An integrated job shop scheduling and assembly sequence planning approach for discrete manufacturing

This paper proposes an integrated job shop scheduling and assembly sequence planning (IJSSASP) approach for discrete manufacturing, enabling the part processing sequence and assembly sequence to be optimized simultaneously. The optimization objectives are to minimize the total production completion time and the total inventory time of parts during production. The interaction effects between the job shop schedule and the assembly sequence plan in discrete manufacturing are analyzed, and the mathematical models including the objective functions and the constraints are established for IJSSASP. Based on the above, a non-dominated sorting genetic algorithm-II (NSGA-Ⅱ) with a hybrid chromosome coding mechanism is applied to solve the IJSSASP problem. Through the case studies and comparison tests for different scale problems, the proposed IJSSASP approach is verified to be able to improve the production efficiency and save the manufacturing cost of the discrete manufacturing enterprise more effectively.     

An enhanced genetic algorithm for automated assembly planning

Automated assembly planning reduces manufacturing manpower requirements and helps simplify product assembly planning, by clearly defining input data, and input data format, needed to complete an assembly plan. In addition, automation provides the computational power needed to find optimal or near-optimal assembly plans, even for complex mechanical products. As a result, modern manufacturing systems use, to an ever greater extent, automated assembly planning rather than technician-scheduled assembly planning. Thus, many current research reports describe efforts to develop more efficient automated assembly planning algorithms. Genetic algorithms show particular promise for automated assembly planning. As a result, several recent research reports present assembly planners based upon traditional genetic algorithms. Although prior genetic assembly planners find improved assembly plans with some success, they also tend to converge prematurely at local-optimal solutions. Thus, we present an assembly planner, based upon an enhanced genetic algorithm, that demonstrates improved searching characteristics over an assembly planner based upon a traditional genetic algorithm. In particular, our planner finds optimal or near-optimal solutions more reliably and more quickly than an assembly planner that uses a traditional genetic algorithm.     

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.     

A connector-based hierarchical approach to assembly sequence planning for mechanical assemblies

This paper presents an approach to find feasible and practical plans for mechanical assemblies based on a connector-based structure (CBS) hierarchy. The basic idea of the approach is to construct plans for an assembly (i.e. the root node of the CBS hierarchy) by systematically merging plans for primitive structures in the CBS hierarchy, while the plans for primitive structures are built using one of three methods: by reusing the existing plans, by retrieving the stored plans, and by geometric reasoning. The input to the approach consists of the assembly solid model, the connector-based relational model (CBRM) graph, the spatial constraint graphs, and the selected base part for the assembly, all of which are assumed to be generated previously in an interactive and/or automated way. Then, a CBS hierarchy for the assembly is automatically derived from the input CBRM by firstly establishing its functional model, which is constructed from the CBRM by decomposing the assembly or resulted part sets with respect to their connectors. Based on the CBS hierarchy, a set of assembly precedence diagrams representing good plans for the assembly are generated by merging plans for primitive structures systematically in a bottom-up manner. It can be proved that the proposed approach is both correct and complete. To verify the validness and efficiency of the approach, a variety of assemblies including some complicated products from industry are tested in the experimental CBHAP planner.     

Non-nominal path planning for robust robotic assembly

In manufacturing and assembly processes it is important, in terms of time and money, to verify the feasibility of the operations at the design stage and at early production planning. To achieve that, verification in a virtual environment is often performed by using methods such as path planning and simulation of dimensional variation. Lately, these areas have gained interest both in industry and academia, however, they are almost always treated as separate activities, leading to unnecessary tight tolerances and on-line adjustments.     

基于Agent的混合流水车间动态调度系统

针对敏捷制造调度环境的不确定性、动态性以及混合流水车间（HFS）调度问题的特点,设计了一种基于多Agent的混合流水车间动态调度系统,系统由管理Agent、策略Agent、工件Agent和机器Agent构成。首先提出一种针对混合流水车间环境的插值排序（HIS）算法并集成于策略Agent中,该算法适用于静态调度和多种动态事件下的动态调度。然后,设计了各类Agent间的协调机制,在生产过程中所有Agent根据各自的行为逻辑独立工作并互相协调。在发生动态事件时,策略Agent调用HIS算法根据当前车间状态产生工件序列,随后各Agent根据生成的序列继续进行协调直到完成生产。最后进行了发生机器故障、订单插入情况下的重调度以及在线调度等动态调度的实例仿真,结果表明对于这些问题,HIS算法的求解效果均优于调度规则,特别是在故障重调度中,HIS算法重调度前后的Makespan一致度达97.6%,说明系统能够灵活和有效地处理混合流水车间动态调度问题。     

A systematic optimization approach for assembly sequence planning using Taguchi method, DOE, and BPNN

