Machine Production Rate Research Articles

Multiple degradation-driven preventive maintenance policy for serial-parallel multi-station manufacturing systems

Machine reliability degradation is common in production systems; besides, machine production rate (PR) degradation also widely occurs as the systems deteriorate. The PR has an essential impact on the capacity of the systems. However, the few efforts involving the capacity requirements in preventive maintenance (PM) policies have not yet taken PR degradation into account, assuming all the PRs are constant. It is necessary for PM policies to consider the dynamic capacity caused by the deteriorating PR. With the two types of degradation involved, a multiple degradation-driven preventive maintenance (MDPM) policy for the serial-parallel multi-station manufacturing systems (SMMS) is proposed. Since PM has an impact on PR each time it is executed, an evolution model of PR, with the imperfect maintenance effect considered, is developed. The capacity efficiency based on deteriorating PR is put forward. By maximizing capacity efficiency and minimizing cost rate, a station-level PM optimization model is proposed. Then, the process of the MDPM scheduling is formulated, where three rules, including minimum remaining time, maintenance time window and capacity balance, are suggested for the system-level decision. Finally, the case study is illustrated to show the application of the PR evolution model and the advantages of the proposed MDPM policy.

Optimization for a Joint Predictive Maintenance and Job Scheduling Problem With Endogenous Yield Rates

While job scheduling problems have been studied extensively, scheduling problems with endogenous yield rates that may be affected by predictive maintenance is not thoroughly investigated. In this study, we consider the optimization of a joint predictive maintenance and job scheduling problem for the minimization of total shortage penalty. As maintenance may be conducted to raise machine yield rates, machine production rates and job processing times become endogenous, and the optimization problem is different from traditional scheduling problems. We formulate a mixed integer program for this problem and develop a heuristic algorithm based on Tabu search. We demonstrate the effectiveness of our algorithm through numerical experiments and a way of estimating the yield declining rate with industry defect and maintenance records. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners</i> —This work is motivated by the real need of our industry collaborator, red electronics manufacturer. Every morning, the manufacturer chooses up to three out of eleven photolithography machines to conduct maintenance. Conducting maintenance helps raise machine yield rates to decrease the number of defects in expectation. However, the production schedule of some jobs must be postponed, and delay and shortage may arise. The decision is thus to schedule jobs as well as maintenance to find a balance between yield loss and shortage loss. We help the manufacturer by formulating an optimization model and develop an algorithm to solve the model. The algorithm may be applied to similar cases when one needs to schedule maintenance and production processes at the same time.

Development and Implementation of Autonomous Quality Management System (AQMS) in an Automotive Manufacturing using Quality 4.0 Concept– A Case Study

PurposeThe goal of this research is to use the Quality 4.0 (Q4.0) concept to digitalize the traditional Quality Management System (QMS) and demonstrate the efficiency of the Autonomous Quality Management System (AQMS) in the case organization. Design/Methodology/ApproachThe Internet of Things (IoT) concept was used to develop AQMS. The Q4.0 technique was used to digitalize the developed system. Gage repeatability and reproducibility (gage R&R) of traditional and autonomous QMS systems have been evaluated. Both the QMS systems were examined using six-sigma quality indicators, as well as machining and inspection cost analysis. FindingsIn traditional and autonomous QMS, the gage R&R was found to be 91.60 percent and 0.67 percent, respectively. The special purpose machine (SPM), computer numerical control (CNC), and hydraulic machine production rates were increased by 44.42 percent, 65.60 percent, and 65.76 percent, respectively. The effective deployment of AQMS has reduced the production and inspection costs by 52.16 percent and 78.35 percent. The AQMS process yield has increased from 86.80 percent to 99.99 percent, and the component rejection rate has decreased by 93.70 percent. Overall machining process improved significantly as the sigma level increased from 1.5 to 5.5. Research Limitation/ImplicationThe current study's findings are limited to hub bolt products only. In future investigations, the application of AQMS to other types of products will be considered. Originality/ValuesThis study was carried out practically to reflect the effectiveness of the implemented Q4.0 concept in the actual industry scenario.

