Industry 4.0 smart reconfigurable manufacturing machines

This paper provides a fundamental research review of Reconfigurable Manufacturing Systems (RMS), which uniquely explores the state-of-the-art in distributed and decentralized machine control and machine intelligence. The aim of this review is to draw objective answers to two proposed research questions, relating to: (1) reconfigurable design and industry adoption; and (2) enabling present and future state technology. Key areas reviewed include: (a) RMS – fundamentals, design rational, economic benefits, needs and challenges; (b) Machine Control – modern operational technology, vertical and horizontal system integration, advanced distributed and decentralized control; (c) Machine Intelligence – distributed and decentralized paradigms, technology landscape, smart machine modelling, simulation, and smart reconfigurable synergy. Uniquely, this paper establishes a vision for next-generation Industry 4.0 manufacturing machines, which will exhibit extraordinary Smart and Reconfigurable (SR*) capabilities.     

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.     

Simultaneous optimal selection of design and manufacturing tolerances with alternative manufacturing process selection

Tolerance specification is an important part of mechanical design. Design tolerances strongly influence the functional performance and manufacturing cost of a mechanical product. Tighter tolerances normally produce superior components, better performing mechanical systems and good assemblability with assured exchangeability at the assembly line. However, unnecessarily tight tolerances lead to excessive manufacturing costs for a given application. The balancing of performance and manufacturing cost through identification of optimal design tolerances is a major concern in modern design. Traditionally, design tolerances are specified based on the designer’s experience. Computer-aided (or software-based) tolerance synthesis and alternative manufacturing process selection programs allow a designer to verify the relations between all design tolerances to produce a consistent and feasible design. In this paper, a general new methodology using intelligent algorithms viz., Elitist Non-dominated Sorting Genetic Algorithm (NSGA-II) and Multi Objective Particle Swarm Optimization (MOPSO) for simultaneous optimal selection of design and manufacturing tolerances with alternative manufacturing process selection is presented. The problem has a multi-criterion character in which 3 objective functions, 3 constraints and 5 variables are considered. The average fitness factor method and normalized weighted objective functions method are separately used to select the best optimal solution from Pareto optimal fronts. Two multi-objective performance measures namely solution spread measure and ratio of non-dominated individuals are used to evaluate the strength of Pareto optimal fronts. Two more multi-objective performance measures namely optimiser overhead and algorithm effort are used to find the computational effort of NSGA-II and MOPSO algorithms. The Pareto optimal fronts and results obtained from various techniques are compared and analysed.     

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.     

RMS中的基于时间的设备选择方法研究

根据生产需求快速确定制造资源是可重组制造系统高效快速响应市场变化的关键功能之一。文中提出了以系统整体加工时间消耗最少为目标的时间虚负荷矩阵算法,这种算法通过矩阵进行运算,计算简单直观,在计算过程中同时实现设备优化选择和工序分配,这种算法还可应用于车间作业调度。     

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.     

并行遗传算法下的农业业务外包伙伴选择

供应链管理环境下的业务外包模式为企业建立和提高自身的竞争优势提供了一种新的途径。其中,合作伙伴的选择是业务外包成功与否的关键因素。分析了农业产品加工业业务外包合作伙伴选择问题,给出了伙伴选择的多目标模型,提出了用一种自适应并行遗传算法来解决伙伴选择的问题,在增强和保持种群多样性的同时,表现出了较好的搜索性能。     

A virtual prototyping system with reconfigurable actuators for multi-material layered manufacturing

Proliferation of layered manufacturing (LM) in various sectors has been calling for fabrication of large, complex products with more materials and efficiency. We address this issue by integrating reconfigurable manufacturing (RM) with LM. This paper first analyses the benefits of such integration, and then presents a virtual prototyping system with reconfigurable actuators (VPRA) that can increase the number of materials, speed, and build volume to improve the efficiency and flexibility of multi-material layered manufacturing (MMLM). The VPRA system offers a test bed for design, visualization, and validation of MMLM facilities and processes. It takes advantage of the convenient graphics platform of SolidWorks™ for constructing a virtual MMLM facility by selecting reconfigurable actuators from predefined templates. The characteristics, including the dimensions and relative spatial constraints, of the actuators can be conveniently configured to suit design requirements. The mechanism and the operation process of the resulting MMLM facility can then be simulated and validated through digital fabrication of complex objects. Case studies are presented to demonstrate some possible applications of the VPRA system. Overall, the VPRA system gives insights into the characteristics of a reconfigurable MMLM system, which can be subsequently materialized for physical fabrication of multi-material objects. This approach highlights a possible direction for development of MMLM technology.     

