Output Product Quality Research Articles

Ultrafine Graphite Scrap and Carbon Blocks Prepared by High-Solid-Loading Bead Milling and Conventional Ball Milling: A Comparative Assessment.

A comparison between the physical characteristics of graphite ultrafine particles and the properties of graphite blocks prepared from graphite scrap using bead and conventional ball milling techniques is presented. Industrial-scale bead milling was used to prepare graphite scrap with an initial particle size d50 of 24 μm in the ultrafine range of <10 μm. Bead milling can significantly reduce the production time of ultrafine graphite from graphite scrap from 72 h by ball milling to 10 min. Ultrafine graphite scrap prepared from both ball milling and bead milling yields particles with a similar morphology, with a minor difference in crystalline size La and stacking height Lc observed. Carbon blocks were fabricated from both techniques, yielding carbon blocks with an almost identical microstructure and block density. Blocks from bead milling have slightly higher flexural strength as well as comparable hardness and resistivity. The block's flexural strength, hardness, and resistivity were 68.37 MPa, 99, and 36.9 μΩ·m, respectively, in a bead-milled carbon block and 61.86 MPa, 95.5, and 38.6 μΩ·m, respectively, for a ball-milled carbon block. Bead milling can be applied for the preparation of ultrafine graphite particles and graphite blocks with production that is 9 times faster for the same ultrafine graphite particle output and final product quality.

Identification of Inhomogeneities: The Selected Coordinate Descent Method Applied in the Drilling Area

The exploration of inhomogeneities is a crucial factor for industries because of the necessary control of the quality of output products or the check adequacy of the data from the helping information systems. In the energy-conception field, the preliminary study of borehole areas has special importance because it can avoid risks of secure drilling and financial expenses. In this paper, an innovative option of the traditional coordinate descent method called selected coordinate descent, was investigated by collating its fundamentals with other methods used in various industrial branches. A practical application of selected coordinate descent was performed for experimental data of seismic event registration observed in the region of geothermal plants. An explicit formula for the resolution parameter was utilized to distinguish well and poorly resolved anomalies. The inhomogeneities were validated on the basis of a good resolution and comparison with data from other disciplines. The main result of our study is the performance of the algebraic technique application in the reconstruction of large-size structures. The identification of the found seismic inhomogeneities permits us to indicate the sites that are questionable for drilling and to obtain knowledge about the rock types at crucial depths.

A multitask encoder–decoder model for quality prediction in injection moulding

It is desirable in injection moulding that every yielded product is of high quality and precision. Several studies are conducted on modelling the relationship between the process parameters e.g., holding pressure, mould temperature, etc. that are either time series or non-time series, and the output product quality characteristics e.g., weight, dimensions, and warpage. To build a more comprehensive/generalized quality prediction model with online learning, this study for the first time takes time series process parameters from 2 pairs of in-mould pressure and temperature sensors simultaneously with 18 non-time series parameters and introduces a single deep learning architecture to perform multitask learning i.e., to leverage information from the related sources to simultaneously predict multiple product quality characteristics on a real injection moulding machine. This along with the discussed modern containerization practices develops a fully automated software part of the quality control process. The non-time series process parameters are learned by fully connected neural network layers of the model while the time series process parameters are related through an attention-based encoder–decoder architecture. This model is ablated as well as compared with a benchmark model to validate the performance advantage in terms of the reduced error asymptote. The proposed model offers an order of magnitude lower average mean squared error than the benchmark model, and 1.2 to 3 times smaller mean squared errors than its ablated versions.

THE APPLICATION OF SIX SIGMA TO IMPROVE THE YIELD OF PLASTIC INJECTION MOLDING

Plastic injection molding has complex process parameters. The Six Sigma improvement method mainly uses quality control and statistical methods to improve product quality, so as to reduce the cost and waste caused by defective products. Taiwan is the world leader for original equipment manufacturing in the laptop industry, and related peripheral accessories are also a major concern of customers. The subject of this study was a professional supplier of laptop batteries. Through the collection of customer complaint data, it was found that battery case deformation was the most important problem for customers. Therefore, in order to remain competitive, the battery case deformation problem needed to be overcome first. Enterprises need to improve their existing processes to reduce the rate of product defects, improve output efficiency and product quality, and reduce production costs, so as to improve their competitiveness in the industry.This study took a Taiwanese company as an example to improve the plastic injection process through the improvement steps of Six Sigma. In this process, two key process factors, namely the injection speed and the cooling time, were identified by analytical tools. After calculating customers’ daily product demand, the most suitable parameters were found using the ‘design of experiments’ method. The results showed that the process capacity and product yield could be significantly improved. The key to the success of this Six Sigma project was the project implementer’s design of the experiment.

