(676c) Proactive Product Quality Control: Bridge the Gap between Theoretical Advancement and Industrial Practice

Generally speaking, there are two classes of product quality control (QC) approaches: inspection-based post-process approaches and prevention-oriented in-process approaches. The first class focuses on examination of the final product performance using various experimental techniques. The identified quality problems are analyzed and then, hopefully, correlated to design and/or operation parameters based on experience. In most industrial practice, only product samples will be checked according to quality evaluation criteria/tool provided by the material supplier. Note that, these quality evaluation criteria (e.g., the acceptable processing requirements) are obtained under experimental conditions, which in most cases are “ideal” conditions unreachable in manufacturing plants. Conceivably, this type of QC is methodologically passive and it can hardly generate effective guidance for quality improvement. On the other hand, the second class of QC approaches takes advantage of the methods of process systems engineering. It focuses on quality prediction by modeling and simulation and then taking prevention-oriented measures for quality assurance. However, some models are highly nonlinear and are too detailed for on-line application. Even if simplified model with relatively ideal experimental validation is available, the industries still question the validity of the model and prefer using their routine QC procedure.

To develop an effective proactive QC framework appreciable by industries is the objective of this work. The gap between theoretical advancement and industrial practice can be bridged by utilizing commonly adopted quality evaluation tool, as well as dynamic modeling and optimization methods in a seamless way. The framework consists of the following three major tasks. (1) Develop and validate the process-product model. This model should be capable of predicting processing dynamics (e.g., temperature or pressure dynamics measured by industry during processing) according to specified operational settings. (2) Predict and evaluate product quality. Predicted processing dynamics will be compared with acceptable processing requirements suggested by the quality evaluation tool. To quantify the quality, relationships between processing conditions and critical parameters representing major product properties should be established. (3) Perform dynamic optimization to identify optimal operational settings. An effective dynamic optimization methodology should be utilized to achieve the best quality suggested by the quality evaluation tool.

This general framework is applied to achieve proactive automotive topcoat QC during the curing process. Paint-specific cure windows, which have traditionally been used as a simple technique for quality assessment, display various curing time-panel temperature ranges over which satisfactory coating quality is assumed to be achievable. In practice, cure windows are used to examine the history data of oven curing, i.e., the durations of film curing at different temperatures. With this widely accepted quality determination criterion, rather than simply checking process data (e.g. panel temperature profiles) directly, we integrate process and material data together and quantify curing completeness against cure windows for further process optimization. Major procedures can be described as follows. (1) With initial oven operational settings, validated first-principles-based vehicle body heating model is adopted to predict panel temperature profiles, which are the curing histories for topcoat as well. (2) To evaluate the coating quality, equivalent isothermal time (EIt) methodology is then applied to map nonisothermal topcoat curing histories to specific isothermal curing schedules indicatable in the cure window. (3) Dynamic optimization approach is finally used to achieve the quality improvement objective: cure uniformly as close to optimal cure as possible. Quality improvement via optimization is shown in a case study. It is demonstrated that optimized settings result in uniformed cure closer to the optimal cure condition and reduced energy consumption compared with results derived from the original industrial settings.

Above all, the principal contribution of this work is to propose an effective proactive QC framework. Since the quality criterion adopted in this framework is widely recognized, the proposed approach with successful simulation results should be appealing for industrials.