敏捷制造中制造资源选择问题的遗传算法

对敏捷制造系统重构中的制造资源选择问题进行了分析,建立了数学模型,提出了一种适合求解该问题的遗传算法。该算法与解决同类问题的已有算法相比,编码方案和遗传算子均比较简单。实验结果表明,遗传算法在解的质量、稳定性和收敛速度方面具有优良性能。     

云制造环境下基于可信评价的云服务选择

针对云制造环境下因存在大量功能相同或相似的制造云服务而导致用户很难获得合适云服务的问题,提出了一种基于可信评价的制造云服务选择方法。对问题进行了抽象,将可靠性、可用性、时效性、价格和诚信度纳入可信特征集,并考虑评价时间、评价者的诚信度对可信值的影响,采用加权平均的方法计算制造云服务的整体可信度;在此基础上,综合考虑制造云服务的功能、任务负载、当前状态和物理距离等因素,通过匹配功能、任务负载和价格,并结合可信评价值来指导云服务的选择。仿真结果表明,所提方法能够有效地识别云制造环境下的制造云服务实体,可提高交易活动的成功率,满足用户的功能需求和非功能需求。     

Trends and perspectives in flexible and reconfigurable manufacturing systems

To better understand future needs in manufacturing and their enabling technologies, a survey of experts in manufacturing has been conducted. The survey instrument (i.e., questionnaire) tries to assess the experience to date with the use of flexible manufacturing systems (FMS) and to examine the potential roles and enabling technologies for reconfigurable manufacturing systems (RMS). The results show that two-thirds of respondents stated that FMSs are not living up to their full potential, and well over half reported purchasing FMS with excess capacity (which was eventually used) and excess features (which in many cases were not eventually used). They identified a variety of problems associated with FMS, including training, reconfigurability, reliability and maintenance, software and communications, and initial cost. However, despite these issues, nearly 75% of respondent expressed their desire to purchase additional, or expand existing FMSs. The experts agreed that RMS (which can provide exactly the capacity and functionality needed, exactly when needed) is a desirable next step in the evolution of production systems. The key enabling technologies for RMS were identified as modular machines, open-architecture controls, high-speed machining, and methods, training and education for the operation of manufacturing systems.     

Near optimal process plan selection for multiple jobs in networked based manufacturing using multi-objective evolutionary algorithms

The networked manufacturing offers several advantages in current competitive atmosphere by way of reducing the manufacturing cycle time and maintenance of the production flexibility, thereby achieving several feasible process plans. In this paper, we have addressed a Multi Objective Problem (MOP) which covers-minimize the makespan and to maximize the machine utilization while generating the feasible process plans for multiple jobs in the context of network based manufacturing system. A new multi-objective based Territory Defining Evolutionary Algorithm (TDEA) to resolve the above computationally challenge problem have been developed. In particular, with two powerful Multi-Objective Evolutionary Algorithms (MOEAs), viz. Non-dominated Sorting Genetic Algorithm (NSGA-II) and Controlled Elitist-NSGA-II (CE-NSGA-II) the performance of the proposed TDEA has been compared. An illustrative example along with three complex scenarios is presented to demonstrate the feasibility of the approach. The proposed algorithm is validated and the results are analyzed and compared.     

A dynamic multi-objective approach for the reconfigurable multi-facility layout problem

The multi-facility layout problem involves the physical organization of departments inside several facilities, to allow flexible and efficient operations. This work studies the facility layout problem in a new perspective, considering a group of facilities, and two different concerns: the location of departments within a group of facilities, and the location of departments inside each facility itself. The problem is formulated as a Quadratic Programming Problem with multiple objectives and unequal areas, allowing layout reconfigurations in each planning period. The objectives of the model are: the minimization of costs (material handling inside facilities and between facilities, and re-layout); the maximization of adjacency between departments; and the minimization of the “unsuitability” of department positions and locations. This unsuitability measure is a new objective proposed in this work, to combine the characteristics of existing locations with the requirements of departments. The model was tested with data from the literature as well as with a problem inspired in a first tier supplier in the automotive industry. Preliminary results show that this work can be viewed as an innovative and promising integrated approach for tackling real, complex facility layout problems.     

A genetic algorithm based approach to optimal fixture configuration

In this paper the application of genetic algorithms (GAs) to the fixture configuration optimisation problem is presented. A general purpose fixturing verification system has been developed to check the validity of individual fixture configurations by analysing various contact types in the workpiece-fixture system. Based on the information provided by the verification system, a genetic algorithm based approach carries out the evaluation process to determine the most statically stable fixture configuration among a large number of candidates. The preliminary implementation is introduced to demonstrate the ability of GAs and two different coding schemes are tested to explain their influence on the performance of GAs.     

