Quality issue in forecasting problem of production and maintenance policy for production unit

In this study, we consider an unreliable deteriorating production system that produces conforming and non-conforming products to satisfy a random demand under a given service level and during a finite horizon. The production system is subjected to a failure-prone machine. The quality of the produced products is affected by the machine deterioration since the rate of defectives increases as the deterioration increases. Preventive maintenance actions can be piloted on the production system to reduce the influence of deterioration and the defective rate. A joint control policy is based on a stochastic production and maintenance planning problem with goals to determine, firstly, the economic plan of production and secondly, the optimal maintenance strategy. The proposed jointly optimisation minimises the total cost of production, inventory, maintenance and defectives. A failure rate and quality relationship are defined to show the influence of the production rates variation on the failures rate as well as on the defective rate. A numerical example and an industrial case study are adopted to illustrate the proposed approach and a sensitivity analysis to validate the jointly optimisation.     

Architectural design of a sensory node controller for optimized energy utilization in sensor networks

The increasing complexity of manufacturing machines and the continued demand for high productivity have led to growing applications of sensor networks to enable more reliable, timely, and comprehensive information gathering from the machines being monitored. An effective and efficient utilization of sensor networks requires new sensor designs that enable adaptive event-driven information gathering based on the condition of the machines, as well as a coordinated information distribution adjusted to the available communication bandwidth of the network. This paper investigates several fundamental aspects regarding the architectural design of a sensory node controller (SNOC). The SNOC is the key element in a large-scale sensor network that coordinates the operation of individual sensors and the communication among various sensing clusters to realize distributed intelligent sensing. A parametric SNOC design that dynamically adjusts the power supply and the data-acquisition procedure to reduce the overall energy consumption of the sensor network is presented. Considerations on both the hardware and software aspects of the design to achieve energy efficiency are described, and analytical formulations are derived. Simulation results for a sensor network consisting of 40 SNOCs, each coordinating eight physical sensors, have shown that the design is able to reduce the energy consumption by about 43%, as compared to traditional techniques. A prototype SNOC was designed and implemented, based on the platform of a commercially available microcontroller, and experimentally tested for its ability to dynamically adjust the power consumption. The study has provided a concrete input to the design optimization and experimental realization of an SNOC-based sensor network for machine-system monitoring.     

A cost‐based selective maintenance decision‐making method for machining line

Machine line is a type of manufacturing system in which machines are connected in series or in parallel. It is significant to ensure the reliability as well as to reduce the total cost of maintenance and failure losses in the maintenance programs of such systems. Cost‐based selective maintenance decision‐making, which is the best method for a selected group of machines in machine line is presented under limited maintenance durations. Fault losses and maintenance costs of a single machine under different maintenance actions i.e. minimal repair, preventive maintenance and overhaul on the fault rate of the machine are calculated. An algorithm combining the heuristic rules and tabu search is proposed to solve the presented selective maintenance model. Finally, a case study on the maintenance decision‐making problem of a connecting rod machining line in the automobile engine workshop is presented to illustrate the applicability of the proposed method. The end result shows that the fault losses can be further reduced by the optimization of maintenance interval and maintenance duration. Copyright © 2010 John Wiley & Sons, Ltd.     

Improving energy efficiency in Bernoulli serial lines: an integrated model

In this paper, we present an integrated model of both productivity and energy consumptions in serial production lines with two machines. Bernoulli reliability is assumed for both machines and the capacity of the buffer is finite. The energy consumption of each machine includes the energy required to set up the machine until it is ready for processing, and the additional energy needed to carry out the processing operation to make the product. The former is typically fixed for a specific manufacturing process, while the latter is proportional to the processing rate. The objective of the model is to minimise energy consumption, while maintaining the desired production rate. Specifically, analytical investigation has been carried out to discover the conditions that energy consumption can be minimised with and without the constraints of workforce or machine processing capability. Optimal allocations of them under different scenarios have been derived. Insights for reducing energy consumption while still ensuring desired productivity have been obtained.     

Cleaner energy production with integrated gasification combined cycle systems and use of metal oxide sorbents for H2S cleanup from coal gas

 World primary energy demand increases with increases in population and economic development. Within the last 25 years, total energy consumption has almost doubled. In order to meet this demand, research into new sources of energy as well as improving the efficiency of energy production technologies is being carried out. In both cases, the production of clean energy is very important because of environmental concerns and regulations. Integrated gasification combined cycle (IGCC) technology seems to be one of the most promising technologies for production of energy by using fossil fuels, especially coal. Hot gas desulfurization is a crucial issue in the development of the IGCC system. This paper reviews the importance of clean energy production, the IGCC technology and focuses on the development of several metal oxide-based sorbents used for desulfurization of the hot coal gas in the IGCC system.     

