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.     

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.     

Generation of alternative process plans in integrated manufacturing systems

The availability of alternative process plans is a key factor for integration of design, process planning, and scheduling functions. The availability of alternative process plans can speed up the process of incremental process plan generation which can help in providing real time cost feed back to the designer after each design modification. Also alternative process plans relax the constraints in the optimization of production schedules, which in turn results in more efficient use of production resources and better delivery schedules. This paper describes a methodology for generation of alternative process plans in the integrated manufacturing environment. The procedure consists of: selection of alternative machining processes, clustering and sequencing of machining processes, and generation of a process plan network. Each of these steps is explained in detail. The result of the procedure is the process plan network that provides all alternative process plans for the given part. Methods for the selection of an optimal process plan are also described. Computational complexity of the procedure is discussed and experimental results on several realistic examples are shown.     

An adaptive process planning approach of rapid prototyping and manufacturing

This paper presents an adaptive approach to improve the process planning of Rapid Prototyping/Manufacturing (RP/M) for complex product models such as biomedical models. Non-Uniform Rational B-Spline (NURBS)-based curves were introduced to represent the boundary contours of the sliced layers in RP/M to maintain the geometrical accuracy of the original models. A mixed tool-path generation algorithm was then developed to generate contour tool-paths along the boundary and offset curves of each sliced layer to preserve geometrical accuracy, and zigzag tool-paths for the internal area of the layer to simplify computing processes and speed up fabrication. In addition, based on the developed build time and geometrical accuracy analysis models, adaptive algorithms were designed to generate an adaptive speed of the RP/M nozzle/print head for the contour tool-paths to address the geometrical characteristics of each layer, and to identify the best slope degree of the zigzag tool-paths towards achieving the minimum build time. Five case studies of complex biomedical models were used to verify and demonstrate the improved performance of the approach in terms of processing effectiveness and geometrical accuracy.     

Towards a generic design method for reconfigurable manufacturing systems: Analysis and synthesis of current design methods and evaluation of supportive tools

In today’s global manufacturing environment, changes are inevitable and something that every manufacturer must respond to and take advantage of, whether it is in regards to technology changes, product changes, or changes in the manufacturing processes. The reconfigurable manufacturing system (RMS) meets this challenge through the ability to rapidly and efficiently change capacity and functionality, which is the reason why it has been widely labelled the manufacturing paradigm of the future. However, design of the RMS represents a significant challenge compared to the design of traditional manufacturing systems, as it should be designed for efficient production of multiple variants, as well as multiple product generations over its lifetime. Thus, critical decisions regarding the degree of scalability and convertibility of the system must be considered in the design phase, which affects the abilities to reconfigure the system in accordance with changes during its operating lifetime. However, in current research it is indicated that conventional manufacturing system design methods do not support the design of an RMS and that a systematic RMS design method is lacking, despite the fact that numerous contributions exist. Moreover, there is currently only limited evidence for the breakthrough of reconfigurability in industry. Therefore, the research presented in this paper aims at synthesizing current contributions into a generic method for RMS design. Initially, currently available design methods for RMS are reviewed, in terms of classifying and comparing their content, structure, and scope, which leads to a synthesis of the reviewed methods into a generic design method. In continuation of this, the paper includes a discussion of practical implications related to carrying out the design, including an identification of potential challenges and an assessment of which tools that can be applied to support the design. Conclusively, further areas for research are indicated, which provides valuable knowledge of how to develop and realize the benefits of reconfigurability in industry.     

Applying simulated annealing for designing cellular manufacturing systems using MDmTSP

The design of cellular manufacturing systems involves many structural and operational issues. One of the important design steps is the formation of part families and machine cells (cell formation). Despite a large number of papers on cell formation published worldwide, only a handful incorporates operation sequence in layout design (intra-cell move calculations). We propose a solution to solve the part-family and machine-cell formation problem considering the within-cell layout problem, simultaneously. In this paper, the cellular manufacturing system is formulated as a multiple departures single destination multiple travelling salesman problem (MDmTSP) and a solution methodology based on simulated annealing is proposed to solve the formulated model. Numerical examples show that the proposed method is efficient and effective in finding optimal solutions. The results also indicate that the proposed approach performs well compared to some well-known cell formation methods.     

A novel methodology to measure the responsiveness of RMTs in reconfigurable manufacturing system

Reconfigurable manufacturing systems are designed to deliver exact functionality and capacity that is needed, when it is needed. The reconfigurable machine tool (RMT) plays a pivotal role in the accomplishment of this objective through their built in modular structure consisting of basic and auxiliary modules along with the open architecture software.     

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.     

A distributed task planning system for computer-integrated manufacturing systems

A formal mathematical framework for a distributed task planning method suitable for computerintegrated manufacturing systems is proposed. All pertinent algorithms are derived. A detailed timing analysis associated with primitive actions and activities (complex tasks) execution is presented. A formal language is designed for event tracking and error specification. Based on the derived language, an error recovery mechanism (automaton) is proposed. A case study demonstrates the applicability of the presented method with and without error occurrences.Dr Kokinaki is currently Science and Engineering Research Centre, De Montfort University, UK.     