Completing of modern energy-saving machine-tractor units

Labor productivity and crop production of a high quality directly depend on its equipment with the most advanced energy-rich machinery and the use of modern resource-saving technologies. Today, despite the difficult financial situation of agricultural enterprises, their technological infrastructure is implemented without necessary scientific justification, most often at the discretion of particular specialists or for other reasons, but without calculating the optimal composition and structure of the machine-tractor fleet. The units are completed according to the seller and manufacturer recommendations, relying only on the technical characteristics of the tractor and agricultural machine. The article focuses on the problem of completing machine-tractor aggregates, consisting of modern tractors and agricultural machines of domestic and foreign production, which are technically based production rates. For these units, a methodology for their completing, using the potential tractor thrust characteristics, has been developed. A graphic-analytical method for determining the technically based machine production rates is proposed.

Factors Affecting the Mechanical Extraction of Jatropha Curcas Oil

The aim of the present research is to study some different factors affecting the mechanical extraction of jatropha oil. The hydraulic pressing machine was evaluated using (whole seeds or kernel), four pressures of 100, 150, 200 and 250 bar, four seed cooking temperatures of 80, 90, 100 and 110 °C and four extraction times of 15, 20, 25 and 30 min. The evaluation included machine production rate, expression efficiency, oil losses, specific energy and economical costs. The obtained data showed that using the whole seeds the highest value of machine productivity of 3.43 kg/h, the lowest specific energy of 144.46 kW.h/Mg and the lowest cost per mass unit of 7.23 L.E/Mg were recorded with extraction pressure of 250 bar, extraction time of 15 minute and seed temperature of 110 °C. And the highest value of expression efficiency of 85.93% at extraction pressure of 250 bar, extraction time of 30 minute and seed temperature of 110 °C. meanwhile, using kernel seeds under room temperature the highest value of machine productivity of 4.87 kg/h, the lowest specific energy of 100.80 kW.h/Mg and the lowest cost per mass unit of 5.05 L.E/Mg were recorded with extraction pressure of 250 bar and extraction time of 15 minute. And the highest value of expression efficiency of 87.04% at extraction pressure of 250 bar and extraction time of 30 minute.

Impact of overall equipment effectiveness on return on investment in the Nigerian cement manufacturing industry

The formidable challenge of maintenance of heavy duty equipment for the day-to-day manufacturing activities informed this research investigation. The study examined the impact of overall equipment effectiveness on return on investment in the Nigerian cement manufacturing industry. It focused on the key indicators of overall equipment effectiveness and their impact on return on investment. Ex-post facto research design was adopted in conducting the empirical investigation. The firms in the industry quoted in the Nigeria Stock Exchange (NSE) were studied. Longitudinal data of 15 years observation (2005-2019) were obtained and analyzed with ordinary least squares regression (system-OLS). The key indicators of overall equipment effectiveness subjected to empirical test proved positively significant to return on investment at the Coefficient values a1, a2 &amp; a3 &gt; 0; Prob.-values a1, a2, &amp; a3 &lt; 0.05 and t-Statistic values a1, a2, &amp; a3 absolutely ≥ 2. These analysis results suggested that machine availability rate (MAR), machine production rate (MPR), product quality rate (PQR) variables of overall equipment effectiveness have significant linear effect on ROI. Based on these results, the study therefore recommends among others for top management’s support and commitment to proactive and continuous improvement production facilities maintenance for improved overall equipment effectiveness and sustainable corporate performance of firms in the industry.