Power-level sampling of metal cutting machines for data representation in discrete event simulation

An extension to the application area for discrete event simulation (DES) has been ongoing since the last decade and focused only on economic aspects to include ecologic sustainability. With this new focus, additional input parameters, such as electrical power consumption of machines, are needed. This paper aim at investigating how NC machine power consumption should be represented in simulation models of factories. The study includes data-sets from three different factories. One factory producing truck engine blocks, one producing brake disc parts for cars and one producing forklift components. The total number of data points analysed are more than 2,45,000, where of over 1,11,000 on busy state for 11 NC machines. The low variability between busy cycles indicates that statistical representations are not adding significant variability. Furthermore, results show that non-value-added activities cause a substantial amount of the total energy consumption, which can be reduced by optimising the production flow using dynamic simulations such as DES.     

Effect of maintenance policies on the just-in-time production system

Using simulation, experimental design, and regression analysis, mathematical models are developed here to describe the effect that maintenance policy, machine unreliability, processing time variability, ratio of preventive maintenance time to processing time, ratio of minimal repair time to preventive maintenance time, and production line size have on various measures of performance, namely total production line output and production line variability of the just-in-time production system. The analysis of the data shows that under different situations, different maintenance policies do not have the same effect on the production line performance. The following conclusions were obtained; when the number of production machines is low (five machines or less), and/or when the ratio of minimal repair time to preventive maintenance time is high, maintenance policy III leads to a higher performance than maintenance policy II. Otherwise maintenance policy II, which is more sensitive to the change of the ratio of minimal repair time to preventive maintenance time, leads to a higher performance. The results of the study can be utilized in choosing a maintenance policy as a function of the production process parameters. Once a policy is chosen, the practitioner can select the most important factors to control under that policy in order to minimize the machine idle time, maximize the production process reliability, improve productivity, and therefore increase the production line performance.     

Optimization-based manufacturing scheduling with multiple resources, setup requirements, and transfer lots

The increasing demand for on-time delivery of products and low production cost is forcing manufacturers to seek effective schedules to coordinate machines and operators so as to reduce costs associated with labor, setup, inventory, and unhappy customers. This paper presents the modeling and resolution of a job shop scheduling system for J. M. Products Inc., whose manufacturing is characterized by the need to simultaneously consider machines and operators, machines requiring significant setup times, operators of different capabilities, and lots dividable into transfer lots. These characteristics are typical for many manufacturers, difficult to handle, and have not been adequately addressed in the literature. In our study, an integer optimization formulation with a separable structure is developed where both machines and operators are modeled as resources with finite capacities. Setups are explicitly considered following our previous work with additional penalties on excessive setups. By analyzing transfer lot dynamics, transfer lots are modeled by using linear inequalities. The objective is to maximize on-time delivery of products, reduce inventory, and reduce the number of setups. By relaxing resource capacity constraints and portions of precedence constraints, the problem is decomposed into smaller subproblems that are effectively solved by using a novel dynamic programming procedure. The multipliers are updated using the recently developed surrogate subgradient method. A heuristic is then used to obtain a feasible schedule based on subproblem solutions. Numerical testing shows that the method generates high quality schedules in a timely fashion.     

冷冻食品加工企业生产能效评估及节能分析

目前我国多数冷冻食品加工企业能耗费用支出占总产值的比率高于企业利润率,能耗费用高支出成为阻碍企业利润率增长的瓶颈。因为企业生产能耗偏大,所以节能降耗成为企业降低成本提高利润率的有效手段。文章列举实例分析了某速冻车间的能耗与产量现状,进行能效评估,指出了目前我国一些冷冻食品加工企业仅凭增加产量来提高利润率的错误;找出了能效、产量、利润率三者之间的关系和节能降耗的关键;系统地分析了如何节能,并且给予了合理性的建议。     

Development of joint maintenance and production strategies in a subcontracting environment

This article, inspired by an industrial problem, develops efficient maintenance and just-in-time production policies in a subcontracting environment according to two orientations. The first invokes subcontracting with the objective of satisfying a constant customer demand knowing that our production system, composed of a machine M ₁, cannot satisfy the totality of demand. Subcontracting is represented by a machine M ₂ which has a constant failure rate, while three maintenance policies for M ₁ are tested and evaluated. The second orientation takes the perspective of our production system as a supplier which is obliged to allocate part of its production capacity to subcontracting so as to satisfy a constant demand. We consider a production system made up of two machines, both of which produce a single type of product, are subject to breakdowns and can carry out subcontracting tasks. The objective of this part of the article is to prove the efficiency of the so-called integrated maintenance policy, which combines production and maintenance decisions in a subcontracting environment.     