An explanatory study on defects in plastic molding parts caused by machine parameters in injection molding process

In the Injection Molding process, the output product quality is dependence on various attributes such as product geometry, the material used for manufacturing, and the machining parameters (process parameters). Here it is an exaggeration to say that the parameters associated with the molding process play a crucial role in the production of high-quality products. In the present study, the causal approach is used to recognize the problems associated with the injection molding components to cause defects such as shrinkage, flow lines, sink marks, burn marks, short shots, warpage, etc. This study aims to reveal the basic understanding of the injection molding process and the importance of machining parameters in moldings. This study will brief the type of defects and their causing machining parameters to the industrial practitioners/researchers. In addition, the study will suggest the parameters to set out on the machine so that the final product will be free from any defect.

Heavy metal in the soil-grain-food path: an overview of the role of Mycotoxins in potential hazards associated with animal products

In recent years, the emerging livestock and poultry business has encountered several obstacles in producing healthy and safe products for human consumption while also providing quality and nutritious food for animals. The presence of fungal toxins and fungi in raw materials is the most significant difficulty in supplying food for livestock and poultry, since mycotoxins can reduce output and lower product quality. Also, their residues in the final products (milk, meat, eggs) can transmit their adverse effects to humans. Fungal toxins are produced as a result of the activity of fungi during their growth process, which is called Mycotoxins. Many fungal toxins have been identified to date, including Aflatoxins, Ziralenone, Fumonisins, and Ochratoxins. Aflatoxins contaminate foods, feeds, and other raw materials involved in their production, posing a serious health risk to humans, including carcinogenesis and severe toxicity. Environmental factors affect the production process of Mycotoxins, which depends on the geographical location, agricultural method, sensitivity of agricultural products, etc. Another important point is that some mycotoxins can be used as bioterrorism weapons. Exposure to Mycotoxins can have a wide range of detrimental biological effects, including bleeding, hepatotoxicity, renal toxicity, neurotoxicity, estrogenic, teratogenic, mutagenic, and carcinogenic. Because of the importance of the subject, in the current study, it was tried to review the role of Mycotoxins in potential hazards associated with animal products for humans.

Functional health prognosis approach of multi-station manufacturing system considering coupling operational factors

The core function of the manufacturing system is to ensure the quality and quantity of output products. Therefore, the performance of the manufacturing function can effectively evaluate the health state of the manufacturing system. However, the complex coupling effects caused by the correlation and interaction between multiple operational factors in the running manufacturing system will significantly affect the performance of manufacturing function, which has not been paid due attention. Therefore, a systematic functional health state prognosis approach for the manufacturing system considering coupling operational factors is given in this paper. First, based on functional performance analysis, the relationship among main coupling operational factors including manufacturing machine, production task, and produced workpiece is expounded, and the connotation of the functional health state is defined. Second, three quantitative models of the coupling effect between operational factors are constructed. Third, an integrated functional health prognosis model for the manufacturing system is established. Finally, a prognosis example of a ferrite phase shifting unit manufacturing system is provided to illustrate the effectiveness of the proposed method.

A framework for near real-time ROM stockpile modelling to improve blending efficiency

PurposeIn the mining industry, a run-of-mine (ROM) stockpile is a temporary storage unit, but it is also widely accepted as an effective method to reduce the short-term variations of ore grade. However, tracing ore grade at ROM stockpiles accurately using most current fleet management systems is challenging, due to insufficient information available in real time. This study aims to build a three-dimensional (3D) model for ROM stockpiles continuously based on fine-grained grade information through integrating data from a number of ore grade tracking sources.Design/methodology/approachFollowing a literature review, a framework for a new stockpile management system is proposed. In this system, near real-time high-resolution 3D ROM stockpile models are created based on dump/load locations measured from global positioning system sensors. Each stockpile model contains a group of layers which are separated by different qualities.FindingsAcquiring the geometric shapes of all the layers in a stockpile and cuts made by front wheel loaders provides a better understanding about the quality and quality distribution within a stockpile when it is stacked/reclaimed. Such a ROM stockpile model can provide information on predicating ore blend quality with high accuracy and high efficiency. Furthermore, a 3D stockyard model created based on such ROM stockpile models can help organisations optimise material flow and reduce the cost.Research limitations/implicationsThe modelling algorithm is evaluated using a laboratory scaled stockpile at this stage. The authors expect to scan a real stockpile and create a reference model from it. Meanwhile, the geometric model cannot represent slump or collapse during reclaiming faithfully. Therefore, the model is expected to be reconcile monthly using laser scanning data.Practical implicationsThe proposed model is currently translated to the operations at OZ Minerals. The use of such model will reduce the handling costs and improve the efficiency of existing grade management systems in the mining industry.Originality/valueThis study provides a solution to build a near real-time high-resolution multi-layered 3D stockpile model through using currently available information and resources. Such novel and low-cost stockpile model will improve the production rates with good output product quality control.

