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Six Sigma has transcended traditional manufacturing and is now becoming increasingly popular in transactional, service and administrative processes. Industries such as healthcare, financial services, hospitality, information technology, insurance and even government are beginning to utilize Six Sigma to continuously improve their processes and impact their bottom line. In addition, manufacturing organizations are now utilizing Six Sigma in non-traditional functions such as human resources, sales, customer service, purchasing, finance, accounting and product development. Even with the growth of transactional Six Sigma, available curriculum for educational efforts has been primarily manufacturing based. Many early efforts at developing a transactional specific curriculum have been transactional project examples injected into a manufacturing program. This lack of curriculum development can slow the progress of Six Sigma within transactional areas.

Low resolution fractional factorial experimental designs, used in screening, are more popular than ever due to the ever increasing costs of materials and machine time. Experimenters have to be more precise in their analysis, making every degree of freedom count. Resolution III designs are becoming more commonplace for use in screening designs. When running unsaturated resolution III designs there are extra degrees of freedom stemming from unassigned interactions. It is common practice to utilize these extra degrees of freedom to approximate error. In many cases, this common practice can over state the error and lead to erroneous results regarding factor statistical significance. Utilizing saturated resolution III designs and statistically analyzing unassigned interactions while estimating the error with replication is a method for strengthening the DOE strategy and improving the results from screening designs.

A current general need within Six Sigma methodologies is to utilize statistical methods including experimental design in the confirmation of new processes and their parameters. This is typically done in the improve step of the DMAIC process. This need is even more evident in microjoining (small scale resistance welding) due to the number and complexity of the process variables. This paper outlines the improve step of a Six Sigma project in which statistical methods are applied to a microjoining process. These statistical methods include linear experimental design, regression analysis with linear transformation and mathematical modeling. The paper documents the methodology used to establish process parameters in microjoining of an electrical lead frame design.

Quality issues in magnet wire stripping and soldering have led to continuous improvement efforts in ignition coil manufacturing using Six Sigma methodologies. This effort has resulted in the investigation of an alternative product and process design, microjoining. This paper describes the continuation of development occurring during the improvement phase of a Six Sigma project. The confirmation of the results is accomplished through the use of experimental design, response surface methodologies, mathematical modeling and optimization of the process. Nonlinear design of experiments have been used to confirm a breakthrough microjoining process developed that is an alternative to soldering. The statistical methods used to develop the process build on the current documented research efforts.

Concerns with stripping and soldering copper magnet wire in ignition coils and other related products have led to the investigation of an alternative product and process design, microjoining. This paper describes the initial development occurring during the improvement phase of a Six Sigma project. The use of microjoining with a folded over welding tab terminal design coupled with a parallel gap welding process is developed as a suitable method for joining a tin plated brass terminals to the 0.65mm magnet wire without prior removal of the polyesterimide over-coated polyamideimide insulation.