Quality Engineering

Competitive pressures and customer demands have made quality an important issue for all providers of consumer and industrial products and services. Dr. Joseph Juran defined the efforts related to quality as "the entire collection of activities through which we achieve fitness-for-use." Quality Engineering Technology (QET) encompasses the full range of these activities, including the planning, organizing, implementing, managing and measuring of product and service quality.

Software Quality Engineering is the company to turn to when you want to improve your software testing and Quality Engineering practices. Quality Engineering training and consulting is delivered by leading experts, and we have a number of products available to help you reach your software goals. Quality Engineering offerings cover such sought-after topics as software testing, project management, measurement, requirements, process improvement, and much more.

Quality Engineering program is concerned with developing advanced skills in the application of Quality Engineering controls in the design and analysis of experiments, reliability and testing, developing robust designs, quality function deployment in product design, diagnostics and the use of total quality control in production and service.

Quality Engineering Design and Analysis of Experiments: One-factor, two-factor and multifactor experiments. Fixed random and mixed models. Blocked confounding, incomplete blocks, factorial experiments, fractional factorial experiments, fractional factorial experiments. Introduction to response surface analysis.

Quality Engineering Robust Design: In many Quality Engineering institutes, students and professionals will learn models and methods in the context of overall strategies to empirically study the design of products and manufacturing processes to reduce variability and to reduce sensitivity to parameter variation. Topics include: process capability studies and measures, basic DOE concepts, factorial experiments, evaluating sources of variation, evolutionary operation and adaptive statistical process control.

Quality Engineering Machinery Diagnostics: Introduction to diagnostic system design. Fundamentals of mechanical vibration and noise. Vibration-generating sources in machinery. Sensing and data acquisition methods. Data interpretation by statistical and spectral analysis methods. Fault classification methods. Computer implementation  

Quality Engineering Total Quality Management: The concepts of total quality control (TQC) for planning in production and service are introduced within a framework of quality, cost, and delivery (QCD). Various interpretations and practices of TQC are discussed. Quality function deployment (QFD) is treated as a system tool for quality development, maintenance, and improvement over the life cycle of a product. The roles and functions of statistical quality control (SQC) and QC Circles are defined and studied. A graduate-level research paper is required.

Quality Engineering Reliability Analysis: Statistics of reliability and life testing. Application of stochastic models for failure based on Poisson and related processes. Use of exponential and extreme value distribution in reliability. Use of Markov process in the areas of equipment reliability, maintenance and availability.