Designing a system that removes metallic inclusions from bulk raw materials on the belt conveyor

Modern industrial technologies require raw materials of high purity from suppliers, especially due to the possible presence of inclusions of heterogeneous metals in them. The existence of metallic inclusions of heterogeneous metals in the raw materials leads to equipment failure, a decrease in the quality of output products, and, consequently, large financial losses. Since the main method of transporting raw materials in the industry is still a conveyor belt, this imposes additional conditions to control and remove metallic inclusions. For various reasons, current methods for removing metallic inclusions in the conveyor belt do not fully meet the needs of modern production. The main issue related to existing removal systems is the lack of intelligent interaction between these systems and the absence of information exchange between systems that detect and remove metallic inclusions. An alternative method for removing metallic inclusions of heterogeneous metals from loose medium has been proposed, which implies the tandem operation of the system that detects metallic inclusions and the system that removes them. The tandem operation of the two systems makes it possible to exchange information about a detected metallic inclusion and, as a result, to more flexibly use tools for the removal of metallic inclusion depending on the size and location of the metallic inclusion relative to the conveyor belt axis. At the same time, the control unit of the removal system makes it possible to control the conveyor belt itself, which allows the removal of complex metallic inclusions using the reverse of the electric drive of the belt, as well as enables a control check of the fact of removal. The developed algorithm of the removal system was implemented in the programming environment TIA-Portal. The introduction of this removal system could reduce the number of metallic inclusions in raw materials by 15‒20 %; moreover, its application is not limited to only one sector of the national economy

РЕШЕНИЕ ЗАДАЧИ МНОГОКРИТЕРИАЛЬНОГО ВЫБОРА ВАРИАНТА РЕКОНСТРУКЦИИ УСТАНОВОК ГИДРООЧИСТКИ ДИЗЕЛЬНОГО ТОПЛИВА

The article describes a diesel fraction hydrofining plant as a rather complex object of management where an important criterion is the optimal resource management of the processes of technological units. This implies maintaining the optimal mode for regulation of the main parameters of the plants, which determine the output product quality. Under inadequate temperature and pressure values, as well as other insufficient operating parameters, the sulfur content in the raw material increases and the cost of the output product decreases. There are several types of hydrofining plants on the Russian market, each of them shows high operating efficiency under different modes. An iterative-fragmentary approach for developing a model of multifactor preferences for selecting the efficient hydrofining plants with optimal modes has been proposed. The results of hydrofining plants processing the different concentrations of sulfur in the raw material and different amounts of the raw material per year were taken as the initial data. Testing of the plants chosen for the study was carried out under different power loads for the raw materials, volumetric feed rates, tempera-ture and hydrogen-containing gas circulation. The optimal choice of the hydrofining plant model and its operating parameters guarantees the high quality of diesel fuel.

Clustering condition-based maintenance for manufacturing systems with both perfect and imperfect maintenance actions

This paper integrates maintenance and product quality for a manufacturing system with three failure modes. The system is monitored and can be alerted by a prediction signal before any failure mode. Due to the high setup cost of maintenance activities, their clustering can be beneficial. To reduce the frequency of failures and their impacts on the quality of output products, a clustering model with perfect and imperfect maintenance actions is applied. The objective is to investigate the impacts of clustering maintenance actions on non conforming production costs and to compare cost saving in perfect and imperfect maintenances. Our analysis and results yield valuable insights into when and how to cluster maintenance operations so that the cost for maintenance and non conforming units be minimized. Clustering perfect maintenance can be more optimal than the imperfect one for several reasons, e.g., because perfect maintenance actions restore the system completely to the as-good-as new state and the time between two maintenance is larger, and thus the frequency of maintenance decreases.