Optimal control and simulation for production planning of network failure-prone manufacturing systems with perishable goods

The problem of controlling the production rates of failure prone manufacturing systems has stochastic features that make it more complex and challenging. In this study, we consider a network of manufacturing machines based on the hedging point policy where the final goods are perishable, and the demand rate is constant. Our objective in this paper is to control the production rates of multiple machines in failure prone manufacturing systems in the presence of perishable goods in order to minimise the expected cost consisting of holding, shortage, perished goods and repair costs over an infinite horizon. We develop a new framework by way of a simulation-optimisation approach to deal with complexity and uncertainty. To this end, we first formulate the analytical model subject to stochastic failures and corrective repairs. Then, we use a combination of simulated annealing metaheuristic, simulation and Taguchi experimental design to estimate the optimal control policy. In addition, a numerical example is presented to illustrate the applicability and efficacy of the proposed framework.

Joint optimization of production and maintenance strategies considering a dynamic sampling strategy for a deteriorating system

This paper presents a control policy of integrated production, maintenance and quality control planning for a continuous production system subject to quality deterioration. The system under study is composed of an unreliable machine that produces one part type satisfying customers demand. The machine can fail at any time and is subject to quality deterioration, and so a preventive maintenance and quality control policies are proposed to decrease the rate of defectives and to increase the system availability. The proposed production policy incorporates production thresholds, which regulate the machine production rate. Traditional sampling inspections standards such as ANSI/ASQC Z1.4 and ISO 2859 have addressed a dynamic quality control level to face quality deterioration. However, these standards are based only on quality considerations, disregarding the economic aspects and the interactions with production and maintenance management in the design of sampling plans. Thus, the proposed integrated model analyses in detail the effect of such dynamic sampling strategy and relevant interactions with production and maintenance strategies. The main objective of the paper is to determine an appropriate production policy as well as the preventive maintenance and the quality control rates in order to minimize the expected average incurred cost and satisfy at the same time a quality constraint. Given the high flexibility and capacity to model complex manufacturing systems, a combination of simulation modeling and optimization techniques are used to determine a solution for this stochastic and constrained problem. In addition, numerical examples and an extensive sensitivity analysis are conducted to illustrate the proposed control approach. Furthermore, we compared the proposed integrated policy with three common policies from the literature. Such study serve us to highlight the effectiveness of the approach, since the proposed integrated policy led to considerable cost savings.

Production and replacement planning of a deteriorating remanufacturing system in a closed-loop configuration

This paper deals with the control of a hybrid manufacturing/remanufacturing system subject to random breakdowns and repairs. Given the heterogeneity of returned products, the remanufacturing machine deteriorates with time as a result of imperfect repairs, and needs to be replaced. The manufacturing machine receives homogeneous raw materials, and is not affected by this type of deterioration. The main objective of this paper is to find the production rates of both machines and the replacement rate of the remanufacturing machine that minimize the total cost, including production, inventory holding, backlog, repair and replacement costs, over an infinite planning horizon. A novel mathematical model is proposed for the underlying class of problems related to the history of breakdowns and repairs. This model is characterized by the extension of the state space, leading to a Markov decision model, allowing a derivation of the optimality conditions. Otherwise, such conditions are impossible to obtain. Optimality conditions in the form of Hamilton-Jacobi-Bellman (HJB) equations are thus developed. We show that despite the increased state space dimension, the problem remains tractable, and the solutions of HJB equations are obtained numerically. Finally, an illustrative example and a sensitivity analysis are provided to verify the robustness of the control policies obtained.

