Scalability planning for reconfigurable manufacturing systems

Scalability is a key characteristic of reconfigurable manufacturing systems, which allows system throughput capacity to be rapidly and cost-effectively adjusted to abrupt changes in market demand. This paper presents a scalability planning methodology for reconfigurable manufacturing systems that can incrementally scale the system capacity by reconfiguring an existing system. An optimization algorithm based on Genetic Algorithm is developed to determine the most economical way to reconfigure an existing system. Adding or removing machines to match the new throughput requirements and concurrently rebalancing the system for each configuration, accomplishes the system reconfiguration. The proposed approach is validated through a case study of a CNC-based automotive cylinder head machining system.     

Simulated annealing with auxiliary knowledge for process planning optimization in reconfigurable manufacturing

In this paper, three simulated annealing based algorithms that exploit auxiliary knowledge in different ways are devised and employed to handle a manufacturing process planning problem for reconfigurable manufacturing. These algorithms are configured based on a generic combination of the simulated annealing technique with; (a) heuristic knowledge, and (b) metaknowledge. Capabilities of the implemented algorithms are tested and their performances compared against a basic simulated annealing algorithm. Computational and optimization performances of the implemented algorithms are investigated and analyzed for two problem sizes. Each problem size consists of five different forms of a manufacturing process planning problem. The five forms are differentiated by five alternative objective functions. Experimental results show that the implemented simulated annealing algorithms are able to converge to good solutions in reasonable time. A computational analysis indicates that significant improvements towards a better optimal solution can be gained by implementing simulated annealing based algorithms that are supported by auxiliary knowledge.     

Enhanced simulated-annealing-based algorithms and their applications to process planning in reconfigurable manufacturing systems

Capabilities of enhanced simulated-annealing-based algorithms in solving process planning problems in reconfigurable manufacturing are investigated. The algorithms are enhanced by combining variants of the simulated annealing technique with other algorithm concepts such as (i) knowledge exploitation and (ii) parallelism. Four configurations of simulated annealing algorithms are devised and engaged to solve an instance of a process planning problem in reconfigurable manufacturing systems. These configurations include; a basic simulated annealing algorithm, a variant of the basic simulated annealing algorithm, a variant of the simulated annealing algorithm coupled with auxiliary knowledge and a variant of the simulated annealing algorithm implemented in a quasi-parallel architecture. Although differences in performances were observed, the implemented algorithms are capable of obtaining good solutions in reasonable time. Experimental results show that the performances of the variants of simulated annealing based algorithms are better in comparison to a basic simulated annealing algorithm. A computational analysis and comparison using ANOVA indicates that improvements towards a better optimal solution can be gained by implementing variants of the simulated annealing algorithm. In addition, little speed gains can be obtained by implementing variants of the simulated annealing algorithms that are coupled with other algorithmic concepts.     

Hierarchical production planning for complex manufacturing systems

A hierarchical approach to production planning for complex manufacturing systems is presented. A single facility comprising a number of work-centers that produce multiple part types is considered. The planning horizon includes a sequence of time periods, and the demand for all part types is assumed known. The production planning problem consists of minimizing the holding costs for all part types, as well as the work-in-process and the backlogging costs for the end items. We present a two-level hierarchy that is based on aggregating parts to part families, work-centers to manufacturing cells and time periods to aggregate time periods. The solution at the aggregate level is imposed as a constraint to the detailed level problems which are formulated for each manufacturing cell separately. This architecture uses a rolling horizon strategy to perform the production management function. We have employed perturbation analysis techniques to adjust certain parameters of the optimization problems at the detailed level to reach a near-optimal detailed production plan. Numerical results for several realistic example problems are presented and the solutions obtained from the hierarchical and monolithic approaches are compared. The results indicate that the hierarchical approach offers major advantages in computational efficiency, while the loss of optimality is acceptable.     