Production planning and worker training in dynamic manufacturing systems

Production planning is a vital activity in any manufacturing system, and naturally implies assigning the available resources to the required operations. This paper develops and analyzes a comprehensive mathematical model for dynamic manufacturing systems. The proposed model integrates production planning and worker training considering machine and worker time availability, operation sequence and multi-period planning horizon. The objective is to minimize machine maintenance and overhead, system reconfiguration, backorder and inventory holding, training and salary of worker costs. Computational results are presented to verify the proposed model.     

A framework for manufacturing system reconfiguration and optimisation utilising digital twins and modular artificial intelligence

Digital twins and artificial intelligence have shown promise for improving the robustness, responsiveness, and productivity of industrial systems. However, traditional digital twin approaches are often only employed to augment single, static systems to optimise a particular process. This article presents a paradigm for combining digital twins and modular artificial intelligence algorithms to dynamically reconfigure manufacturing systems, including the layout, process parameters, and operation times of numerous assets to allow system decision-making in response to changing customer or market needs. A knowledge graph has been used as the enabler for this system-level decision-making. A simulation environment has been constructed to replicate the manufacturing process, with the example here of an industrial robotic manufacturing cell. The simulation environment is connected to a data pipeline and an application programming interface to assist the integration of multiple artificial intelligence methods. These methods are used to improve system decision-making and optimise the configuration of a manufacturing system to maximise user-selectable key performance indicators. In contrast to previous research, this framework incorporates artificial intelligence for decision-making and production line optimisation to provide a framework that can be used for a wide variety of manufacturing applications. The framework has been applied and validated in a real use case, with the automatic reconfiguration resulting in a process time improvement of approximately 10%.     

Design and planning problems in flexible manufacturing systems: a critical review

This review paper describes the state-of-the-art research on flexible manufacturing systems (FMS) design and planning issues. The emphasis is on presenting research results coming out of the current FMS literature that help the FMS manager in setting up a highly efficient manufacturing system. In addition to that, it discusses relevant research contributions after 1986, that were not part of any of the previous survey papers on operations research models for FMSs. Also, applications of combinatorial optimization approaches to FMS planning problems are adequately exposed in the paper.     

Application of genetic algorithm to computer-aided process planning in distributed manufacturing environments

In a distributed manufacturing environment, factories possessing various machines and tools at different geographical locations are often combined to achieve the highest production efficiency. When jobs requiring several operations are received, feasible process plans are produced by those factories available. These process plans may vary due to different resource constraints. Therefore, obtaining an optimal or near-optimal process plan becomes important. This paper presents a genetic algorithm (GA), which, according to prescribed criteria such as minimizing processing time, could swiftly search for the optimal process plan for a single manufacturing system as well as distributed manufacturing systems. By applying the GA, the computer-aided process planning (CAPP) system can generate optimal or near-optimal process plans based on the criterion chosen. Case studies are included to demonstrate the feasibility and robustness of the approach. The main contribution of this work lies with the application of GA to CAPP in both a single and distributed manufacturing system. It is shown from the case study that the approach is comparative or better than the conventional single-factory CAPP.     

Decision-based process planning for wire and arc additively manufactured and machined parts

The conventional manufacturing of aircraft components is based on the machining from bulk material and the buy-to-fly ratio is high. This, in combination with the often low machinability of the materials in use, leads to high manufacturing costs. To reduce the production costs for these components, a process chain was developed, which consists of an additive manufacturing process and a machining process. To fully utilize the process chain’s capabilities, an integrated process planning approach is necessary. As a result, the work sequence can be optimized to achieve the economically most suitable sequence. In this paper, a method for a joint manufacturing cost calculation and subsequent decision-based cost minimization is proposed for the wire and arc additive manufacturing (WAAM) & milling process chain. Furthermore, the parameters’ influence on the results and the magnitude of their influence are determined. These results make it possible to design an economically optimal work sequence and to automate the process planning for this process chain.     

用混合遗传算法求解虚拟企业生产计划

针对虚拟企业生产计划的特点,以各成员企业承担的生产任务为对象,以快速响应市场为目标,建立了生产任务计划的数学模型,并基于该模型,提出一种基于遗传算法与模拟退火算法混合的求解算法,充分发挥了遗传算法良好的全局搜索能力和模拟退火算法有效避免陷入局部极小的优点．从而提高了算法的全局寻优能力．数值仿真计算表明了该算法的良好收敛性和有效性．     

A Petri net-based methodology to increase flexibility in service-oriented holonic manufacturing systems