A maintenance driven scheduling cockpit for integrated production and maintenance operation schedule

The production and maintenance functions have objectives that are often in contrast and it is essential for management to ensure that their activities are carried out synergistically, to ensure the maximum efficiency of the production plant as well as the minimization of management costs. The current evolution of ICT technologies and maintenance strategies in the industrial field is making possible a greater integration between production and maintenance. This work addresses this challenge by combining the knowledge of the data collected from physical assets for predictive maintenance management with the possibility of dynamic simulate the future behaviour of the manufacturing system through a digital twin for optimal management of maintenance interventions. The paper, indeed, presents a supporting digital cockpit for production and maintenance integrated scheduling. The tool proposes an innovative approach to manage health data from machines being in any production system and provides support to compare the information about their remaining useful life (RUL) with the respective production schedule. The maintenance driven scheduling cockpit (MDSC) offers, indeed, a supporting decision tool for the maintenance strategy to be implemented that can help production and maintenance managers in the optimal scheduling of preventive maintenance interventions based on RUL estimation. The simulation is performed by varying the production schedule with the maintenance tasks involvement; opportune decisions are taken evaluating the total costs related to the simulated strategy and the impact on the production schedule.The full text can be downloaded at https://link.springer.com/article/10.1007/s40436-021-00380-z     

Energy Prediction in IoT Systems Using Machine Learning Models

The Internet of Things (IoT) technology has been developed for directing and maintaining the atmosphere in smart buildings in real time. In order to optimise the power generation sector and schedule routine maintenance, it is crucial to predict future energy demand. Electricity demand forecasting is difficult because of the complexity of the available demand patterns. Establishing a perfect prediction of energy consumption at the building’s level is vital and significant to efficiently managing the consumed energy by utilising a strong predictive model. Low forecast accuracy is just one of the reasons why energy consumption and prediction models have failed to advance. Therefore, the purpose of this study is to create an IoT-based energy prediction (IoT-EP) model that can reliably estimate the energy consumption of smart buildings. A real-world test case on power predictions is conducted on a local electricity grid to test the practicality of the approach. The proposed (IoT-EP) model selects the significant features as input neurons, the predictable data is selected as output nodes, and a multi-layer perceptron is constructed along with the features of the Convolution Neural Network (CNN) algorithm. The analysis of the proposed IoT-EP model has higher accuracy of 90%, correlation of 89%, and variance of 16% in less training time of 29.2 s, and with a higher prediction speed of 396 (observation/sec). When compared to existing models, the results showed that the proposed (IoT-EP) model outperforms with a satisfactory level of accuracy in predicting energy consumption in smart buildings.     

基于S7-300的瓦楞纸温控系统设计

基于瓦楞纸板生产节能降耗、提升纸板质量的需求,设计了瓦楞纸板生产线的原纸温度控制系统,实现纸板的低温生产。描述了系统工作原理,给出了系统的硬件配置以及PLC和HMI的软件设计;投入使用后的运行数据表明:系统能节省蒸汽消耗,提高产品质量,在瓦楞包装行业有很好的使用前景。     

Inspection Policies for A System which Is Inoperative During Inspection Periods

Inspection policy models deal with stochastically failing systems in which failures are detected by inspections only. In this paper we examine a model for a system which is inoperative during the inspection periods. This problem is important in situations where inspections demand a considerable amount of time, such as the inspection of production machines, inspection of communication systems, aircraft maintenance, etc. The costs incurred include the cost of inspections, the cost per time unit of unobserved breakdowns, and the cost of repairing (or replacing) the failed system. The objective is to minimize the expected total cost per time unit. Minimization of the system's unavailability is obtained as a special case by appropriate substitutions.     

A methodology for determining auxiliary and value-added electricity in manufacturing machines

A methodology was developed that accurately and flexibly determines the auxiliary (AU) and value-added electricity in manufacturing operations. A tool was developed for production engineers which allows for the verification of machine efficiency in relation to their energy consumption. Historical production and electricity consumption data were collected for a period of three months from four different machines in a value stream at a manufacturing facility. The data were examined using a methodology based on statistical analysis of the historical data collected and were verified using heuristic machines profiles. Results showed AU electricity consumption varied between 10 and 26% per machine. When weekend data (non-productive periods) were excluded from calculations, AU electricity consumption reduced. Past work focuses on optimising single machine, and the quantification of wasted electricity is not always clear. This research work can be applied to one or more machines, and to single or multiple products passing through the same machine. It places particular attention to AU electricity since potential energy and cost reduction of up to 20% could be achieved. Hence, this work can aid in developing key performance indicators to measure energy usage in manufacturing operations, particularly focused towards reducing AU electricity consumption.     

