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Assess Production Quality

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SKILL.md

Skill: Assess Production Line Quality

Domain

advanced_manufacturing

Description

Evaluates production line output quality against proprietary manufacturing standards, identifying defects, process deviations, and recommending corrective actions based on statistical process control metrics.

Tags

manufacturing, quality-control, six-sigma, process-optimization, defect-detection

Use Cases

  • •Real-time production quality monitoring
  • •Defect root cause analysis
  • •Process capability assessment
  • •Supplier quality evaluation

Proprietary Business Rules

Rule 1: Control Limit Enforcement

Parts must fall within ±3 sigma control limits based on historical process capability data specific to each product line.

Rule 2: Consecutive Deviation Detection

Three consecutive measurements trending in the same direction (even within limits) triggers a process drift alert.

Rule 3: Critical Dimension Tolerance

Critical dimensions (marked in specs) have zero tolerance for out-of-spec readings - immediate line stop required.

Rule 4: Material Lot Correlation

Defects must be correlated with material lot numbers to identify supplier quality issues.

Input Parameters

  • •part_number (string): Unique identifier for the part being inspected
  • •measurements (list): Array of measurement readings
  • •dimension_type (string): Type of dimension (critical, major, minor)
  • •material_lot (string): Material lot identifier
  • •production_line (string): Production line identifier
  • •operator_id (string): Operator performing the measurement

Output

  • •quality_status (string): PASS, FAIL, or ALERT
  • •defects_found (list): List of identified defects
  • •process_capability (float): Cpk value for this measurement set
  • •recommendations (list): Corrective actions if needed
  • •lot_correlation (dict): Material lot quality correlation data

Implementation

The quality logic is implemented in quality_assessor.py and references tolerance data from CSV files:

  • •parts.csv - Reference data
  • •cpk_thresholds.csv - Reference data
  • •lot_quality_history.csv - Reference data
  • •production_lines.csv - Reference data
  • •parameters.csv - Reference data.

Usage Example

python
from quality_assessor import assess_quality

result = assess_quality(
    part_number="MFG-2024-001",
    measurements=[10.02, 10.01, 10.03, 9.98, 10.00],
    dimension_type="critical",
    material_lot="LOT-A-2024-0156",
    production_line="LINE-07",
    operator_id="OP-445"
)

print(f"Status: {result['quality_status']}")
print(f"Cpk: {result['process_capability']}")

Test Execution

python
from quality_assessor import assess_quality

result = assess_quality(
    part_number=input_data.get('part_number'),
    measurements=input_data.get('measurements', []),
    dimension_type=input_data.get('dimension_type', 'major'),
    material_lot=input_data.get('material_lot'),
    production_line=input_data.get('production_line'),
    operator_id=input_data.get('operator_id')
)