Multiple Process Routes Research Articles

Simulation Model of a Steelmaking-Continuous Casting Process Based on Dynamic-Operation Rules.

The steelmaking-continuous casting process (SCCP) is a complex manufacturing process which exhibits the distinct features of process manufacturing. The SCCP involves a variety of production elements, such as multiple process routes, a wide array of smelting and auxiliary devices, and a variety of raw and auxiliary materials. The production-simulation of SCCP holds a natural advantage in being able to accurately depict the intricate production behavior involved, and this serves as a crucial tool for optimizing the production operation of the SCCP. This paper thoroughly considers the various production elements involved in the SCCP, such as the fluctuation of the converter smelting cycle, fluctuation of heat weight, and ladle operation. Based on the Plant Simulation software platform, a dynamic simulation model of the SCCP is established and detailed descriptions are provided regarding the design of an SCCP using dynamic-operation rules. Additionally, a dynamic operational control program for the SCCP is developed using the SimTalk language, one which ensures the continuous operation of the caster in the SCCP, using the discrete simulation platform. The effectiveness of the proposed dynamic simulation model is verified by the total completion time of the production plan, the transfer time of the heat among the different processes, and the frequency of ladle turnover. The simulation's results indicate that the dynamic simulation model has a satisfactory effect in simulating the actual production process. On this basis, the application effects of different schedules are compared and analyzed. Compared with a heuristic schedule, the optimized schedule based on the "furnace-machine coordinating" mode reduces the weighted value of total completion time by 8.7 min, reduces the weighted value of transfer waiting time by 45.5 min, and the number of rescheduling times is also reduced, demonstrating a better application effect and verifying the optimizing effect of the "furnace-machine coordinating" mode on the schedule.

Operation risk assessment of Flexible Manufacturing Networks subject to quality-reliability coupling

In flexible manufacturing network (FMN), the growth of product types increases the diversity of interacting behaviors between machine reliability and product quality, and the product diversity and machine flexibility promote the explosion of processing routes and processing sequences, increasing the gap between current risk evaluation of manufacturing systems and engineering practice. To fill this gap, a novel operation risk assessment framework is constructed for the complex FMN. In detail, to model the reliability-quality interactions, a mathematical framework describing a complete manufacturing process involving "feedstock quality (Q1) - machine degradation (R) - product processing quality (Q2) - quality inspection status (I)" is proposed for complex FMN, solving the measurement of interacting behaviors between machine reliability and product quality in an FMN that suffers from both multiple process routes and multi-type products. Then, an effective evaluation framework is proposed to evaluate the connectivity risk of multiple process routes and the quality risk of multiple product types in a complex FMN, providing a concurrent solution for risk evaluation under both complex system structures and complex quality-reliability interrelationships. At last, a simulation experiment verifies the effectiveness of our proposed methods and reveals differences in the operation risk of different product types in an FMN.

Analyzing the Phase Evolution, Microstructure and Wear Response of Spark Plasma Sintered Al0.5CoCrFeNi2 High Entropy Superalloy

High entropy superalloys (HESA), an extension of high entropy alloys (HEAs), exhibit excellent high‐temperature properties with low density, making them a potential replacement for expensive and heavy superalloys. While multiple processing routes are available for HEAs, spark plasma sintering is becoming increasingly popular due to its ability to fabricate alloy powder in short period of time. Herein, the phase evolution and microstructural development of gas atomized Al0.5CoCrFeNi2 HESA powder fabricated using spark plasma sintering and the wear properties of the sintered specimen are reported. The alloy powder and all the specimen sintered in the range of 800–1050 °C show an extremely stable single‐phase face centered cubic structure. Relative density of 99% is achieved at 1000 °C and above with porosity pinning effect playing an important role in densification behavior and grain growth. In all the sintered specimen, a combination of adhesive, delamination, oxidation, and abrasive wear is observed, with the coefficient of friction lying in the range of 0.6–0.7.

High-throughput rapid experimental alloy development (HT-READ)

The current bulk materials discovery cycle has several inefficiencies from initial computational predictions through fabrication and analyses. Materials are generally evaluated in a singular fashion, relying largely on human-driven compositional choices and analysis of the volumes of generated data, thus also slowing validation of computational models. To overcome these limitations, we developed a high-throughput rapid experimental alloy development (HT-READ) methodology that comprises an integrated, closed-loop material screening process inspired by broad chemical assays and modern innovations in automation. Our method is a general framework unifying computational identification of ideal candidate materials, fabrication of sample libraries in a configuration amenable to multiple tests and processing routes, and analysis of the candidate materials in a high-throughput fashion. An artificial intelligence agent is used to find connections between compositions and material properties. New experimental data can be leveraged in subsequent iterations or new design objectives. The sample libraries are assigned unique identifiers and stored to make data and samples persistent, thus preventing institutional knowledge loss.