Distributed control systems such as the holonic manufacturing systems and service-oriented architectures have demonstrated to provide higher levels of flexibility, notably in the planning and scheduling functionalities, if well exploited. In scheduling, the use of fixed process plans generated by traditional planning approaches, usually leads to unrealistic schedules due to the lack of considerations of the workshop status. IPPS approaches try to break the gap between these two functionalities in favor of providing flexible plans adapting to the shop floor's state. A key element in the creation of flexible process plans is the definition of a process model capable of representing alternatives solutions to the sequencing problem and therefore increasing the potential solution space. This paper presents a methodology to increase planning flexibility in service-oriented manufacturing systems (SOHMS). The methodology introduces a Petri net service-oriented process model (SOP model) capable of computing a product's deadlock free sequential space and adapts to the fractal character of holonic architectures. A set of modeling rules, with illustrations, is presented for the automatic generation of the Petri net, based on a set of precedence conditions. To explore the solution space represented by the SOP model a holonic interaction protocol is presented. Moreover, a set of behavioral strategies is proposed in order to cope with the effects of a possible combinatorial explosion. A study case applied workshop example is presented to illustrate the modeling process of SOP models, compute the sequential solution space and demonstrate how this notably increases the number of potentially goods feasible solutions.     

Capacity and flexibility planning of an economical manufacturing system

This paper presents a simple dynamic model for determining the capacity and the flexibility of a manufacturing system over a finite planning horizon. We consider a problem that arises from the development phase of an investment plan for an economical manufacturing system. The objective is to minimize the total cost associated with the capacity expansion, flexibility expansion and operation. This problem is formulated as an integer program. A Lagrangian heuristic is developed for determining a near optimal solution to this integer program. Finally, we show how to incorporate aggregate production planning into the model.Based on a presentation given at the ORSA/TIMS Miami meeting in 1986.     

A non-dominated sorting genetic algorithm based approach for optimal machines selection in reconfigurable manufacturing environment

This paper deals with a problem of reconfigurable manufacturing systems (RMSs) design based on products specifications and reconfigurable machines capabilities. A reconfigurable manufacturing environment includes machines, tools, system layout, etc. Moreover, the machine can be reconfigured to meet the changing needs in terms of capacity and functionality, which means that the same machine can be modified in order to perform different tasks depending on the offered axes of motion in each configuration and the availability of tools. This problem is related to the selection of candidate reconfigurable machines among an available set, which will be then used to carry out a certain product based on the product characteristics. The selection of the machines considers two main objectives respectively the minimization of the total cost (production cost, reconfiguration cost, tool changing cost and tool using cost) and the total completion time. An adapted version of the non- dominated sorting genetic algorithm (NSGA-II) is proposed to solve the problem. To demonstrate the effectiveness of the proposed approach on RMS design problem, a numerical example is presented and the obtained results are discussed with suggested future research.     

Applications of particle swarm optimisation in integrated process planning and scheduling

Integration of process planning and scheduling (IPPS) is an important research issue to achieve manufacturing planning optimisation. In both process planning and scheduling, vast search spaces and complex technical constraints are significant barriers to the effectiveness of the processes. In this paper, the IPPS problem has been developed as a combinatorial optimisation model, and a modern evolutionary algorithm, i.e., the particle swarm optimisation (PSO) algorithm, has been modified and applied to solve it effectively. Initial solutions are formed and encoded into particles of the PSO algorithm. The particles “fly” intelligently in the search space to achieve the best sequence according to the optimisation strategies of the PSO algorithm. Meanwhile, to explore the search space comprehensively and to avoid being trapped into local optima, several new operators have been developed to improve the particles’ movements to form a modified PSO algorithm. Case studies have been conducted to verify the performance and efficiency of the modified PSO algorithm. A comparison has been made between the result of the modified PSO algorithm and the previous results generated by the genetic algorithm (GA) and the simulated annealing (SA) algorithm, respectively, and the different characteristics of the three algorithms are indicated. Case studies show that the developed PSO can generate satisfactory results in both applications.     

A review of function blocks for process planning and control of manufacturing equipment

Manufacturing in a job-shop environment is often characterized by a large variety of products in small batch sizes, requiring real-time monitoring for dynamic distributed decision making, and adaptive control capabilities that are able to handle, in a responsive way, different kinds of uncertainty, such as changes in demand and variations in production capability and functionality. In many manufacturing systems, traditional methods, based on offline processing performed in advance, are used. These methods are not up to the standard of handling uncertainty, in the dynamically changing environment of these manufacturing systems. Using real-time manufacturing intelligence and information to perform at a maximum level, with a minimum of unscheduled downtime, would be a more effective approach to handling the negative performance impacts of uncertainty. The objective of our research is to develop methodologies for distributed, adaptive and dynamic process planning as well as machine monitoring and control for machining and assembly operations, using event-driven function blocks. The implementation of this technology is expected to increase productivity, as well as flexibility and responsiveness in a job-shop environment. This paper, in particular, presents the current status in this field and a comprehensive overview of our research work on function block-enabled process planning and execution control of manufacturing equipment.