Performance prediction of impact hammer using ensemble machine learning techniques

Various mechanical excavation systems such as roadheaders, tunnel boring machines (TBM), and impact hammers, are commonly used in mining, tunneling, and civil engineering projects. However, in recent years, the application of impact hammers in hard rock excavation has been on the rise, especially in fractured geological formations. Impact hammers offer some advantages over drill and blast systems like selective mining, mobility, less over excavation, minimal ground disturbance, elimination of blast vibration, reduced ventilation requirements, and compared to TBMs, low specific energy and small initial investment cost.Prediction of the net machine production rate in terms of net (instant) breaking rate (NBR) plays an important role in estimation of completion time, schedule and cost of the projects. Performance prediction models has been developed based on field data where Impact hammers were used in tunneling operations. While some models are based on statistical analysis of field data, a fewer subset have been developed using artificial neural network (ANN). In this study, 121 data sets, including machine production rate, uniaxial compressive strength (UCS), rock quality designation (RQD), excavator power (P), and weight of excavator (W) have been compiled and using a CRISP-DM data mining technique along with principal component analysis (PCA), a new model for prediction of the impact hammer performance has been introduced with R2 of over 85%.

SIMULATION-BASED INVESTIGATIONS ON ELECTRICITY DEMAND RESPONSE FOR MANUFACTURING SYSTEMS TO MITIGATE OVERGENERATION DUE TO HIGH PENETRATION OF RENEWABLE SOURCES

Electricity demand response is an effective tool that can mitigate overgeneration due to high penetration of renewable sources in electricity grid by providing additional desired electricity consumption to match generation. In this paper, we propose a simulation based methodology to investigate the potential benefits by participating overgeneration mitigation oriented demand response program for manufacturers. A simulation platform is built so that various participation strategies of manufacturers can be examined and the effectiveness regarding cost reduction can be compared. An intuitive demand response strategy considering the utilization of onsite generation system is tested. Sensitivity analysis considering the effects of the variations due to production system parameters, such as machine rated power, machine production rate, etc., and demand response program parameters, such as incentive, penalty, etc., on the overall cost is conducted. The results are analyzed and some initial insights are discussed regarding the optimal control strategy for manufacturers when participating such overgeneration mitigation oriented demand response program.

Analysis of a multimachine flexible manufacturing cell using stochastic Petri nets

In this article, a generalized stochastic Petri net model is developed to analyze the performance of a multimachine flexible manufacturing cell. The analyzed flexible manufacturing cell consists of one or more machine(s), a single conveyor, and a single robot. The conveyor transfers similar workpieces to the robot areas. Then, the robot loads parts onto the machines, which perform similar machining processes. The machines are subject to failure after a certain time due to tool wear or resetting. When a machine is in failure, a repair action is taken by the flexible manufacturing cell operator. A generalized stochastic Petri net model is constructed for a single-machine and three-machine flexible manufacturing cells, after which the suggested generalized stochastic Petri net model can be generalized to a flexible manufacturing cell with n machines. The obtained results provide an estimate of machine, robot, and operator utilization and production rates for the considered flexible manufacturing cell. Sensitivity analysis is carried out to illustrate the effect of the input parameters on the selected performance measures. The suggested model is tested using numerical examples and simulated using TimeNet tool.

Prediction of roadheaders' performance using artificial neural network approaches (MLP and KOSFM)

Application of mechanical excavators is one of the most commonly used excavation methods because it can bring the project more productivity, accuracy and safety. Among the mechanical excavators, roadheaders are mechanical miners which have been extensively used in tunneling, mining and civil industries. Performance prediction is an important issue for successful roadheader application and generally deals with machine selection, production rate and bit consumption. The main aim of this research is to investigate the cutting performance (instantaneous cutting rates (ICRs)) of medium-duty roadheaders by using artificial neural network (ANN) approach. There are different categories for ANNs, but based on training algorithm there are two main kinds: supervised and unsupervised. The multi-layer perceptron (MLP) and Kohonen self-organizing feature map (KSOFM) are the most widely used neural networks for supervised and unsupervised ones, respectively. For gaining this goal, a database was primarily provided from roadheaders' performance and geomechanical characteristics of rock formations in tunnels and drift galleries in Tabas coal mine, the largest and the only fully-mechanized coal mine in Iran. Then the database was analyzed in order to yield the most important factor for ICR by using relatively important factor in which Garson equation was utilized. The MLP network was trained by 3 input parameters including rock mass properties, rock quality designation (RQD), intact rock properties such as uniaxial compressive strength (UCS) and Brazilian tensile strength (BTS), and one output parameter (ICR). In order to have more validation on MLP outputs, KSOFM visualization was applied. The mean square error (MSE) and regression coefficient (R) of MLP were found to be 5.49 and 0.97, respectively. Moreover, KSOFM network has a map size of 8 × 5 and final quantization and topographic errors were 0.383 and 0.032, respectively. The results show that MLP neural networks have a strong capability to predict and evaluate the performance of medium-duty roadheaders in coal measure rocks. Furthermore, it is concluded that KSOFM neural network is an efficient way for understanding system behavior and knowledge extraction. Finally, it is indicated that UCS has more influence on ICR by applying the best trained MLP network weights in Garson equation which is also confirmed by KSOFM.