Intelligent production planning for complex garment manufacturing

Apparel production is characterised by labour-intensive manual operations, frequent style changes, seasonal demand and shortening production lead times. With fierce competition worldwide, many manufacturers are switching their production from mass mode to lean mode to shorten their response time to changes. In a complex mixed mode production environment, it is very important to allocate job orders to suitable production lines so as to ensure the effective utilization of production resources and on-time completion of all job orders. In this paper, planning algorithms are proposed for automatic job allocations based on group technology and genetic algorithms. For genetic algorithms based intelligent planning algorithms, single-run and multiple-run genetic algorithms are suggested. Real production data are used to validate the proposed method. The proposed algorithms has been shown being able to substantially improve planning quality. These planning algorithms are currently used by apparel manufacturers in Hong Kong as part of their routine planning operations.     

A distributed architecture for reconfigurable control of continuous process operations

This paper considers a new distributed approach to reconfigurable control of continuous process operations such as in chemical plants. The research is set on a premise that emerging business pressures of product customization and industrial globalization will lead to increased need for reconfigurability in process plants. The ability of processes to support dynamic and smooth reorganization of process schemes in tandem with the changing requirements of supply chains will become important in future. Conventional control approaches based on hierarchical architectures are limited in dealing with such emerging requirements due to their inflexible structures and operating rules. Instead, more distributed approaches are required which can support increased level of reconfigurability in control systems, especially at the lower levels in hierarchy where the visibility to disturbances remains high. In this paper, one such distributed approach is considered based on the concepts of holonic manufacturing and supply chain management. The proposed approach distributes the functionality of process control into several reconfigurable process elements. These elements, while having a stand-alone capability for making their own control decisions, are also able to reconfigure themselves into alternative process schemes which evolve with the changing requirements of production. An analogy between process plants and so-called dynamic supply networks or virtual enterprises is used in this paper to define the composition of reconfigurable process elements and their operations. The proposed approach is shown to offer improved process control system reconfigurability and a control architecture which is compatible with the supply chain management needs at the next higher level. The purpose of this paper is qualitative and motivational. It is aimed to propose a new research direction in the field of reconfigurable process control.     

Neural-network process modeling of a continuous manufacturing operation

Neural-network techniques for the development of models of critical parameters in continuous forest products manufacturing processes are described. Predictive models of strength parameters in particleboard manufacturing were developed utilizing both backpropagation and counterpropagation neural network techniques. The modeled strength parameters were modulus of rupture and internal bond. The backpropagation neural network model did not provide sufficient accuracy in predicting the values of the strength parameters. Counterpropagation was successful at predicting modulus of rupture within ± 10% and internal bond within ± 15%. The trained counterpropagation network can be used to improve process control and reduce the amount of substandard and scrap board produced. Efforts are underway to refine the counterpropagation network and further improve its predictive capability, as well as to evaluate alternative neural network paradigms.     

Incorporating sustainable criteria in a dynamic multi-objective recommendation planning tool for a continuous manufacturing process: A dairy case study

The activity of scheduling the production plan with the aim of achieving an optimal criterion has been explored in literature for several manufacturing sectors, in particular when it comes to solving scheduling NP-complete problems. In Dairy Manufacturing, determining an optimum criterion for the scheduling process has numerous internal and external challenges due to the complexity of this environment.The initial stages in the Dairy process are characterised by a continuous manufacturing environment and specific operational issues are observable: interruptions for the accomplishment of Cleaning-In-Place (CIP); a short raw material lifespan which demands a fast processing rate; and the stochastic raw material supply variation. By highlighting these three aspects, a critical trade-off emerges: CIP cycle-times heavily reduce the processing capacity, whereas the raw material processed requires an increase in available capacity due to the impact of seasonality, perishability and stochastic deliveries. Therefore, the scheduling plan must be dynamically readapted based on the current inventory, volume and frequency supplied, CIP cycle-times, maximum equipment running hours and downstream capacities.The aim of this research is to develop an integrated approach for generating equipment schedules under supply uncertainty typically observed in the dairy sector where criteria of sustainability are effortlessly incorporated for an improved decision-making process. An efficient Multi-objective Algorithm (MOA) combining conflicting key performance metrics such as minimising Work-In-Process (WIP), maximising Service Level Agreement (SLA), Utilisation and Energy consumption is proposed.The novelty consists of the ability to dynamically select trade-off criteria and visualise the optimum production plan according to the conditions defined by the decision-maker. The appropriate schedules are presented in a Pareto Frontier graph highlighting the entire non-dominance region according to the volume and frequency supplied. Even though sustainability metrics are usually ignored during production plan definitions, namely when a weak correlation between both environmental and profitable criteria is identified, the results demonstrate improved performance when both sustainable approaches are well explored.     