Research in assembly planning can be categorised into three types of approach: graph-based, knowledge-based and artificial intelligence approaches. The main drawbacks of the above approaches are as follows: the first is time-consuming; in the second approach it is difficult to find the optimal solution; and the third approach requires a high computing efficiency. To tackle these problems, this study develops a novel approach integrated with some graph-based heuristic working rules, robust back-propagation neural network (BPNN) engines via Taguchi method and design of experiment (DOE), and a knowledge-based engineering (KBE) system to assist the assembly engineers in promptly predicting a near-optimal assembly sequence. Three real-world examples are dedicated to evaluating the feasibility of the proposed model in terms of the differences in assembly sequences. The results show that the proposed model can efficiently generate BPNN engines, facilitate assembly sequence optimisation and allow the designers to recognise the contact relationships, assembly difficulties and assembly constraints of three-dimensional (3D) components in a virtual environment type.     

A new hybrid improvement heuristic approach to simple straight and U-type assembly line balancing problems

This paper presents a new hybrid improvement heuristic approach to simple straight and U-type assembly line balancing problems which is based on the idea of adaptive learning approach and simulated annealing. The proposed approach uses a weight parameter to perturb task priorities of a solution to obtain improved solutions. The weight parameters are then modified using a learning strategy. The maximization of line efficiency (i.e., the minimization of the number of stations) and the equalization of workloads among stations (i.e., the minimization of the smoothness index or the minimization of the variation of workloads) are considered as the performance criteria. In order to clarify the proposed solution methodology, a well known problem taken from literature is solved. A computational study is conducted by solving a large number of benchmark problems available in the literature to compare the performance of the proposed approach to the existing methods such as simulated annealing and genetic algorithms. Some test instances taken from literature are also solved by the proposed approach. The results of the computational study show that the proposed approach performs quite effectively. It also yields optimal solutions for all test problems within a short computational time.     

A dynamic priority-based approach to concurrent toolpath planning for multi-material layered manufacturing

This paper presents an approach to concurrent toolpath planning for multi-material layered manufacturing (MMLM) to improve the fabrication efficiency of relatively complex prototypes. The approach is based on decoupled motion planning for multiple moving objects, in which the toolpaths of a set of tools are independently planned and then coordinated to deposit materials concurrently. Relative tool positions are monitored and potential tool collisions detected at a predefined rate. When a potential collision between a pair of tools is detected, a dynamic priority scheme is applied to assign motion priorities of tools. The traverse speeds of tools along the x-axis are compared, and a higher priority is assigned to the tool at a higher traverse speed. A tool with a higher priority continues to deposit material along its original path, while the one with a lower priority gives way by pausing at a suitable point until the potential collision is eliminated. Moreover, the deposition speeds of tools can be adjusted to suit different material properties and fabrication requirements. The proposed approach has been incorporated in a multi-material virtual prototyping (MMVP) system. Digital fabrication of prototypes shows that it can substantially shorten the fabrication time of relatively complex multi-material objects. The approach can be adapted for process control of MMLM when appropriate hardware becomes available. It is expected to benefit various applications, such as advanced product manufacturing and biomedical fabrication.     

An overview of function block enabled adaptive process planning for machining

Small- and medium-sized enterprises (SMEs) in job-shop machining are experiencing more shop-floor uncertainties today than ever before, due to multi-tier outsourcing, customised product demands and shortened product lifecycle. In a fluctuating shop floor environment, a process plan generated in advance is often found unsuitable or unusable to the targeted resources, resulting both in wasted effort spent in early process planning and in productivity drop when idle machines have to wait for operations to be re-planned. Consequently, an adaptive process planning approach is in demand. Targeting shop-floor uncertainty, the objective of this research is to develop a novel adaptive process planning method that can generate process plans at runtime to unplanned changes. This paper, in particular, presents an overview of adaptive process planning research and a new methodology, including two-layer system architecture, generic supervisory planning, machine-specific operation planning, and adaptive setup planning. Particularly, function blocks are introduced as a core enabling technology to bridge the gap between computer systems and CNC systems for adaptive machining.     

An approach to determine geometric feasibility to assembly states by intersection matrices in assembly sequence planning

In this article, an approach is described for the development of a process for the determination of geometric feasibility whose binary vector representation corresponds to assembly states. An assembly consisting of four parts is considered as an example. First, contact matrices generate the assembly's connection graph. The developing connection graph was used to model the example assembly. In the assembly's connection graph, each node corresponds to a part in the assembly, and edges in the graph of connections correspond to connecting every pair of nodes. Moreover, in the connection graph, each connection corresponds to an element in the binary vector representation. In the development of the approach, intersection matrices are used to represent interference among assembling parts during the assembly operation. Intersection matrices are defined to along the Cartesian coordinate system's six main directions. The elements of intersection matrices are constituted to Boolean values. Each element of binary vector representations includes a connection between a pair of parts. First, ordered pairs of parts are established. Then, Cartesian products, which are produced from these established ordered pairs of parts, are applied to Boolean operators. Finally, geometric feasibility of these binary vector representations is determined. In this work, some assembly systems are sampled and examined. Among these examples, six assembly sequences for a four-part packing system; two assembly sequences for a five-part shaft bearing system; 373 assembly sequences for a seven-part clutch system and assembly states have been investigated.     