Multimachine Flexible Manufacturing Cell Analysis Using a Markov Chain-Based Approach

In this paper, a stochastic model is developed to analyze the performance of a flexible manufacturing cell (FMC). The FMC considered in this paper consists of a single conveyor, a single robot, and one or more machine(s). The conveyor belt delivers the working part to the robot, which loads it onto the machine. A Markov chain model is constructed for one-machine and two-machine FMCs, after which the model is generalized to an FMC with n machines. Most importantly, the model provides an estimate of the overall machine utilization and production rate for the FMC under consideration and also illustrates the effect of different operational factors on machine utilization and production rate. The results indicated that the overall machine utilization increases with conveyor belt and robot delivery rates and decreases with machine rate, as expected. However, this decrease or the increase in the overall machine utilization is sharp at low levels of each parameter (e.g., conveyor belt delivery and robot loading), but it gradually stabilizes at higher levels of the parameters. Finally, the production rate increases sharply at low levels of each parameter and gradually stabilizes at higher levels.

Analytical modelling and control of a hybrid manufacturing/remanufacturing system

A production system consisting of parallel machines deteriorating with the production dependent failure rates of the machine is investigated in this paper and extended within the framework of a manufacturing/remanufacturing system within a closed loop reverse logistics system The machines produce one type of final products. The demand rate for the final commodity is constant and unmet demand is backlogged. The goal is to find the production rates of both machines so as to minimize a long term average expected cost which includes backlogging as well as inventory costs. In the proposed model, the main machine (characterized by a higher production rate) is more robust than the second machine. The failure rate of the main machine depends on its production rate, while the failure rate of the second machine is constant. This paper proposes a stochastic dynamic programming formulation of the problem and derives the optimal production policies numerically. A simulation example is included, and sensitivity analyses with respect to the system parameters are examined to illustrate the effectiveness of the proposed methodology.

EFFECT OF BINDING MATERIALS, FEEDING RATE AND DIE PROFILE ON AQUATIC PELLETS QUALITY USING RING-DIE PELLETING MACHINE

The overall goal of the present study isto investigate the effectof using binding materials, feeding rate and die profile on aquatic pellets quality using ring-die pelleting machine to produce a strong and durable aquatic pelleted feeds with lowest specific energy consumption. Three feed blends containing 30% protein were formulated with the same concentrate level of each starch, gelatin and molasses of 3% as a binding material instead of fish meal (FM) along with appropriate concentrate levels of soybean meal, corn flour, mix of vitamin and mix of mineral and pelleted in a ring-die pelleting machine. The ring-die pelleting machine was evaluated under the same concentrate levels of replacement percentage of binding materials in feed formula, feeding rate (70, 100 and 125 kg/h) and two types of forming unit of die profile (1) and die profile (2). Using constant values of die speed of 300 rpm, average ration moisture content of 17% (w.b). The evaluation included machine production rate, pelleting efficiency, specific energy consumption, pellets quality (durability, bulk density) and economical costs of aquatic feed pellets mass. The obtained data showed that, the highest values of machine productivity of 123.96 kg/h, , lowest specific energy consumption of 19.26 kW.h/Mg, the highest values of durability (92.68%) and pelleting efficiency of 98.67 % were recorded in the case of gelatin as binding material, die profile (1) and feeding rate of 125 kg/h. but the highest values of pellets bulk density (0.822 g/cm3) was recorded in the case of molasses as binding material, die profile (1) and feeding rate of 125 kg/h.