An adapted NSGA-2 algorithm based dynamic process plan generation for a reconfigurable manufacturing system

With burgeoning global markets and increasing customer demand, it is imperative for companies to respond quickly and cost effectively to be present and to take the lead among the competitors. Overall, this requires a changeable structure of the organization to cater to a wide product variety. It can be attained through adoption of the concept of reconfigurable manufacturing system (RMS) that comprises of reconfigurable machines, controllers and software support systems. In this paper, we propose a new approach to generate the dynamic process plan for reconfigurable manufacturing system. Initially, the requirements of the parts/products are assessed which are then compared with the functionality offered by machines comprising manufacturing system. If the production is feasible an optimal process plan is generated, otherwise the system shows an error message showing lack of functionality. Using an adapted NSGA-2 algorithm, a multi-objective scenario is considered with the aim of reducing the manufacturing cost and time. With the help of a numerical example, the efficacy of the proposed approach is demonstrated.     

Modified genetic algorithms for manufacturing process planning in multiple parts manufacturing lines

Manufacturing process planning for multiple parts manufacturing is cast as a hard optimization problem for which a modified genetic algorithm is proposed in this paper. A cyclic crossover operation for an integer-based representation is implemented to ensure that recombination will not result in any violation of processing constraints. Unlike classical approaches, in which the mutation operator alone is used to foil the tendency towards premature convergence, a combination of a neighborhood search based mutation operator and a threshold operator were implemented. This combined approach was designed to; (a) improve the exploring potential and (b) increase population diversity of neighborhoods, in the genetic search process. Capabilities of a modified genetic algorithm method were tested through an application example of a multiple parts reconfigurable manufacturing line. Simulation results show that the proposed modified genetic algorithm method is more effective in generating manufacturing process plans when compared to; a simple genetic algorithm, and simulated annealing. A computational analysis indicates that improved, near optimal manufacturing process planning solutions for multiple parts manufacturing lines can be obtained by using a modified genetic algorithm method.     

BiNem® manufacturing process for mass production

Abstract— As the need for high‐resolution ultra‐low‐power bistable displays grows, it is important to rapidly implement the mass‐production manufacturing of BiNem® LCDs. The cost‐effective approach for BiNem® manufacturing is based on using STN manufacturing process technologies since BiNem® and STN displays have a similar internal structure. The key differences between BiNem® and STN displays from a manufacturing point of view will be discussed. We show that industrial STN manufacturing equipment can be used to produce BiNem® LCDs at competitive costs. Reflective e‐book display modules with VGA resolution are produced in the pilot‐production series.     

A practical approach to a process planning expert system for manufacturing processes

In this paper the methodical techniques applied by the human process planning expertise is simulated. It considers the process plans design, or process selection. Software modules are designed to generate a process plan or several plans for a new part according to the input data from its engineering drawing. A specific module for each surface type, to match the surface parametric data and the required quantities with respect to the capability matrices, in order to locate the most eligible process plan is identified and used.     

Artificial intelligence-based sampling planning system for dynamic manufacturing process

The dynamic sampling instead of static sampling can more effectively utilize the inspection capacity for quicker excursion detection and increase the throughput of inspection machines without affecting the quality of inspection, so that achieve cycle time reduction. Accordingly, many researchers and semiconductor fabs start investigating the impacts of using dynamic sampling and there is currently a growing need for the dynamic sampling strategies in today's highly competitive semiconductor industry. Meanwhile, the use of artificial intelligence (AI) for knowledge discovery has become more common in industrial and manufacturing process control systems and recent advances in technology, particularly in networking, and information processing, have made the implementation of dynamic process sampling feasible. In this paper the optimal dynamic sampling method and the associated decision process based on AI technique are proposed and the effectiveness of them is validated through actual data sets collected from a semiconductor fabrication line. Finally, we present an AI-based dynamic sampling planning system incorporated the proposed methodology, which possesses four sub-components: wafer bin map (WBM) data mart, optimal sampling method generator (OSMG), sampling knowledge, and sampling adaptation monitor. Our research results provide an effective solution to implement a successful dynamic process sampling.     