Ensuring the quality and reliability of power equipment components during their production and testing as part of a prototype

The object of study is the power equipment of thermal power plants. When developing new parts and components of power equipment, a problem arises of ensuring the quality and reliability at the stage of their putting into production and in the process of serial production. To solve this problem, the following tasks have been set: to develop ways and methods to ensure the quality and reliability of the development batch of equipment; to determine the scope of running-in tests of components as part of a prototype during serial production; to show the influence of production factors on the quality and reliability of the output product; to determine the nomenclature of production quality parameters and their impact on the manufacturability and reliability of products. The scientific novelty consists in generalization of methods, ways, mathematical models for ensuring the quality and reliability of newly created similar products. The theoretical approaches, which are proposed in the article, can be aimed at creation of state-of-art conditions for manufacturing of parts, structures and components of power equipment with new process and material characteristics in terms of reliability. The practical significance is determined by implementation of the developed methods, ways, mathematical models in the normative and technical documentation of newly created equipment. A procedure is described for holding running-in and acceptance tests. The factors, which affect the quality and reliability of components of power equipment of thermal power plants, are considered. Various groups of parameters for assessment of the quality of output products have been described. Equations are presented for calculation of manufacturability parameters, in particular, for labor-intensity, material consumption, material utilization rate, production prime cost, as well as for standardization and unification parameters. Methods are considered for determination of the product quality parameters.

Application of nanopowders in industrial production of mixed feed

One of the main directions of the feed manufacturing industry development is the improvement of the output product quality that ensures its efficacy in modern agricultural production. Some international experience in using the essential microelements in the form of nanopowders in the diets of farm animals has been gained. The article is devoted to the development and justification of a technical solution to the problem of dispensing ultrafine (nano) powders in the industrial production of combined feeds. The authors carried out theoretical and experimental studies of the influence of the conditions of functioning of industrial-technological equipment on the properties of several nanopowders of micro metals. The parametric model of the proposed pneumatic dispenser of bulk nanomaterials allows taking into account various structural and technological changes in the dosing process during the preparation of feed mixtures. The author presents a methodology for calculating the oscillatory system of a pneumatic dispenser, including an annular membrane with fixed edges, based on the equivalent replacement of a system with distributed parameters (membrane) with a system with lumped parameters, which allows replacing the oscillatory model with a translational motion model. An engineering-mathematical model that describes forced membrane oscillations, the dispenser oscillation system and the behaviour of the ultrafine material in the working chamber was constructed on its basis. Practical implementation of the results made it possible to obtain a prototype of bulk ultrafine materials dispenser.

A Hybrid Data-Based and Model-Based Approach to Process Monitoring and Control in Sheet Metal Forming

The ability to predict and control the outcome of the sheet metal forming process demands holistic knowledge of the product/process parameter influences and their contribution in shaping the output product quality. Recent improvements in the ability to harvest in-line production data and the increased capability to understand complex process behaviour through computer simulations open up the possibility for new approaches to monitor and control production process performance and output product quality. This research presents an overview of the common process monitoring and control approaches while highlighting their limitations in handling the dynamics of the sheet metal forming process. The current paper envisions the need for a collaborative monitoring and control system for enhancing production process performance. Such a system must incorporate comprehensive knowledge regarding process behaviour and parameter influences in addition to the current-system-state derived using in-line production data to function effectively. Accordingly, a framework for monitoring and control within automotive sheet metal forming is proposed. The framework addresses the current limitations through the use of real-time production data and reduced process models. Lastly, the significance of the presented framework in transitioning to the digital manufacturing paradigm is reflected upon.

Integrated Maintenance-Quality strategies taking into account the impact of the system degradation on the quality of output products

This paper deals with integrated models joining maintenance and quality. We consider a manufacturing system composed of a single machine subject to an increased random failure rate and producing conforming and non-conforming items. In order to decrease the random failure rate and its impact on the quality of output products, a bloc type preventive maintenance strategy with minimal repair is considered. Our study consists in developing integrated analytical models joining maintenance and quality in order to determine the optimal preventive maintenance plan taking into account the progressive quality perturbation and the economic impact of reworking activities. Two strategies are developed. In fact, for the first strategy, we propose reworking activities for all non-conforming products in order to improve their quality condition and to sell all batches at the best price Pmax. The aim of this strategy is to found the optimal number of batches produced and reworked N* before each preventive maintenance action, maximizing the total net profit per time unit PT1. For the second strategy, the same problem is studied but for a finite horizon. Two mathematical models are developed in order to find the optimal value of the decision variable N* for both strategies. Numerical examples are presented in order to illustrate proposed models and a sensitivity study is developed to evaluate the influence of the variation of some parameters.

