Hybrid systems for planning and optimization of virtual assembly

Virtual assembly is the simulation of parts assembly processes by computer, analysing, evaluating and optimizing the feasibilities and procedures of assembly. It can thus avoid the potential problems and risks from designing to assembling. In this way, we can achieve the global optimization of the products and timely respond to the needs of the market. This paper presents a modelling framework for virtual assembly paths design and optimization of two objects on the basis of a class of hybrid system, which is applicable in many manufacturing environments. We propose an elementary hybrid machine containing time-driven and event-driven dynamics. We describe in detail a method of assembly paths design. The objective of optimization is evaluated in terms of time in the transition dynamics so as to make the problem more tractable. An explicit algorithm for deriving optimal assembly policies is developed. The optimal results indicate the feasibility and efficacy of the model and control algorithms.     

Computer-aided set-up planning for machining centres configuration

One of the key issues in defining the optimal configuration of a machining centre is the problem of determining the minimal number of set-ups for the part types to be machined. This paper proposes a method to define near-optimal set-up plans for prismatic workpieces when multiple parts can be mounted on the same pallet. Set-ups are determined taking into account the accessibility of the machining directions of the workpiece and the technological constraints among the required operations. The technological constraints are divided into three types: constraints that force the operations to share the same set-up, precedence constraints that cannot be violated in the sequence of set-ups, and constraints that translate technological preferences and that might be sacrificed to optimize the set-up plan. The technological constraints are analysed with a graph-based approach. The method proposes a solution for three-, four- and five-axis machines. The set-up plan for three axes is the starting point to determine the solutions for four- and five-axis machines: the set-up plan for four and five axes results from the combination of set-ups of the three-axis machine. Alternative solutions with the minimal number of set-ups are determined. Each solution specifies the orientation of the workpiece on the pallet fixture in each set-up, the operations to be executed in each set-up and the precedence relations among set-ups. Starting from the results of the set-up planning, the configuration of the pallet can be defined and taking into account the pallet configuration, the optimal machining centre for specific manufacturing needs is selected.     

Multiperiod production planning carrying over set-up time

Set-ups eat production capacity time and continue troubling production planning, especially on bottlenecks. The shortening of production planning periods to days, shifts or even less has increased the relative length of set-up times against the periods. Yet, many production planning models either ignore set-up times or, paradoxically, split longer multiperiod batches by adding set-ups at breaks between planning periods. The MILP-based capacitated lot-sizing models that include set-up carry-overs, i.e. allow a carry-over of a set-up of a product to the next period in case a product can be produced in subsequent periods, have incorporated fixed set-up fees without consideration of capacity consumed by set-up time. Inspired by production planning in process industries where set-up times still remain substantial, we incorporated set-up time with associated cost in two modifications of carry-over models. Comparison with an earlier benchmark model without set-up carry-over shows that substantial savings can be derived from the fundamentally different production plans enforced by carry-overs. Moreover, we show that heuristic inclusion of carry-overs by removal of set-ups from non-carry-over solutions is inefficient.     

Methodology for set-up planning automation of turned parts

This paper presents an automatic set-up planning module integrated in a CAPP system for rotational parts to be machined on a lathe. The developed system determines the possible set-up combinations that are necessary for a complete machining of the part as well as the order of each set-up and the surfaces to be used for clamping the part. The applied methodology takes into consideration constraints such as the geometry of both the stock and the final part, the geometry and the capacity of the chuck, and the part tolerances. In general, these constraints allow the system to obtain several valid solutions for clamping the part. Some criteria based on the clamping force and the value of tolerances have been considered in order to establish a preference order among these solutions. Finally, the analysis of linked tolerances and the tool approach direction to each surface determine the sets of surfaces to be machined within each set-up. An example part is used in the course of the paper to illustrate conveniently the methodology, and two additional case studies prove that this methodology is adequate for the solution of real cases.     

Graph-based set-up planning and tolerance decomposition for computer-aided fixture design

Set-up planning plays an important role in integrating design with process planning and fixture design. Its main task is to determine the number and sequence of set-ups, and select locating datum, machining features and operations in each setup. Computer-based set-up planning may be used to generate automatically a setup plan based on tolerance analysis for minimizing locating error, calculating machining error stack-up and improving CAD/CAPP/CAFD (Computer-aided Fixture Design) integration efficiency. Most of the current set-up planning systems were based on empirical methods (such as rule and knowledge base) and their applications background was plus/minus a dimensioning and tolerancing (GD&T) scheme. Today, more and more industries are using a true positioning Geometric Dimensioning and Tolerancing (GD&T) scheme in design and manufacturing. To support this requirement, this paper presents an analytical set-up planning approach with three techniques, (1) an extended graph to describe a Feature and Tolerance Relationship Graph (FTG) and a Datum and Machining Feature Relationship Graph (DMG), which could be transferred to an analytical computer model; (2) seven set-up planning principles to minimize machining error stack-up under a true positioning GD&T scheme; and (3) tolerance decomposition models to partition a tolerance into interoperable machining errors, which could be used for locating error analysis or for feedback to design stage for design improvement. These techniques are implemented in a computer system and it is integrated with a commercial CAD/CAM system to support an automobile manufacturing enterprise in fixture design and production planning.     