EFFECT OF USING POULTRY BY-PRODUCT MEAL ON AQUATIC PELLETS QUALITY AND FISH GROWTH USING RING-DIE PELLETING MACHINE

In present study, three feed blends containing 30% protein with an energy content of 350 Kcal/100 grams were formulated with three levels of poultry by-product meal (PBM) of 0.0, 50 and 100% as a cheaper protein source instead of Herring fish meal (HFM) along with appropriate levels of soybean meal, corn flour, vitamin mix and mineral mix and pelleted in a ring-die pelleting machine to fed Nile tilapia fingerlings (5.4 ± 0.13 g).The ring-die pelleting machine was evaluated under three levels of replacement percentage of PBM in feed formula, roller clearances (0, 2 and 4mm) and levels of moisture content of (15, 20 and 25%). The evaluation included machine production rate, pelleting efficiency, specific energy consumption, pellets quality (durability, bulk density) and economical costs of aquatic feed pellets mass, in addition to growth performance of fish. The obtained data showed that, the highest values of machine productivity of 58.68 kg/h, pelleting efficiency of 95.48%, lowest specific energy consumption of 29.65 kW.h/Mg and the lowest cost per mass unit of 1976.68 L.E/Mg were recorded in the case of PBM percentage of 100% ,roller clearance of 0 mm and moisture content of 15%, while the highest values of durability(0.914 g/cm3) and pellets bulk density (92.54% )were recorded at roller clearance of 2 mm, PBM percentage of 100% and moisture content of 15%.The results revealed that no disparity in growth response of fish and efficiency of feed utilization and did not affect fish composition of dry matter, protein, or fat levels in addition to the survival rate of Nile tilapia was high in all treatments. Hence, HFM can be replaced by PBM as a cheap source of protein in Nile tilapia diets because no significant effect for both HFM and PBM on pellet quality, machine performance and fish growth

Issues concerning optimization of non-conventional parameters in manufacturing

There are several parameters regarding the manufacture of an item such as machine setup cost, carrying cost, demand and production rate etc. Traditionally, manufacturing batch size or production cycle time is optimized after formulation of the total relevant cost. A discussion is made in the present paper concerning variation of non-conventional parameters such as production rate, on the basis of available literature. Implications related to variation of demand of an item are also mentioned. Focus is on discussion and formulation of the problem. Complete solution methods are not provided. However various available non-linear programming methods may be applied such as univariate method, penalty function method etc. in addition to classical optimization technique.

Average Cost Optimal Policy for a Stochastic Two-Machine Flowshop with Limited Work-in-Process

We consider a production planning problem in a two-machine flowshop subject to breakdown and repair of machines and subject to nonnegativity and upper bound constraints on work-in-process. The objective is to choose machine production rates over time to minimize the long-run average inventory/backlog and production costs. For sufficiently large upper bound on the work-in-process, the problem is formulated as a stochastic dynamic program. We then establish a verification theorem and a partial characterization of the optimal control policy if it exists.

Development of a new machine system for the forming of micro-sheet-products

Challenges concerning industrial applications of microforming have been recognised, and efforts are being made to meet these challenges. For micro-sheet forming, fundamentals related to the development of an industrial system are being examined further by the EU MASMICRO consortium, based on which prototype micro-sheet-forming machine system has been developed. The system development addresses the specific issues concerning machine dynamics, material feeding, production rate, tool design and fabrication, and part transport. The prototype system has been tested with the forming of demonstration components and an industrial version of the machine has been designed.