An optimal image watermarking approach based on a multi-objective genetic algorithm

In accordance with the multi-objective nature of image watermarking, an optimal image watermarking approach using a multi-objective genetic algorithm is presented in this paper. Both watermarking parameters and embedding positions are often important factors affecting the performance of watermarking systems. The proposed multi-objective watermarking method can automatically optimize system parameters, and a variable-length mechanism is specially designed to search the most suitable positions for embedding watermarks. The method can also remove the difficult issue of determining optimal watermarking parameters from previous watermarking algorithms. The proposed multi-objective watermarking method directly deals with the problem of optimizing watermarking under non-dominated meaning, thus it can effectively avoid the difficulty of determining the optimally weighted factor in existing single-objective watermarking schemes. In addition, a Pareto-optimal set generated by multi-objective optimization can provide flexibility in selecting the most suitable watermarking parameters according to practical requirements.     

An evolutionary algorithm approach to generate distinct sets of non-dominated solutions for wicked problems

Many engineering design problems must optimize multiple objectives. While many objectives are explicit and can be mathematically modeled, some goals are subjective and cannot be included in a mathematical model of the optimization problem. A set of alternative non-dominated fronts that represent multiple optima for problem solution can be identified to provide insight about the decision space and to provide options and alternatives for decision-making. This paper presents a new algorithm, the Multi-objective Niching Co-evolutionary Algorithm (MNCA) that identifies distinct sets of non-dominated solutions which are maximally different in their decision vectors and are located in the same non-inferior regions of a Pareto front. MNCA is demonstrated to identify a set of non-dominated fronts with maximum difference in decision vectors for a set of real-valued problems.     

基于启发式遗传算法的SVM模型自动选择

支撑矢量机(SVM)模型的自动选择是其实际应用的关键.常用的基于穷举搜索的留一法(LOO)很繁杂且效率很低.到目前为止,大多数的算法并不能有效地实现模型自动选择.本文利用实值编码的启发式遗传算法实现基于高斯核函数的SVM模型自动选择.在重点分析了SVM超参数对其性能的影响和两种SVM性能估计的基础上,确定了合适的遗传算法适应度函数.人造数据及实际数据的仿真结果表明了所提方法的可行性和高效性.     

An approach to partner selection in agile manufacturing

It is believed that agile manufacturing is the twenty-first century manufacturing enterprise strategy and should be realized by agile virtual enterprise (AVE) which is dynamically configured for the manufacturing of one or more products. Agile manufacturing creates a significant challenge for the partner selection in configuring an AVE. This paper presents a brief introduction to the task decomposition for the manufacturing of a product in multisite environment. A bidding process should be carried out for each task so as to get the information necessary for the configuration of AVE. To minimize the manufacturing cost, an integer programming (IP) formulation is presented for the partner selection problem. The IP formulation is then transformed into a graph-theoretical formulation by taking the advantage of the precedence relationship between the tasks. The graph obtained turns out to be a directed multipartite graph. Based on the graph-theoretical formulation, an efficient solution algorithm is proposed for the problem. It is shown that the algorithm is polynomial bounded. Therefore, it is applicable to large practical problems.     

A decision support system for material and manufacturing process selection

The material and manufacturing process selection problem is a multi-attribute decision-making problem. These decisions are made during the preliminary design stages in an environment characterized by imprecise and uncertain requirements, parameters, and relationships. Material and process selection decisions must occur before design for manufacturing can begin. This paper describes a prototype material and manufacturing process selection system called MAMPS that integrates a formal multi-attribute decision model with a relational database. The decision model enables the representation of the designer's preferences over the decision factors. A compatibility rating between the product profile requirements and the alternatives stored in the database for each decision criteria is generated using possibility theory. The vector of compatibility ratings are aggregated into a single rating of that alternative's compatibility. A ranked set of compatible material and manufacturing process alternatives is output by the system. This approach has advantages over existing systems that either do not have a decision module or are not integrated with a database.     

A multi-objective genetic algorithm approach to rule mining for affective product design

A novel multi-objective genetic algorithm (GA)-based rule-mining method for affective product design is proposed to discover a set of rules relating design attributes with customer evaluation based on survey data. The proposed method can generate approximate rules to consider the ambiguity of customer assessments. The generated rules can be used to determine the lower and upper limits of the affective effect of design patterns. For a rule-mining problem, the proposed multi-objective GA approach could simultaneously consider the accuracy, comprehensibility, and definability of approximate rules. In addition, the proposed approach can deal with categorical attributes and quantitative attributes, and determine the interval of quantitative attributes. Categorical and quantitative attributes in affective product design should be considered because they are commonly used to define the design profile of a product. In this paper, a two-stage rule-mining approach is proposed to generate rules with a simple chromosome design in the first stage of rule mining. In the second stage of rule mining, entire rule sets are refined to determine solutions considering rule interaction. A case study on mobile phones is used to demonstrate and validate the performance of the proposed rule-mining method. The method can discover rule sets with good support and coverage rates from the survey data.