Simulation and assessment of manufacturing ethylene carbonate from ethylene oxide in multiple process routes

• Two processes of manufacturing EC from EO without EO purification are developed. • The rigorous models are established for the developed two processes. • Various schemes for absorbing EO are designed to reduce the liquid–vapor ratio. • Material chain analysis is performed to explore connections of each section of the processes. Ethylene oxide (EO) is an important raw material for producing ethylene carbonate (EC). However, the traditional method for the separation of EO from mixture gas by water in the refining process is high energy consumption. In this paper, two processes of manufacturing EC from EO mixture gas were studied by process simulation. Two processes for producing EC from EO mixture as raw materials without EO purification, called the OSAC process and the Modified OSAC process, were developed and assessed systematically. Both processes use EC as the absorbent to capture EO, avoiding the separation process of EO from solution. For comparisons, the EC producing process containing EO absorption by water, EO refinement and carbonylation process were also modeled, which was called the ERC process. Three schemes were designed for the EO absorber using EC as absorbent. Compared with the initial absorber scheme, the optimal liquid–vapor ratio is reduced from 1.66 to 1.45 (mass). Moreover, the mass distribution analysis for the three processes were carried out in the form of the material chain. It was found that, compared with the ERC process, the energy consumption of the OSAC and the Modified OSAC process is reduced by 56.89% and 30.03%, respectively. This work will provide helpful information for the industrialization of the OSAC process.

Cellular Manufacturing Scheduling in the Presence of Multiple Process Routings and Considering Job Splitting

Cellular Manufacturing Scheduling in the Presence of Multiple Process Routings and Considering Job Splitting

A queuing theory- based approach for designing cellular manufacturing systems

This paper presents a new cell formation and cell layout problem considering multiple process routings and subcontracting using the principles of queuing theory. It is assumed that each machine operates as an M/M/1 queuing system and a queuing network is used to obtain in-process inventories and machine utilization. The problem is formulated as a mixed-integer nonlinear program with the objective of minimizing the total costs, including the production, subcontracting, material handling, machine idleness, and holding costs. Due to the computational complexity of the problem, a heuristic method is suggested to effectively solve the problem. A numerical example is given to clarify the proposed approach, and finally, further instances are solved to verify the performance of the solution method and to accomplish comparisons. The computational results show that the proposed heuristic is both effective and efficient.

Genetic Algorithm approach for Machine Cell Formation with Alternative Routings

In cellular manufacturing systems, study and optimization of machine cell formation (CF) problems have long drawn attention of researchers. Optimum CF results in reduction of overall processing time, material handling cost, labor cost, in-process inventories and number of set-ups requirements. Also, it simplifies process plans and improves product quality. Since the modern manufacturing machines in a cell are generally multifunctional, the processing of parts are performed following alternative processing routes. The objective of study is to determine the optimal alternative processing route in order to minimize the total intercellular movements of parts in CF problems. Intercellular movements of parts depend on many factors such as parts volume including batch size and number of batches, sequence of processes and routes of production. In this paper, a genetic algorithm heuristic is presented for the CF problem with multiple process routes, sequence of processes and parts volume. Computational experimentation was performed with five benchmark problems. The results demonstrate that the performance of the proposed approach in terms of total intercellular movements of parts and best route selection are either better or competitive with the well-known existing methods.

Machine Cell Formation with Alternative Routings based on Genetic Algorithm

In automated batch type production systems, machine-part cell formation problems (CFP) have long drawn attention of researchers. The objective of CFP in cellular manufacturing system (CMS) is to identity machine cells and part families in order to minimize the intercellular movements of parts as well as maximize the utilization of machines. Optimum cell formation results reduction in total production times, in-process inventories, material handling cost, labor cost/times, paper works, number of machine set-ups, set-up times. It also simplifies process plans, management and improves product quality, productivity, utilization of resources. Since the modern manufacturing machines are generally multifunctional, so the processing of parts can be performed by number of alternative routes. The objectives of this study are to determine the optimal machine cells with optimal processing route and balanced machine cells (minimum cell load variation). Here a genetic algorithm heuristic approach is presented for six benchmark problems with multiple process routes, sequence of processes and parts volume. Computational outcome show that the proposed heuristic gives better result comparatively with the well-known existing methods in terms of total intercellular movements.

