Chumpol Yuangyai

Robust parameter design for multiple-stage nanomanufacturing

Process reproducibility is a major concern for scientists and engineers, especially when new processes or new products are transitioned from laboratory-scale to full-scale manufacturing. Robust Parameter Design (RPD) is often used to mitigate this problem. However, in multiple-stage manufacturing process environments, it is difficult to employ the RPD concept because experiments cannot strictly follow the principle of complete randomization. Furthermore, the stages can be located at different sites, leading to multiple sets of noise factors. In the existing literature, only a single set of noise factors is considered. Therefore, in this research, the foundation of using the RPD concept with multistage experiments is developed and discussed. Some optimal design catalogs are provided based on a modified minimum aberration criterion. The context for this work is the development of a medical device made of nanoscale composites using a multiple-stage manufacturing process.

Decentralized Network Building Change in Large Manufacturing Companies towards Industry 4.0

Abstract In complex industrial ecosystems together with an increasing global competition, success depends on a complete value chain transformation. The use of Industry 4.0 standards is therefore gradually emerging in many industries to ensure significantly higher factory productivity, flexibility, and efficiency. However, selected methodology and results are required to be studied to fully understand the digital transformation as well as its characteristics. This research presents a system conversion study, from centralized to decentralized systems, using epidemic membership protocols on a large manufacturing company towards Industry 4.0. The system conversion shows that the epidemic membership protocols provide an ability to rewrite the structure of the overlay topology. The experimental results are presented in two categories: (1) convergence speed and (2) accuracy of the epidemic applications. These provide the information for the performance guarantee of the global aggregate computation. The expectation of this paper is to present a preliminary study focusing on the system conversion methodology in the context of Industry 4.0. There are several recent publications based on Industry 4.0; however, nothing has been done to address any methodologies applied in Industry 4.0 or their simulation results.

Image Analysis and High Dimensional Control Chart for Inspection of Sausage Color Homogeneity and Uniformity

Abstract Sausage color usually influences consumers’ selection due to the perceptions of quality. Extensive studies have applied image processing to capture the characteristics of food products according to the high-dimensional nature of the resultant images. However, the color homogeneity (i. e. “within pack” variation) and uniformity (i. e. “between-pack” variation) have rarely been studied. Therefore, this paper proposes a new framework to detect both variations using images. In addition, a new approach has been developed to deal with high-dimension data involving colorimetric characteristics, namely L*, a*, b*, hue (h) and chroma (C*). These high-dimensional data are transformed to represent color homogeneity and uniformity. Hotelling T2 chart is used to detect color abnormalities. Our approach indicates that the out-of-control items can be identified with the control chart signals. Nonetheless, the out-of-control signals alone are inadequate for determination of the possible causes. Then, the proposed analysis framework was subsequently applied to identify possible causes that contributed to the process deviations. Furthermore, prior to the experiments with sausages, the image inspection device was tested for gauge repeatability and reproducibility.

Manpower Planning with Multiple Tasks for a Call Center in Healthcare Service

Private hospitals offer an advanced appointment program that allows patients to receive medical care services at their convenient time. While the amount of callers has increased, many hospitals face difficulties to determine the number of operators to promptly respond the calls. Long waiting time may cause some callers to abandon their lines, which leads to the loss of opportunity. This chapter focuses on how to determine the optimal number of operators and their assignment in a service time horizon. An integrated framework is proposed using mixed-integer nonlinear programming to solve the staff planning and allocation problem. The result shows that the framework is viable.

Image Analysis for High-Dimensional Control Chart in Sausage Color Consistency Detection

A quality control activity is one of the key elements in a food logistic system. It plays an important role to reduce cost and customer-claim due to unsatisfactory food products. Typically, the food inspection is performed by human, and this leads to inconsistent inspection results due to fatigue and tediousness. Therefore, an automated quality inspection program is required to ensure that the system is effective. This chapter presents an approach to improve consistency of sausage color inspection after packaging using an integrated framework of image processing techniques and high-dimensional control charts. The results indicate that sausage color consistency can be improved resulting in a more stable process.

Statistical process control with autocorrelated data using neural networks

Statistical Process Control (SPC) is widely used for monitoring the performance of processes in manufacturing. Traditional SPC methods require trained individuals to read data which results in slow and limited detection. Much research has been devoted into developing an online automated system for SPC, so that the abnormality can be detected quickly and corrected by the process operation. To build a system as such, artificial neural networks (ANN) are widely used as tools where complex patterns can be difficult to recognize. Many research projects involve using random data patterns for training and recognition of patterns for ANN/SPC applications. However, many manufacturing processes involve autocorrelated data, to determine the effect of autocorrelated data, green sand data was analyzed and a neural network was built and trained to analyze a number of out of control patterns. Overall, the network performed best for detecting larger mean shifts.