可重构制造系统可重构逻辑控制器设计与实现

针对可重构制造系统的逻辑控制问题,提出一种可重构逻辑控制器的解决方案．该逻辑控制器具有递阶分布式的控制体系结构,并根据模块化的设计思想设计成多个分离的功能模块．然后给出基于CORBA组件模型(CCM)的可重构逻辑控制器软件的开发过程．由递阶分布式体系、模块化设计和软件组件开发技术实现的可重构逻辑控制器具有快速动态重构的能力,能满足可重构制造系统逻辑控制的要求．     

Bio-inspired multi-agent systems for reconfigurable manufacturing systems

The current market's demand for customization and responsiveness is a major challenge for producing intelligent, adaptive manufacturing systems. The Multi-Agent System (MAS) paradigm offers an alternative way to design this kind of system based on decentralized control using distributed, autonomous agents, thus replacing the traditional centralized control approach. The MAS solutions provide modularity, flexibility and robustness, thus addressing the responsiveness property, but usually do not consider true adaptation and re-configuration. Understanding how, in nature, complex things are performed in a simple and effective way allows us to mimic nature's insights and develop powerful adaptive systems that able to evolve, thus dealing with the current challenges imposed on manufacturing systems. The paper provides an overview of some of the principles found in nature and biology and analyses the effectiveness of bio-inspired methods, which are used to enhance multi-agent systems to solve complex engineering problems, especially in the manufacturing field. An industrial automation case study is used to illustrate a bio-inspired method based on potential fields to dynamically route pallets.     

A knowledge-based process planning framework for wire arc additive manufacturing

Wire arc additive manufacturing (WAAM) provides a rapid and cost-effective solution for fabricating low-to-medium complexity and medium-to-large size metal parts. In WAAM, process settings are well-recognized as fundamental factors that determine the performance of the fabricated parts such as geometry accuracy and microstructure. However, decision-making on process variables for WAAM still heavily relies on knowledge from domain experts. For achieving reliable and automated production, process planning systems that can capture, store, and reuse knowledge are needed. This study proposes a process planning framework by integrating a WAAM knowledge base together with our in-house developed computer-aided tools. The knowledge base is construed with a data-knowledge-service structure to incorporate various data and knowledge including metamodels and planning rules. Process configurations are generated from the knowledge base and then used as inputs to computer-aided tools. Moreover, the process planning system also supports the early-stage design of products in the context of design for additive manufacturing. The proposed framework is demonstrated in a digital workflow of fabricating industrial-grade components with overhang features.     

Optimizing modular product design for reconfigurable manufacturing

The problem of optimizing modular products in a reconfigurable manufacturing system is addressed. The problem is first posed as a generalized subset selection problem where the best subsets of module instances of unknown sizes are determined by minimizing an objective function that represents a trade-off between the quality loss due to modularization and the cost of reconfiguration while satisfying the problem constraints. The problem is then formulated and solved as an integer nonlinear programming problem with binary variables. The proposed method is applied to the production of a modular drive system composed of a DC motor and a ball screw. The study is a first attempt toward developing a systematic methodology for manufacturing modular products in a reconfigurable manufacturing system.     

模糊需求和模糊能力约束的集约生产计划

通过对模糊需求量和模糊等式及模糊需求环境下生产-库存平衡方程两种等价的描述方法的基础上,建立了具有模糊需求量和模糊能力约束集约生产计划问题的最佳平衡模型(BAPP)、交互式集约计划模型(IAPP)和交互式求解方法.     

A heuristic procedure for production planning of modern manufacturing systems

The purpose of this paper is to present a new heuristic algorithm based on a feasible enumeration method, developed to solve the machine loading and product-mix decision problems for manufacturing systems based on group technology. It provides an efficient tool for machine load and product-mix analysis to optimally select parts to be manufactured in a limited amount of time available in a given production facility, by applying the group technology concept. A computational algorithm is developed, a sample numerical problem included, and computational results presented. It is shown that the algorithm herein proposed is very efficient from the computational view point. The heuristics imbedded in the feasible enumeration procedure is repreented by a priority rule which has been found to be independent of problem data and general for the class of problem analysed.