A two-stage stochastic model for designing cellular manufacturing systems with simultaneous multiple processing routes and subcontracting

In recent decades, many researchers have studied the cellular manufacturing system with consideration of various issues such as scheduling, production planning, layout, reliability, etc. However, limited research papers have investigated this problem in an uncertain environment. The present paper addresses a stochastic problem in cellular manufacturing systems considering simultaneous multiple routings and subcontracting. In the developed problem, each part can be simultaneously produced in multiple processing routes. It is also assumed that the unsatisfied part demands as a result of limited machine capacity or high manufacturing cost could be outsourced. A two-stage stochastic programming approach is employed to take the uncertainty into consideration and to formulate the problem. The objective function is to minimize the summation of production, subcontracting, material handling, and machine idleness costs. A sample average approximation method is applied as a solution method. Also, for further illustration of the problem, a numerical example is solved and sensitivity analyses are conducted. Finally, through some numerical examples extracted from related literature, the advantages of constructing a stochastic optimization model for the problem are demonstrated.

A Heuristic Approach for Machine Cell Formation Problems with Alternative Routings

Cell Formation (CF) is an important problem in today's automated batch type production systems. It reduces material handling cost, processing time, labor requirement, in-process inventories, number of set-ups, simplifies process plan, and increases quality of product. Manufacturing equipments of automated manufacturing systems are highly multifunctional. As a result, production processes can be done by multiple process routes. Optimum cell formation can lead to more independent cells and less intercellular movement of parts. Previous research reveals that most of the researchers considered single route, unique volume of parts, non-sequential Cell Formation Problem (CFP). However, intercellular movements of parts depend on parts volume, sequence of processes, and routes. In this paper, a heuristic approach based on Euclidean Distance matrix is proposed for CF in multiple routes, process sequential and parts volume (included with batch size and number of batches) problems. Computational experiments were performed with five benchmark problem sets taken from the literature and the results demonstrate that the performances of the proposed heuristic in terms of intercellular movements of parts are either better than or competitive with the well-known existing algorithms.

Laser-assisted Processing and Diagnostics of Functional Micro/Nanomaterials

Lasers have proven to be efficient tools in a variety of micro/nanoscale materials processing and diagnostics. This article will be focused on presenting recent progress on producing functionalized micro/nanomaterials for various application fields via multiple laser-assisted processing routes including ablation, crystallization, sintering, and chemical vapor deposition. In addition, demonstrated strategies on the advanced in-situ laser processing monitoring under electron microscopes will be also shown.

Design of robust cellular manufacturing system for dynamic part population considering multiple processing routes using genetic algorithm

In this paper, a comprehensive mathematical model is proposed for designing robust machine cells for dynamic part production. The proposed model incorporates machine cell configuration design problem bridged with the machines allocation problem, the dynamic production problem and the part routing problem. Multiple process plans for each part and alternatives process routes for each of those plans are considered. The design of robust cell configurations is based on the selected best part process route from user specified multiple process routes for each part type considering average product demand during the planning horizon. The dynamic part demand can be satisfied from internal production having limited capacity and/or through subcontracting part operation without affecting the machine cell configuration in successive period segments of the planning horizon. A genetic algorithm based heuristic is proposed to solve the model for minimization of the overall cost considering various manufacturing aspects such as production volume, multiple process route, machine capacity, material handling and subcontracting part operation.

Clustering algorithm for solving group technology problem with multiple process routings

Cell formation is an important problem in the design of a cellular manufacturing system. Most of the cell formation methods in the literature assume that each part has a single process plan. However, there may be many alternative process plans for making a specific part, specially when the part is complex. Considering part multiple process routings in the formation of machine-part families in addition to other production data is more realistic and can produce more independent manufacturing cells with less intercellular moves between them. A new comprehensive similarity coefficient that incorporates multiple process routings in addition to operations sequence, production volumes, duplicate machines, and machines capacity is developed. Also, a clustering algorithm for machine cell formation is proposed. The algorithm uses the developed similarity coefficient to calculate the similarity between machine groups. The developed similarity coefficient showed more sensitivity to the intercellular moves and produced better machine grouping.

Integration of Process Routes Planning and Scheduling Based on Network Diagram and Ant Colony Algorithm

The integration method based on network diagram and ant colony algorithm was proposed to realize integration of process routes planning and scheduling. A model of multiple process routes and resource (MPRR) network diagram was built, which applied the network diagram expression and represented the precedence constraint relationship among operations and resource conditions. In the model, the workpiece, operation and machine were joined together as a basic optimization cell to realize concurrent and holistic optimization for process routes planning and scheduling. The combination of MPRR network diagram and characteristics of ant colony algorithm was developed for generating optimal process routes and scheduling project. An example was studied to illustrate the effectiveness of the strategy.

