Reducing variation in key product features is a very important goal in process improvement. Finding and trying to control the cause(s) of variation is one way to reduce variability, but is not cost effective or even possible in some situations. In such cases, Robust Parameter Design (RPD) is an alternative. The goal in RPD is to reduce variation by reducing the sensitivity of the process to the sources of variation, rather than controlling these sources directly. That is, the goal is to find levels of the control inputs that minimize the output variation imposed on the process via the noise variables (causes). In the literature, a variety of experimental plans have been proposed for RPD, including Robustness, Desensitization and Taguchi’s method. In this thesis, the efficiency of the alternative plans is compared in the situation where the most important source of variation, called the “Dominant Cause”, is known. It is shown that desensitization is the most appropriate approach for applying the RPD method to an existing process.

Reducing variation in key product features is a very important goal in process improvement. Finding and trying to control the cause(s) of variation is one way to reduce variability, but is not cost effective or even possible in some situations. In such cases, Robust Parameter Design (RPD) is an alternative. The goal in RPD is to reduce variation by reducing the sensitivity of the process to the sources of variation, rather than controlling these sources directly. That is, the goal is to find levels of the control inputs that minimize the output variation imposed on the process via the noise variables (causes). In the literature, a variety of experimental plans have been proposed for RPD, including Robustness, Desensitization and Taguchi’s method. In this thesis, the efficiency of the alternative plans is compared in the situation where the most important source of variation, called the “Dominant Cause”, is known. It is shown that desensitization is the most appropriate approach for applying the RPD method to an existing process.

Variation is inevitable in any process, so it has to be dealt with effectively and economically. Reducing variation can be achieved in assembly lines strategically and dynamically. Implementing both the strategic and dynamic variation reduction techniques is expected to lead to further reduction in the number of failed final assemblies. The dissertation is divided into three major parts. In the first part, we propose to reduce variation for assemblies by developing efficient inspection plans based on (1) historical data for existing products, or simulated data for newly developed products; (2) Monte Carlo simulation; and (3) optimization search techniques. The cost function to be minimized is the total of inspection, rework, scrap and failure costs. The novelty of the proposed approach is three-fold. First, the use of CAD data to develop inspection plans for newly launched products is new, and has not been introduced in the literature before. Second, frequency of inspection is considered as the main decision variable, instead of considering whether or not to inspect a quality characteristic of a subassembly. Third, we use a realistic reaction plan (rework-scrap-keep) that mimics reality in the sense that not all out-of-tolerance items should be scrapped or reworked. At a certain stage, real-time inspection data for a batch of subassemblies could be available. In the second part of this dissertation, we propose utilizing this data in near real-time to dynamically reduce variation by assigning the inspected subassembly parts together. In proposing mathematical models, we found that they are hard to solve using traditional optimization techniques. Therefore, we propose using heuristics.Finally, we propose exploring opportunities to reduce the aforementioned cost function by integrating the inspection planning model with the Dynamic Throughput Maximization (DTM) model. This hybrid model adds one decision variable in the inspection planning; which is whether to implement DTM (assemble the inspected subassemblies selectively) or to assemble the inspected items arbitrarily. We expect this hybrid implementation to substantially reduce the failure cost when assembling the final assemblies for some cases. To demonstrate this, we solve a numerical example that supports our findings. / Ph. D.

Error equivalence concerns the mechanism whereby different error sources result in identical deviation and variation patterns on part features. This could have dual effects on process variation reduction: it significantly increases the complexity of root cause diagnosis in process control, and provides an opportunity to use one error source as based error to compensate the others. There are fruitful research accomplishments on establishing error equivalence methodology, such as error equivalence modeling, and an error compensating error strategy. However, no work has been done on developing an efficient process design approach by investigating error equivalence. Furthermore, besides the process mean shift, process fault also manifests itself as variation increase. In this regard, studying variation equivalence may help to improve the root cause identification approach. This thesis presents engineering driven approaches for process design and control via embedding error equivalence mechanisms to achieve a better, insightful understanding and control of manufacturing processes. The first issue to be studied is manufacturing process design and optimization based on the error equivalence. Using the error prediction model that transforms different types of errors to the equivalent amount of one base error, the research derives a novel process tolerance stackup model allowing tolerance synthesis to be conducted. Design of computer experiments is introduced to assist the process design optimization. Secondly, diagnosis of multiple variation sources under error equivalence is conducted. This allows for exploration and study of the possible equivalent variation patterns among multiple error sources and the construction of the library of equivalent covariance matrices. Based on the equivalent variation patterns library, this thesis presents an excitation-response path orientation approach to improve the process variation sources identification under variation equivalence. The results show that error equivalence mechanism can significantly reduce design space and release us from considerable symbol computation load, thus improve process design. Moreover, by studying the variation equivalence mechanism, we can improve the process diagnosis and root cause identification.