Methodological Approaches to Validation of Therapeutic Recombinant Proteins Production Based on the Quality by Design Concept

Validation of production processes based on the Quality by Design (QbD) principles calls for thorough scientific understanding of the processes and enhancement of their stability by implementation of new technologies. The aim of the study consisted in substantiating a QbD-based technological approach to validation of commercial production of dornase alfa. For this purpose a design space was established in a scale-down model, i.e. 2 L reactors; the model was shown to be representative in terms of all parameters except for the reactor size; the similarity of hydrodynamic conditions, design characteristics and operation modes of laboratory, pilot and commercial scale reactors was established; the process scalability was demonstrated by using the PCA (Principal Component Analysis) multivariate mathematical model including the volume range of 2–1000 L, input and output process parameters and product quality attributes for a number of recombinant therapeutic products derived from the same CHO cell line and expression construction as dornase alfa producer. The article demonstrates the applicability of engineering space, which includes bioreactor design features and production process parameters, to different production scales by implementing 3 processes at the pilot scale (100 L) and 2 processes at the commercial scale (1000 L) and building a PCA model based on the obtained data.

Integrated maintenance–quality policy taking into account the economical impact of reworking activities

ABSTRACTIn this paper, we study integrated strategies joining maintenance and quality. We consider an imperfect manufacturing system composed of a single machine subject to an increasing random failure rate and producing conforming (first and second choice) and non conforming items. In order to decrease the failure rate and its impact on teh quality of output products, a preventive maintenance (PM) policy, block type, with minimal reapair is applied. Indeed, two different strategies are developed: The first strategy consists on selling top quality products at the best price, selling products with substandard quality at a lower price and rejecting non-conforming items. For the second strategy, we propose reworking activities for products with substandard quality and non-conforming items in order to be sold at the best price. We aim to establish the optimal number of batches produced N* before each PM action maximizing the total profit per time unit. These two strategies are studied for both an infinite and finite horizon. A comparative study between these two strategies for an infinite horizon is developed in order to identify the most economical strategy. Numerical and sensitivity studies are developed, inspired from an industrial case, in order to illustrate the proposed models.

Application and Evaluation of Random Forest Classifier Technique for Fault Detection in Bioreactor Operation

Bioreactors and associated bioprocesses are quite complex and nonlinear in nature. A small change in initial condition can greatly alter the output product quality. It is pretty difficult at times to model the system mathematically. In this work, the fault detection problem is studied in the context of bioreactors, mainly, a reactor from the penicillin production process. It is very important to identify the faults in a live process to avoid product quality deterioration. We have focused on the process history-based methods to identify the faults in a bioreactor. We want to introduce random forest (RF), a powerful machine learning algorithm, to identify several types of faults in a bioreactor. The algorithm is simple, easy to use, shows very good generalization ability without compromising much on the classification accuracies, and also has an ability to give variable importance as a part of the algorithm output. We compared its performance with two popular methods, namely support vector machines (SVM) and artificial neural networks (ANN), and found that the overall performance is superior in terms of classification accuracies and generalization ability.

Quality Integral Regulator

A pragmatic and practical device for process and product quality control in oil, petroleum, paper, manufacturing and food processing and manufacturing industries. The Quality Integral Regulator design is based on the mathematical process methodology and statistical process monitoring and feedback adjustment. A feedback control regulator algorithm is simulated under feedback control stability constraints to yield process control outputs in the form of number of adjustment intervals required to bring the output close to the target. The integral controller element in the regulator calculates the input adjustment required to bring the control error standard deviation of the outgoing product quality control variable close to the desired value (target) or the regulator set point. Statistical control procedures and process regulation schemes are formulated to minimise output product quality variance and maintain output quality variable at regulator set point. The quality integral regulator design, parameters, performance measures are explained in this book. The regulator can be implemented through modern computer parallel processing architectures for SISO and MIMO processes.

Impact of Preventive Maintenance on the Service Level of Multi-stage Manufacturing Systems with Degrading Machines

In manufacturing systems, preventive maintenance plays a fundamental role in keeping and improving the operational condition of machines and the output product quality. However, preventive maintenance policies affect the available productive time of the system. This directly influences the lot completion time and delivery dates of customer orders. Therefore the choice of preventive maintenance policies needs to be taken by jointly considering the actual condition of machines and the impact of these policies on the service level of the system. This paper proposes an analytical approach for the analysis of the impact of preventive maintenance on the service level of manufacturing systems characterized by degrading machines. The actual degradation state of critical machines is inferred from condition monitoring information gathered by sensor networks. The results obtained from the application of the method to a real industrial case in the aeronautics industry show that the decisions on the optimal maintenance policies depend on both the actual status of machines and the target lot completion time, thus paving the way to new service level oriented maintenance policies.