Integrated Modeling of Process Routes Planning and Scheduling Based on Network Diagram

Modeling method based on network diagram was proposed to realize integrated modeling of process routes planning and scheduling. A model of multiple process routes and resource (MPRR) network diagram was built, which applied the network diagram expression and represented the precedence constraint relationship among operations and resource conditions. In the model, the workpiece, operation and machine are joined together as a basic optimization cell to realize concurrent and holistic optimization for process routes planning and scheduling. An example was studied and the integrated model of process routes planning and scheduling was built.

A PSO-based approach to cell formation problems with alternative process routings

Group technology (GT) has been extensively applied to cellular manufacturing system (CMS) design for decades due to many benefits such as decreased number of part movements among cells and increased machine utilisation in cells. This paper considers cell formation problems with alternative process routings and proposes a discrete particle swarm optimisation (PSO) approach to minimise the number of exceptional parts outside machine cells. The approach contains two main steps: machine partition and part-routing assignment. Through inheritance and random search, the proposed algorithm can effectively partition machines into different cells with consideration of multiple part process routings. The computational results are compared with those obtained by using simulated annealing (SA)-based and tabu search (TS)-based algorithms. Experimental results demonstrate that the proposed algorithm can find equal or fewer exceptional elements than existing algorithms for most of the test problems selected from the literature. Moreover, the proposed algorithm is further tailed to incorporate various production factors in order to extend its applicability. Four sample cases are tested and the results suggest that the algorithm is capable of solving more practical cell formation problems.

Amygdala damage affects event‐related potentials for fearful faces at specific time windows

The amygdala is known to influence processing of threat‐related stimuli in distant brain regions, including visual cortex. The time‐course of these distant influences is unknown, although this information is important for resolving debates over likely pathways mediating an apparent rapidity in emotional processing. To address this, we recorded event‐related potentials (ERPs) to seen fearful face expressions, in preoperative patients with medial temporal lobe epilepsy who had varying degrees of amygdala pathology, plus healthy volunteers. We found that amygdala damage diminished ERPs for fearful versus neutral faces within the P1 time‐range, ∼100–150 ms, and for a later component at ∼500–600 ms. Individual severity of amygdala damage determined the magnitude of both these effects, consistent with a causal amygdala role. By contrast, amygdala damage did not affect explicit perception of fearful expressions nor a distinct emotional ERP effect at 150–250 ms. These results demonstrate two distinct time‐points at which the amygdala influences fear processing. The data also demonstrate that while not all aspects of expression processing are disrupted by amygdala damage, there is a crucial impact on an early P1 component. These findings are consistent with the existence of multiple processing stages or routes for fearful faces that vary in their dependence on amygdala function. Hum Brain Mapp, 2010. © 2009 Wiley‐Liss, Inc.

Hybrid simulated annealing algorithm with mutation operator to the cell formation problem with alternative process routings

In this study, a hybrid simulated annealing algorithm with mutation operator is proposed to solve the manufacturing cell formation problem considering multiple process routings for parts, so that either the intercellular movements are minimized or the grouping efficacy is maximized, depending on the definition of the decision objective. The proposed algorithm is designed mainly to explore solution regions efficiently and to expedite the solution search process. The performance of the proposed algorithm is tested by a range of test problems, some of which are from the literature and some of which are generated within this study. The comparative study shows that the proposed algorithm improves the best results found in the literature for 28.6% of the test problems and the percentages of improvement are even higher than 18% in several test instances.

A mathematical model for job shop scheduling with multiple process plan consideration per job

In this paper, a mixed integer programming model is formulated for scheduling a set of jobs through a shop when each job is supplied or provided with multiple process plans or process routings. Simultaneous selection of a process plan for each job and the sequencing of the jobs through the machines in the shop based on the set of selected process plans is addressed. The procedure developed seeks to integrate the selection of machines for each job and the sequencing of jobs on each machine based on the objective of minimizing production makespan. the application of the procedure is demonstrated with an example problem. The following conclusions were drawn as a result of the research: (1) the procedure developed produces optimal or near optimal solution; (2) the benefit from the developed approach is that it allows a shop to adaptively select process plans for jobs to optimize on production makespan. By combining solution quality with scheduling flexibility and efficiency, the productivity of a shop can be greatly enhanced.