Robust Design Project Support

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Our robust system implementation project support dramatically reduce the cost of designing and developing & producing robust world class products and services. Your product and service are robust when it performs perfectly to user expectations for its entire design life, even in unpredictable adverse circumstances. An example of robustness is the McDonalds fast food company. The company, not known for gourmet meals, is famous for its signature “Big Mack and Fries” which look, taste and feel the same anywhere in the world. Another example is the Toyota Camry. To many people the Camry is a boring car, but it outsells any other automobile in the country, because it consistently does what its owners expect for as long as they own it. Robustness is not an accident. First a product or service is defined as robust at the concept stage. Then it is subjected to rigorous design, development testing to achieve optimization before it is released to final users. The number of variables that must be considered is large. Consequently, a comprehensive test program can be lengthy and expensive. Indeed most companies, when faced with the cost and duration of rigorous testing opt instead to let the final user do much of the testing, and risk damage to reputation, profitability and even long term survival. Nutek has addressed this challenge. Nutek provides a specialized service that reduces the cost of robust optimization testing to such a low level that it can be afforded by any quality conscious company, big or small. Companies use Nutek’s support to layout experimental plans for both products and production processes and to analyze the results. This is achieved through the use of Nutek’s proprietary Qualitek-4 analysis software which is developed from DOE (Design of Experiments) technology and the work of Dr. Genichi Taguchi. Qualitek-4 dramatically reduces the number of experiments necessary to achieve optimization, thereby slashing both cost and duration. In addition Nutek employs the Taguchi QLF (Quality Loss Function) to determine the likely cost effect of individual test results. The output is a set of critical design parameters for both products and associated processes. Client companies can then convert these parameters into intelligent tolerances for size, strength and performance and achieve truly robust world class products or services. Process Optimization Examples: Study of Crankshaft Surface Finishing Process, Adjustment of Transmission Control Cable Parameters, Study of Plastic Wire Extrusion Process, Experiment on the Binding Force of a Plastic Product, Experiment with a Fabric Dyeing Process, Optimization Automobile Drivability Parameters, Determination of Optimum Gel Content in Polyethylene Compound, Study of Heat Treatment Process Parameters, Minimization of Surface Finish on the Bearing Journals, Parameter Study of Graco-2 Spray Gun, Study of Front End Alignment, Optimization of Instrument Panel Foaming Process, Aqueous Cleaning for PC Board Soldering, Process Study of Pinion Bore Honing Process, Cylinder Core Wash Elimination Study, Machining Parameters for Minimum Tool Wear, Optimization Plastic Injection Process, Study of Effect of Salt Spray on Seal Friction, etc. Product Design Optimization examples: Engine Idle Stability, Study of Instrument Panel Optimization, Design Optimization (using FEM model) Study Leading to Selection of Worst Case Barrier Vehicle, Airbag Optimization Design Study, Study of Automobile Front Crush Structure Design Parameters, Fishing Reel Line Roller Design Study, Automobile Hood Hinge Design Study, Ultimate Strength Optimization of Bearing Outer Race, etc. Example Application Projects: (Refer to Example DOE/Taguchi Case Studies included in this site for detail DESIGN and ANALYSIS, and PRESENTATION)		When you select us to support you with your robust design projects, we will support your project in the following three major phases. Phase-I Facilitate Experiment Planning Session (Production problem or design optimization studies) This is the most critical phase in the application process and must be done on-site with the project team (generally a one-day session). Here our specialist will work with your project team and facilitate the experiment planning session. Your team members need to set aside the entire day (8 AM to 5 PAM) to work with the consultant. The success of the study depends on identifying the appropriate factors for the project through an open and participative brainstorming session. Be aware that this brainstorming may differs from "brainstorming" in conventional sense. Unlike the normal brainstorming session, this meeting follows a rigid format which is carefully controlled by the facilitator. In most engineering projects, the desired project objectives are generally more than one. Usually these objectives are also measured in different units (say size, weight, surface finish, etc.) of measurements. How do you handle multiple objectives? How do you determine the project parameters that is not just the best for one objective, but better overall for all objectives? We will help you address your individual concerns by consensus weighting and show you a way to formulate an Overall Evaluation Criteria (OEC) which is appropriate for your project objectives. (Time: 1 day with the project team) Details of services we provide: Facilitate the brainstorming session Work with team and help identify and reduce experimental parameters by CONSENSUS decisions Establish performance evaluation criteria Establish scopes of the study (# of experiments, samples required) Phase-II Lay out Experimental Plan and Prescribe Data Collection Procedure Work in this phase is generally done at Nutek. A successful planning session produces all information necessary to layout the experiment. The size of the experiment designed depend on the number of factors selected for the study and their levels chosen. Based on the designed experiment, individual trial conditions are described. These descriptions serve as the work order for each separate experiment setup (called the Trial condition). Along with the prescription for the experiment setups, the method of data collection is also described. (1 - 2 days at Nutek, 1 day on-site is optional. Written report provided) Specific Services provided: Design experiment Prescribe the recipe of each separate experiments to be conducted Establish the evaluation criteria Defined and the method of data collections, etc. [The project team follow the prescribed test recipes and completes tests. The team leader forwards the collected test results to Nutek for analysis.] Phase-III Analyze Results and Report Findings Results of experiments carried out contain a vast amount of information. But there are FOUR basic categories of information which even the smallest (L-4 experiment with 1 sample/trial) experiment can yield. Analysis of DOE results contain information such as: (1) Factor Influence (Factor average effect or Main effect), (2) Relative influence of the factors to the variation of results (ANOVA, which is short form for Analysis of Variance), (3) Optimum condition, and (4) Expected performance at the optimum condition. Other types of information like Confidence level, Significance tests, Confidence Interval (C.I.), Loss, etc. are easily obtained with slight additional effort. For simple experiments, i.e., those with single objectives and one sample/trial condition, calculations are relatively straight forward. With multiple objectives and multiple samples in each trial condition, analysis can become complicated. (Time: 1-3 days at Nutek and 1 day with the project team. Report provided) Specific Services provided: Perform complete analysis Present the findings (formal report) Recommend the optimum design Predict the expected performance with confidence interval Establish relative influence of factors Determine interactions possibilities between factors (Click here to review: List of Nutek Client Companies)

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