Production Control Parameters Research Articles

QFD-Driven Rotational Moulding Optimization and Quality Control to Reduce Defects and Increase Efficiency in LLDPE Fuel Tank Production

The present study utilized a House of Quality (HoQ) methodology to develop a fuel tank manufactured using the rotational moulding process, employing LLDPE material. In order to methodically tackle this issue, three House of Quality (HoQ) charts were created, outlining consumer demands, functional necessities, and production control parameters. The results of the study emphasize the critical significance of identifying and adapting essential customer requirements, functional prerequisites, and production control parameters throughout the design and production phases of rotational moulded fuel tanks. The inquiry findings emphasized that the major customer requirements were on the provision of a fuel tank that possesses qualities of strength, longevity, reliability, and cost-effectiveness, while also ensuring a prolonged lifespan and an aesthetically pleasing external appearance. Furthermore, it was determined that the material qualities and tank design are the most crucial functional requirements. In addition, the precise regulation of oven temperature and the automation of the moulding process have been identified as crucial control criteria in guaranteeing the quality and functionality of the fuel tanks.

Optimization experimental design of stable production life and estimated ultimate recovery in gaseous compound of hydrogen and carbon from shale reservoir

In this study, a mixed numerical model is established to simulate the production performance of gaseous compound of hydrogen and carbon in shale reservoir. Estimated ultimate recovery (EUR) and stable production life (SPL) of gaseous compound of hydrogen and carbon are affected by reservoir parameters and production control parameters, such as Langmuir pressure, Langmuir volume, critical desorption pressure, fracture half length, permeability in stimulated reservoir volume (SRV) area, bottom hole flow pressure and production of gaseous compound of hydrogen and carbon. Therefore, in the later research, in order to reduce the number of numerical experiments, the orthogonal experimental method is used to optimize the influence of hydrocarbon ERU and SPL, and analyze the main control factors affecting these two indices at the same time. The purpose of this paper is to optimize the production of hydrocarbons by orthogonal experimental method and find the main controlling factors affecting the production of compound of hydrogen and carbon. Two groups of orthogonal experiments were designed to find the best scheme in the process of compound of hydrogen and carbon development. The calculative results show that the output of hydrogen and carbon compounds has a highly significant impact on SPL, Langmuir volume and capability in SRV have a significant impact, and Langmuir pressure Critical destruction pressure and hydraulic fracture half-length had no significant effects.

Design of Fully Intelligent Quality Error Prevention System for Tobacco Production Line Based on Configuration Software and PLC Program

Aiming at the problems of wrong brand, mixed batch and wrong transmission of electronic parameter information in the production of cut tobacco of different brands in the tobacco industry, a fully intelligent quality error prevention system based on WinCC configuration software and PLC distributed control program is designed. The information of the production and manufacturing execution system in the general control room and the brand, batch and other information stored in the PLC of the bottom electric control cabinet is compared and verified instead of manual self-inspection. If the information is consistent, production is allowed. Otherwise, the equipment locks itself and sends alarm feedback. The design and application of the system realize the automatic error prevention of process equipment and the implementation accuracy of production control parameters, to ensure that the production process meets the requirements of process technology, effectively avoid major quality accidents, and finally guarantee the product quality.

Random Forest Regression models for Lactation and Successful Insemination in Holstein Friesian cows. 1. Mathematical aspects

The random forest regression (RFR) method is applied to study dairy cows’ economical breeding and milk production to over- come well-known difficulties in establishing reliable models based on large data sets. Regarding the features of RFR, there are sev- eral positive experiences in various areas of applications supporting that with RFR, one can achieve reliable model predictions for industrial production of any product providing a useful base for decisions. In this study, a data set compiled over a decade for about 80,000 cows, was analyzed through RFR. Ranking of production control parameters is obtained, and the most important explanatory variables are found by computing the variances of the target variable on the sets created during the training phases of the RFR. Predictions are made for milk production and the calves’ conception with high accuracy on given data, and simulations are used to investigate prediction accuracy. This study is primarily concerned with the mathematical aspects of a forthcoming article that focuses on the agricultural aspects. The results will be compared with models based on factor analysis and linear regression for future mathematical research plans.

Study on Preparation and Performance Evaluation of Environment-Friendly Asphalt Mixture

In this paper, the environmental characteristics of foaming asphalt mixture are analyzed and the benchmark ratio is determined by Marshall test. Through the research on the production control parameters of mineral foaming and mechanical foaming, on the one hand, the optimal content of ZQ mineral foaming agent was determined to be 7%. On the other hand, The water consumption for mechanical foaming of SBS modified asphalt was set as 3.0±0.2%, the asphalt temperature was set as 165±2°C, and the water temperature was set as 30±2°C. At the same time, a method for determining the optimal content of mineral foaming agent based on asphalt modulus evaluation is proposed, and determine the influence degree of each factor of mechanical foaming based on grey correlation method. During the mixing test, the actual production was simulated, the samples were formed after the mixture aged for 2 hours in a short time, and the cooling range and performance of the foamed asphalt mixture were studied. The results show that the foamed asphalt mixture can reduce about 25°C compared with the hot-mixed asphalt mixture, and the performance of the mixture can meet the requirements of the current specification.