The effect of correlated demand on the cyclical scheduling system

This paper investigates the effect of correlated demand on the cyclical scheduling system. The paper first analytically demonstrates that a blend-to-order production environment will generally realize correlated demand for blending components, even if the demands for finished blends are mutually independent. The degree of component demand correlation is shown to depend on the nature of the recipes used to blend finished products. A simulation model is then used to investigate the effects of this correlation, for the case of blending components produced on a single process under a cyclical scheduling policy. The results demonstrate that component demand correlation increases the variance of the production cycle length, and induces correlation between the cycle length and demand per period within the cycle. Both effects contribute to an increase in the variability of total demand during the production cycle, thus increasing the requirement of safety stock for the blending components. In the environment under concern, an analysis of the cyclical scheduling system--which assumes independent random demand--will underestimate the true safety stock requirement.     

Forecasting and maintenance problem under subcontracting constraint with transportation delay

In this paper, a forecasting production/maintenance optimization problem has been proposed with a random demand and single machine M₁ on a finite horizon. The function rate of the machine M₁ is depending on the production rate for each period of the forecasting horizon. In order to satisfy the customer, a subcontracting assures the rest of the production through machine M₂ with transportation delay. An analytic formulation of the problem has been proposed using a sequential computation of the optimal production plan for which an optimal preventive maintenance policy has been calculated based on minimal repair. Firstly, we find, the optimal production plans of principal and subcontracting machines, which minimises the total production and inventory cost for the cases without and with returned products under service level and subcontracting transportation delay. Secondly, we determine a joint effective maintenance policy with the optimal production plan, which integrates the various constraints for the production rates, the transportation delay and the returned production deadline. Numerical results are presented to highlight the application of the developed approach and sensitivity analysis shows the robustness of the model.     

Capacity management under uncertainty with inter-process, intra-process and demand interdependencies in high-flexibility environments

In this paper, we develop models for capacity planning within the framework of stochastic processing times and stochastic demand for different process outcomes in high-flexibility environments. We particularly address stochastic interdependencies between processing times for different processes (inter-process correlation), interdependencies between the capacity consumption (task times) of different executions of the same task in a given production stage (intra-process correlation) as well as interdependencies between the demand for different process outcomes. After presenting the base model, we conduct extensive sensitivity analyses and analyze the main relationships between different model variables. We use process and demand data from the financial industry to demonstrate the applicability and relevance of our findings.     

Asynchronous teams for joint lot-sizing and scheduling problem in flow shops

The integrated production scheduling and lot-sizing problem in a flow shop environment consists of establishing production lot sizes and allocating machines to process them within a planning horizon in a production line with machines arranged in series. The problem considers that demands must be met without backlogging, the capacity of the machines must be respected, and machine setups are sequence-dependent and preserved between periods of the planning horizon. The objective is to determine a production schedule to minimise the setup, production and inventory costs. A mathematical model from the literature is presented, as well as procedures for obtaining feasible solutions. However, some of the procedures have difficulty in obtaining feasible solutions for large-sized problem instances. In addition, we address the problem using different versions of the Asynchronous Team (A-Team) approach. The procedures were compared with literature heuristics based on Mixed Integer Programming. The proposed A-Team procedures outperformed the literature heuristics, especially for large instances. The developed methodologies and the results obtained are presented.     

Hochtemperaturprüfung – Ein Beitrag zur Werkstoffentwicklung und ‐qualifizierung sowie Simulation der Bauteilbeanspruchung

High temperature testing – A contribution to alloy development, alloy qualification and simulation of component loading In parallel to continued developments of steam and gas turbines as well as traffic engineering machines on the one hand, and marginal conditions like low specific fuel consumption and sufficient environment‐friendliness on the other hand, the aim of improving the degree of efficiency by augmenting process parameters such as temperature and pressure is being followed. These efforts impact especially components of thermic machines and facilities subject to high thermal and mechanic exposure. Still largely unexplored is the interaction between microstructure characteristics determined through chemical composition, production processes and heat treatment, changes in the microstructure due to multiaxial load and the time‐dependent deformation and stability resulting hereof. With regard to this background, improved methods of material properties determination, their modelling and transfer on the component enable to optimize wall thicknesses and degrees of efficiency. In the course of evaluation of static and cyclic material properties carried out also on faulty specimens, uncertainties occur which can originate from the testing process and analysis, as well as being influenced by the material itself and its process of production. Altogether, the demand for reliable determination of material properties and methods of scatterband treatment and their mathematical‐statistical evaluation is in business. For simulation, consistent material datasets that describe the complex interaction between temperature, period of exposure and type of exposure are needed. Summarizing, the tasks dealt with qualify the entire process from production to the operational behaviour of components.