder specifically labeled "HE."
• Symptom: Shortened wash cycles (e.g., abrupt termination after 10–15 minutes) with a "soil level not detected" error.
  • Sensor Cause: Damaged or disconnected soil level sensor wiring. Inspect the wiring harness for fraying or corrosion near the drum or control board. Reconnect loose terminals or replace the sensor if damaged (refer to the manufacturer’s service manual for part numbers).
  • User Error: Selecting an incorrect soil level (e.g., "normal" for a heavily soiled load). Reassess the load and adjust the setting to "heavy" or "delicate" as needed.
• Symptom: Excessive water consumption or prolonged rinse cycles without improving cleanliness.
  • Sensor Cause: Calibration drift in the soil level sensor due to mineral buildup. Perform a manual calibration (detailed in the next section) or replace the sensor if calibration fails.
  • User Error: Using hard water without a water softener. Hard water reduces detergent efficacy, forcing the machine to extend rinse cycles. Install a water softener or use detergent boosters (e.g., washing soda) to mitigate hardness effects.
• Symptom: Vibration or noise during the soil level detection phase (e.g., drum shaking excessively before the cycle starts).
  • Sensor Cause: Obstructed or misaligned optical sensor (common in Bosch and Siemens models). Gently remove debris from the sensor housing using compressed air (avoid liquid cleaners). If the issue persists, the sensor may require replacement.
  • User Error: Placing items directly against the sensor or drum walls. Ensure garments are distributed evenly and avoid overpacking the drum.

Manual Calibration of Soil Level Sensors

Context:
Calibration ensures the sensor accurately interprets turbidity (cloudiness) or conductivity (dissolved detergent) in wash water. Incorrect calibration leads to over- or under-dosing of detergent, reduced efficiency, and potential machine errors. Always consult the manufacturer’s service manual for model-specific procedures.

Tools Required:

Safety Precautions:

Calibration Process:

1. Access the Sensor:
   Remove the washing machine’s rear or front panel (depending on the model) by unscrewing the retaining bolts. Refer to the service manual for the exact location of the soil level sensor (typically near the drum or detergent dispenser).

   Example: In Samsung models, the turbidity sensor is located behind the control panel, accessible by removing two screws and detaching the panel.

2. Inspect and Clean the Sensor:
   Use compressed air to remove dust and debris from the sensor housing. For optical sensors, wipe the lens with isopropyl alcohol and a lint-free cloth. For conductivity sensors, clean the probes with a cotton swab dipped in vinegar (1:1 ratio with water).
3. Test Sensor Functionality:
   Use a multimeter to measure the sensor’s resistance or output voltage (consult the service manual for specifications). For turbidity sensors, the resistance should vary with water clarity (higher resistance = cleaner water). For conductivity sensors, the voltage should increase with detergent concentration.

   Example: A LG turbidity sensor typically shows 10–50 kΩ in clean water and <5 kΩ in soapy water.

4. Calibrate the Sensor:
   • Optical Sensors: Adjust the calibration screw (if present) using a screwdriver to fine-tune the sensor’s sensitivity. Rotate clockwise to increase sensitivity (detect lower turbidity) and counterclockwise to decrease it.
   • Conductivity Sensors: Replace the sensor if calibration is not adjustable, as these typically require factory-level recalibration.

   Warning: Over-tightening calibration screws may damage the sensor. Perform adjustments in 5° increments and retest after each change.

5. Reassemble and Test:
   Reconnect all wiring, secure the panels, and restore power. Run a test cycle with a lightly soiled load to verify improved soil level detection. Monitor for error codes or abnormal behavior.

Optimizing Soil Level Settings for Eco-Mode and Quick-Wash Cycles

The soil level in washing machines is far more than a simple dial adjustment; it is a convergence of sensor technology, textile science, and energy optimization that shapes every aspect of laundry performance. By aligning settings with load characteristics—whether through automated AI adaptation or informed user selection—households can achieve superior stain removal while minimizing resource waste and fabric damage. The evolution from manual controls to smart, predictive systems highlights a broader trend toward precision engineering in household appliances, where data-driven decisions replace guesswork. As consumers increasingly prioritize efficiency and longevity, mastering the nuances of soil level settings becomes instrumental in transforming routine laundry tasks into a seamless, high-performance process.

FAQ

What does the soil level in a washing machine mean?

The soil level in a washing machine refers to the degree of dirtiness or load size the appliance is set to handle. It determines water usage, detergent mixing, and cycle intensity—higher settings use more water and energy for heavily soiled or large loads, while lower settings conserve resources for lightly soiled or small loads.

What is the soil level setting on a washing machine?

The soil level setting is a control that adjusts the washing cycle based on how dirty your clothes are. Options typically range from "light" (for lightly worn items) to "heavy" (for heavily soiled or work clothes), allowing you to optimize water, detergent, and energy use.

What is the soil level in a laundry machine?

The soil level in a laundry machine indicates the expected cleanliness of the clothes being washed. It adjusts the cycle’s aggressiveness—softer for lightly used fabrics and stronger for stained or heavily used items—to balance cleaning efficiency and fabric care.

What does the soil level do in a washing machine?

The soil level adjusts the washing cycle’s intensity by controlling water temperature, agitation, and detergent dilution. A higher setting increases water and detergent use for tough stains, while a lower setting reduces resources for lightly soiled laundry, saving water and energy.

What is the soil level in an LG washing machine?

In an LG washing machine, the soil level setting (often labeled "Load & Soil" or "Soil Level") lets you select from options like "Light," "Normal," or "Heavy" to match your laundry’s dirtiness. This optimizes cleaning performance while conserving water and power based on the load size and stain level.

What is the soil level in a washing machine?

The soil level in a washing machine is a setting that determines how aggressively the machine washes based on the dirtiness of your clothes. It adjusts water temperature, agitation, and detergent mixing to ensure effective cleaning without overusing resources for light loads.

• appliance efficiency
• fabric care optimization
• laundry science
• smart home technology
• washing machine sensors