Statistical Methods Can Confirm Industry-sponsored University Design Project Results

An industry-sponsored project was recently developed to automatically inspect soup mix packages. The industry sponsor had determined that its highest customer complaint was the absence of a flavor packet within the soup mix package. It partnered with Indiana UniversityPurdue University Indianapolis (IUPUI) to develop an automatic system to detect the missing flavor packet and remove it from the production line before the package was bulk-packed for shipment. The system was designed, built and installed by a team of Electrical Engineering Technology (EET) and Mechanical Engineering Technology (MET) students. A four-hour production test confirmed that the percentage of soup mix bags without flavor packets detected by the machine was nearly the same as the total percentage of bags without flavor packets returned by customers the previous year. But how reliable was the system over a longer period? This paper describes a semester-long IUPUI project to determine how well the inspection system performed on its production line for a ten-month period. An honors-student project was devised to use multiple statistical methods to determine whether the automatic inspection system actually improved the overall quality of the soup mix shipments; leading to reduced customer complaints. Customer complaint data for four-million units were analyzed to determine whether a significant difference of complaints existed between the production line with the inspection system and the one without. These data were analyzed using a Two Proportion Hypothesis Test to determine if there is a difference, and a Confidence Interval to estimate the size of difference. The student concluded with 95% confidence that customer complaints were significantly lower on the production line with the inspection system

Multi-Disciplinary Capstone Project on Self-Replicating 3-D Printer

This paper explores the dynamics of a multi-semester multi-disciplinary team approach applied within a traditional senior capstone project that involves strong design and manufacturing components. In addition, the logistics of running a successful senior project will be discussed along with the associated problems of organization within a multi-program environment. The key drivers and motivators behind this paper are, most importantly, that multi-disciplinary teams are very common in industry and that our industrial advisory boards for Electrical Engineering Technology (EET) and Mechanical Engineering Technology (MET) suggested that we do more multi-disciplinary projects. Furthermore, this multi-disciplinary team approach will satisfy the proposed ABET/ETAC outcomes for 2016. The Proposed Revisions to the Program Criteria for Mechanical Engineering Technology and Similarly Named Programs ABET/ETAC outcomes say “The capstone experience, ideally multidisciplinary in nature, must be project based and include formal design, implementation and test processes.” (emphasis added) Faculty searched for a technology that would allow both EET and MET students to contribute equally to the success of the project, and decided upon additive manufacturing. Students have been exposed extensively through formal course material covering 3D printing technology and would be familiar with the operation of 3D printers in general. Therefore, it was reasoned a familiarity with the project goal of designing and constructing a self-replicating 3D printer would give students more confidence in tackling the difficult task of managing an extended project over both the design and manufacture phases, and mastering effective communicate across disciplines. The student team organization mirrors current industry standard operating procedures. First, the team is multidisciplinary, including EET students with programing and circuits skills and MET students with CAD, design, mechanical analysis skills. All students must demonstrate project process skills, utilizing current design for six-sigma procedures. The students learn a standard set of tools to manage the project, as well as synthesize those tools with their discipline specific knowledge. Because of the program curriculum plans, the EET students are involved in the project for two semesters. The MET students have a one semester project course; this enables one group of MET students to design the mechanical system, document their work, and pass it on to a second team for implementation. This was considered a positive based on what is typical in industry, where engineering groups are constantly interfacing. Results include observations of group member dynamics, quality of work, timeliness, budget management, and communication across disciplines. Rubrics to document student achievement of outcomes are used

Circuit Troubleshooting Based on Applying Lean Six Sigma Techniques: American Society for Engineering Education

This paper presents Lean Six Sigma techniques and methods that Electrical Engineering Technology (EET) students have found useful in their in-class circuit troubleshooting activities.When students are first learning circuit analysis and fabrication, they often lack the skills totroubleshoot failed circuits based on a specification. In addition to presenting the tools used in the instruction of the test student group this paper also describes how the Lean Six Sigma method were used to arrive at the optimal course content. For this paper, two student groups, in an EET laboratory experience, are compared based on the primary metric number of failed attempts to meet circuit board test specifications. The student test body was divided into two groups. A control course section group, where no troubleshooting instruction was given and designated the “As Is” state. The second section group, “Improved State” was given an extensive troubleshooting methodology as part of their initial training. The primary metric, number of failed attempts to meet specification, was chosen as it is easy to measure by student Teaching Assistants (TA) and was also used to assess the Sigma process capability for each group. The Sigma capability of each group provided a further measure of the overall success of the intervention. The authors quickly realized that students in the control group were making two classic types of errors. Many students were making a rule or knowledge-based error, where students were not following the instructions for the specific circuit fabrication and test. This type of error was addressed by improving instructional material and adding root-cause analysis checklists to the course content. The second type of observed error, where a student is incorrectly applying a base skill to the construction protocol, is classified as event-based and is more difficult to resolve. Theoretically, there can be many possible solutions to an event based error. Perhaps there may even be no optimal solution to the error, or “right answer,” just a work around that students must find. To address this type of error students were instructed how to apply Lean Six Sigma tools such as root-cause analysis and Failure Modes and Effects (FMEA) matrices in their problem-solving sessions. Also, Sneak Analysis was included to address typical design flaws