Comparative Performance Analysis of Optimal PID Parameters Tuning Based on the Optics Inspired Optimization Methods for Automatic Generation Control

The Optics Inspired Optimization (OIO) algorithm is a new metaheuristic optimization method. In this paper, the OIO algorithm was proposed for automatic production control parameters in electrical power systems. The performance of the proposed algorithm was realized on two power systems that have different structures. The first structure is a two-area interconnected thermal reheat power system and the other one is a two-area interconnected multi-unit hydro-thermal power system. The results obtained with the proposed algorithm were compared with an artificial bee colony and particle swarm optimization, initial values are randomly defined that are commonly used in literature. The results were examined using four different cost functions based on area control error. Considering the obtained results, the proposed algorithm reached to the global minimum value with less number of iterations and is more suitable for online optimization. According to the results obtained with this novel method, it has a better performance for maximum overshoot and settling time values when the test systems are implemented.

Rule Extraction from Support Vector Machine and its Application to Hot-Dip Galvanizing

A new rule extraction algorithm based on convex hull for strip hot-dip galvanizing process monitoring is proposed in this paper. It overcomes the black-box problem of support vector machine. The zinc coating weight is used as the investigated subject. The sample datasets are trained by support vector machine rule extraction method, and the quantitative relationship can be obtained in the form of knowledge rules among input variables (such as the parameters of raw materials and control parameters of production) and output ones (the quality parameters), with which the production control parameters can be set and updated easily.

Joint Design of Quality and Production Control in Multi-Stage Asynchronous Manufacturing Systems

AbstractThis paper proposes an analytical method for the joint design of quality and production control parameters in unreliable asynchronous multi-stage lines. Classical statistical quality control charts are typically designed without taking into consideration the logistic performance of multi-stage systems. Similarly, production control parameters, such as the number of kanban cards at any stage of a kanban controlled system, are typically designed without taking into consideration the impact of this decision on the product quality. By doing so, it is implicitly assumed that the two controllers act as independent. However, the dynamics of real systems show that this is not true. Specifically, the proposed method optimally sets the sample size, the sampling frequency and the position of the control limits of the quality control charts as well as the number of kanban cards at any stage of production, by jointly considering the mutual relations between the controllers. Numerical results compare the solution of this integrated design with the configurations obtained by solving the two problems in isolation with existing techniques. They show that great benefits can be achieved by the proposed integrated design of the quality and production control parameters, since it fully captures the interaction between the dynamics of the two controllers.

Joint design of quality and production control in manufacturing systems

An analytical method for the joint design of quality and production control parameters in unreliable multi-stage lines is proposed in this paper. Specifically, the method optimally sets the sample size, the sampling frequency and the position of the control limits of the quality control charts as well as the number of kanban cards at any production stage, by jointly considering the mutual relations between the controllers. Numerical results compare the solution of this integrated design with the configurations obtained by solving the two problems in isolation with existing techniques. They show that great benefits can be achieved by the proposed integrated design of quality and production control parameters, since it fully captures the interaction between the dynamics of the two controllers.

Cooperative flow dynamics in production lines with buffer level dependent production rates

We study, in the fluid flow framework, the cooperative dynamics of a buffered production line in which the production rate of each work-cell does depend on the content of its adjacent buffers. Such state dependent fluid queueing networks are typical for people based manufacturing systems where human operators adapt their working rates to the observed environment. We unveil a close analogy between the flows delivered by such manufacturing lines and cars in highway traffic where the driving speed is naturally adapted to the actual headway. This close analogy is thoroughly explored. In particular, by investigating the dynamic response of small perturbations around free flow stationary regimes, we can draw a “phase diagram”. This diagram exhibits two different flow patterns, namely the free and jamming production regimes. The transitions between these regimes are tuned by the production control parameters (i.e. the buffer capacities, the reaction sensitivity, the control sampling time, etc.). We finally extract a dimensionless dynamic parameter directly relevant for design purposes.

Statistical Modelling of MOS Transistor Mismatch for High‐voltage CMOS Processes

AbstractThe random mismatch of semiconductor devices caused by local variations of the production process strongly influences critical performance parameters of analog circuits. In order to estimate the influence of the device mismatch on the circuit yield during the design phase, statistical mismatch models for the individual components must be provided for circuit simulation. Based on extensive statistical measurements of matched pairs using special test structures, an algorithm is presented to extract device mismatch parameters for high‐voltage transistors. Due to the special construction of high‐voltage transistors the mismatch parameters must be determined in two steps taking into account the effect of the drift region at high gate voltages. Therefore, the device mismatch for the threshold voltage, the gain factor and the mobility reduction are extracted from a sensitivity model in saturation, whereas the extraction of the drift‐resistance mismatch uses a modified MOS transistor model in the ohmic region. Based on the mismatch parameters for several device sizes a proper geometric model is extracted allowing the mismatch parameters to be scaled with the device dimensions. The mismatch parameters are implemented as part of circuit simulation model libraries to be used for Monte Carlo simulation. Production control parameters enable the monitoring of device mismatch for high‐voltage CMOS transistors. Copyright © 2005 John Wiley & Sons, Ltd.

Perturbation analysis for production control and optimization of manufacturing systems

We use stochastic fluid models (SFM) to capture the operation of threshold-based production control policies in manufacturing systems without resorting to detailed discrete event models. By applying infinitesimal perturbation analysis (IPA) to a SFM of a workcenter, we derive gradient estimators of throughput and buffer overflow metrics with respect to production control parameters. It is shown that these gradient estimators are unbiased and independent of distributional information of supply and service processes involved. In addition, based on the fact that they can be evaluated using data from the observed actual (discrete event) system, we use them as approximate gradient estimators in simple iterative schemes for adjusting thresholds (hedging points) on line seeking to optimize an objective function that trades off throughput and buffer overflow costs.