Li, Qing

Statistics & Machine Learning in Nondestructive Evaluation (NDE)

Statistical Quality Assurance

  • Improve the quality and repeatability of products
  • Quantify different sources of variation
  • Process characterization

Probability of Detection

  • Quantify the capability of an NDE technique both to detect and size flaws
  • Help to establish compliance with standard requirements for inspection quality
  • Serve as a target to achieve for testing processes

Statistics, Data Analytics, and Machine Learning in Advanced Manufacturing

  • Statistical process monitoring
  • Defects detection and prediction
  • Data fusion among different modalities of data, such as thermal images, CT tomography data, surface topography data, etc. for more accurate process prognosis

Qing Li areas of research

Point of Contact:

Qing Li
Assistant professor in IMSE
Department Profile:

Funded Research Sponsors

  • NSF, Department of the Navy,
  • Iowa State University
    1. College of Engineering
    2. IMSE Department
    3. Debbie and Jerry Ivy College of Business


  • Quality, Statistics, Machine Learning, Data Analytics

Memberships & Awards

  • American Society for Nondestructive Testing (ASNT)
  • Institute of Industrial and Systems Engineers (IISE)
  • American Statistical Association (ASA)
  • M&D Best Track Paper Award of the Manufacturing and Design Division in IISE