Quality in Design

• Quality planning is a systematic approach to ensuring that products and services meet customer requirements and expectations.
• Feedback, Assessment, and Corrective Action is the stage when the effectiveness of all the quality planning work in previous stages is verified.
• Measurement System Analysis is a statistical tool that can be used to assess data quality.
• Product planning is the first stage of planning for a product when the major features for the product are determined.
• Product/process approval refers to testing the product from the preproduction trial and verifying that the product meets the design specifications.
• Finding customer needs, also referred to as “listening to the voice of the customer,” can be done through surveying of past and potential customers, listening to focus groups of customers, collecting information from the history of complaints and warranty services, and learning from the experiences of cross-functional team members.
• A customer survey aims to identify customer needs and the level of importance that customers attach to the different needs.
• Two common sampling techniques for customer surveys are simple random sampling and stratified random sampling.
• Quality Function Deployment (QFD) is a method used to closely tie the design features of a product with the expressed preferences and needs of the customers.
• The major component of the QFD method is a matrix created with the customers’ preferences in the rows and the design features selected to meet those preferences in the columns.
• A process for determining the importance of customer preferences in the context of product design is known as Customer Requirements and Design Features.
• Prioritizing Design Features is a process for assessing the importance of design features in meeting customer requirements.
• In the example provided, a numerical scale is used for strength relationships: very strong = 5, strong = 3, weak = 1.
• The obtained values for each design feature are then normalized using the formula “yj = 100 (xj /Σxj )”.
• Choosing a Competitor as Benchmark is the assessment on how well competitor products fare with respect to the established customer preferences.
• “Specification limits” or “tolerance limits” usually refer to the limits of variability that a customer has imposed based on where and how the product is to be used.
• Randomization involves conducting trials in a randomly chosen order, which prevents the systematic influence of noise factors on experimental results.
• A method for evaluating the significance of factors using confidence intervals begins by explaining the process of obtaining estimates for experimental error and standard error for factor effects.
• Many situations arise in industrial settings, where tolerance for an assembly has to be calculated from the tolerances of components.
• Traditionally, tolerancing has been done based on the experience of designers regarding what has worked for them satisfactorily.
• Model building, where a mathematical model is developed based on experimental results to represent how a response is affected by different factor levels, is a part of the Taguchi method.
• The result from a 2^2 Design with the respective corners of the square representing the treatment-combinations.
• Data from experiments under discussion are rearranged to calculate factor effects.
• Tolerance Design is a way to acknowledge that certain parts or product characteristics will always vary, but this variability should be minimized to a minimum.
• According to Dr. Taguchi, the "Taguchi loss function" shows that the loss is only zero when the characteristic deviates from the target.
• Interaction between two factors exists if the effect of the two factors acting together is much more, or much less, than the sum of the effects caused by the individual factors acting alone.
• The “contrast” of a factor is the result of multiplying the column of signs under that factor by the column of treatment combination codes.
• The effect caused by a main factor, called a “main effect,” is calculated by subtracting the average response at the two treatment-combinations where the factor is at the lower level from the average response at the treatment-combinations where the factor is at the higher level.
• The 2^3 Design is used when three factors influence a response, and each factor is tested at two levels.
• Taguchi method involves reducing the variation in a process through robust design of experiments with the overall objective of producing high quality product at low cost to the manufacturer.
• Targets for the design features that have been prioritized as the most important are selected for the new product based on a comparison with the benchmark.
• Period B is characterized by a consistent failure rate, eventually transitioning into an increasing rate.
• The hazard function, denoted by h(t), represents the rate at which survivors at time t will fail in the instant immediately following time t.
• Parameter Design refers to selecting the product parameters, or those critical characteristics of the product that determine its quality -its ability to meet the needs of the customer and provide satisfaction.
• The reliability of a product at time t, denoted as R(t), is defined as the probability that the product will not fail before the time t, under a stated set of conditions.
• The “infant mortality” region is a period characterized by a decreasing failure rate, primarily due to the removal or repair of defective units in a population.
• A factorial experiment involves combining each level of one factor with every level of every other factor to achieve all possible treatment combinations for trials.
• Period C is a time of increasing failure rate because of parts starting to wear out.
• The mean of the distribution, or the average life of all units in the population, is referred to as MTTF for products that have only one life (i.e., those that are not repairable) and as MTBF for products that are repairable.
• Design of Experiments is an experiment designed to study the effect of some input variables, called “factors,” on a “response,” which may be the performance of a product or output of a process.