A repository for design, process planning and assembly

This paper provides an introduction to the Design, Planning and Assembly Repository available through the National Institute of Standards and Technology (NIST). The goal of the Repository is to provide a publically accessible collection of 2D and 3D CAD and solid models from industry problems. In this way, research and development efforts can obtain and share examples, focus on benchmarks, and identify areas of research need. The Repository is available through the World Wide Web at URL http://www.parts.nist.gov/parts.     

A hierarchical approach on assembly sequence planning and optimal sequences analyzing

Assembly sequence planning (ASP) is a critical technology that bridges product design and realization. Deriving and fulfilling of the assembly precedence relations (APRs) are the essential points in assembly sequences reasoning. In this paper, focusing on APRs reasoning, ASP, and optimizing, a hierarchical ASP approach is proposed and its key technologies are studied systematically. APR inferring and the optimal sequences searching algorithms are designed and realized in an integrated software prototype system. The system can find out the geometric APRs correctly and completely based on the assembly CAD model. Combined with the process APRs, the geometric and engineering feasible assembly sequences can be inferred out automatically. Furthermore, an algorithm is designed by which optimal assembly sequences can be calculated out from the immense geometric and engineering feasible assembly sequences. The case study demonstrates that the approach and its algorithms may provide significant assistance in finding the optimal ASP and improving product assembling.     

Tool selection-embedded optimal assembly planning in a dynamic manufacturing environment

This paper presents an approach for tool selection-embedded optimal assembly planning in dynamic manufacturing environments. It aims to embed assembly tools into the planning process of assembly sequences in a dynamic shop-floor. The experimental results demonstrate that the developed approach is efficient and practical for a high fidelity assembly sequence with alternatives of assembly-tool sets. The dynamic assembly planning can efficiently support product assembly by generating feasible assembly sequences. It provides an effective design-aiding tool to virtually deal with various what-if scenarios regarding product assembly. In particular, the Web-based application developed in this research can be incorporated into a high-performance design and manufacturing environment on the Web, forming a distributed, collaborative and globally networked tool for product assembly planning.     

A two-level advanced production planning and scheduling model for RFID-enabled ubiquitous manufacturing

Radio frequency identification (RFID) technology has been used in manufacturing industries to create a RFID-enabled ubiquitous environment, in where ultimate real-time advanced production planning and scheduling (APPS) will be achieved with the goal of collective intelligence. A particular focus has been placed upon using the vast amount of RFID production shop floor data to obtain more precise and reasonable estimates of APPS parameters such as the arrival of customer orders and standard operation times (SOTs). The resulting APPS model is based on hierarchical production decision-making principle to formulate planning and scheduling levels. A RFID-event driven mechanism is adopted to integrate these two levels for collective intelligence. A heuristic approach using a set of rules is utilized to solve the problem. The model is tested through four dimensions, including the impact of rule sequences on decisions, evaluation of released strategy to control the amount of production order from planning to scheduling, comparison with another model and practical operations, as well as model robustness. Two key findings are observed. First, release strategy based on the RFID-enabled real-time information is efficient and effective to reduce the total tardiness by 44.46% averagely. Second, it is observed that the model has the immune ability on disturbances like defects. However, as the increasing of the problem size, the model robustness against emergency orders becomes weak; while, the resistance to machine breakdown is strong oppositely. Findings and observations are summarized into a number of managerial implications for guiding associated end-users for purchasing collective intelligence in practice.     

Real-virtual components interaction for assembly simulation and planning

Assembly motion simulation is crucial during conceptual product design to identify potential assembly issues and enhance assembly efficiency. This paper presents a novel assembly simulation system incorporating real-virtual components interaction in an augmented-reality based environment. With this system, users can manipulate and assemble virtual components to real components during assembly simulation using natural gestures through an enhanced bare-hand interface. Accurate and realistic movements of virtual components can be obtained based on constraints analysis, determination of resultant forces from contacts with real components and manipulation from the user's hands (forces and torques applied, etc.) during assembly simulation. A prototype system has been developed, and a case study of an automobile clutch was conducted to validate the effectiveness and intuitiveness of the system.     

A hybrid-graph approach for automated setup planning in CAPP

In this paper, a systematic approach for automated setup planning in CAPP is introduced. The concept of “hybrid graph”, which can be transferred into directed graph by changing any two-way edge into one-way edge, is introduced. The specified graph theory is effectively used in setup planning. Tolerance relations are used as critical constraints for setup planning. Comprehensive principles are explored and summarized for setup planning. The hybrid-graph theory, accompanied by matrix theory, is used to aid computerizing these principles. An example is illustrated to demonstrate the algorithm.