Automatic set-up planning for metal cutting: an integrated methodology

Automatic set-up planning has been regarded as a challenging task in computer-aided process planning (CAPP), which is a critical link in the integration of computer-aided design (CAD) and computer-aided manufacturing (CAM). Solving this problem is a top priority for CAD/CAM researchers and developers. Many researchers have worked on the problem and proposed different solutions based on geometry analysis, precedence constraint analysis, kinematic analysis, force analysis and tolerance analysis. However, these solutions generally use an ad hoc approach rather than a systematic approach. Consequently, the solutions can sometimes be impractical. An integrated methodology is presented to generate set-ups and select set-up datums and to determine set-up sequence. The methodology can deal successfully with a variety of components, thus providing a practical solution to the set-up planning problem that is crucial to the metal cutting industry.     

Integrated assembly and disassembly sequence planning using a GA approach

In a product life cycle, an assembly sequence is required to produce a new product at the start, whereas a disassembly sequence is needed at the end. In typical assembly and disassembly sequence planning approaches, the two are performed as two independent tasks. In this way, a good assembly sequence may contradict the cost considerations in the disassembly sequence, and vice versa. In this research, an integrated assembly and disassembly sequence planning model is presented. First, an assembly precedence graph (APG) and a disassembly precedence graph (DPG) are modelled. The two graphs are transformed into an assembly precedence matrix (APM) and a disassembly precedence matrix (DPM). Second, a two-loop genetic algorithm (GA) method is applied to generate and evaluate the solutions. The outer loop of the GA method performs assembly sequence planning. In the inner loop, the reverse order of the assembly sequence solution is used as the initial solution for disassembly sequence planning. A cost objective by integrating the assembly costs and disassembly costs is formulated as the fitness function. The test results show that the developed method using the GA approach is suitable and efficient for the integrated assembly and disassembly sequence planning. Example products are demonstrated and discussed.     

Layout optimization of trusses using improved GA methodologies

Summary The main concern of the paper is the simultaneous treatment of size, shape and topology variables in the optimum design of space trusses. As compared to only size optimization, this is a challenging, more difficult and complex problem. The paper discusses a solution algorithm which is based on the use of GAs. Two new methodologies, annealing perturbation and adaptive reduction of the design space, are introduced in conjunction with GAs, bringing additional increase in computational efficeiency. Some common problems in handling shape and topology design considerations are eliminated, which in turn provides a large and a flexible design environment. A numerical problem is presented to test the performance of the proposed methodologies and to compare the results with those existing in the literature. Furthermore, the paper studies a second problem designed to observe the efficiency of GAs in a considerably large and complex design space.     

Studying the impact of sequence-dependent set-up times in integrated process planning and scheduling with E-ACO heuristic

In job-shop scheduling, the importance of set-up issues is well known and has been considered in many solution approaches. However, in integrated process planning and scheduling (IPPS) involving flexible process plans, the set-up times are often ignored, or absorbed into processing times in IPPS domain, with the purpose to reduce the complexity. This is based on the assumption that set-up times are sequence-independent, or short enough to be ignored compared to processing times. However, it is not uncommon to encounter sequence-dependent set-up times (SDSTs) in practical production. This paper conducts a detailed investigation on the impact of SDSTs on the practical performance of the schedule: a comparative study is made for different cases where set-up times are (1) separately considered, (2) absorbed into processing times, or (3) totally ignored. An enhanced version of ant colony optimisation (E-ACO) algorithm is used to solve the IPPS problem, with the objective to minimise the total makespan. The following four types of set-up issues are considered: part loading/unloading, fixture preparation, tool switching and material transportation. Situations with various set-up time lengths have been studied and compared. A special case of IPPS problem involving a large number of identical jobs has been specifically studied and discussed. The results have shown that, set-up times should be carefully dealt with under different circumstances.     

Global optimization of a feature-based process sequence using GA and ANN techniques

Operation sequencing has been a key area of research and development for computer-aided process planning (CAPP). An optimal process sequence could largely increase the efficiency and decrease the cost of production. Genetic algorithms (GAs) are a technique for seeking to ‘breed’ good solutions to complex problems by survival of the fittest. Some attempts using GAs have been made on operation sequencing optimization, but few systems have intended to provide a globally optimized fitness function definition. In addition, most of the systems have a lack of adaptability or have an inability to learn. This paper presents an optimization strategy for process sequencing based on multi-objective fitness: minimum manufacturing cost, shortest manufacturing time and best satisfaction of manufacturing sequence rules. A hybrid approach is proposed to incorporate a genetic algorithm, neural network and analytical hierarchical process (AHP) for process sequencing. After a brief study of the current research, relevant issues of process planning are described. A globally optimized fitness function is then defined including the evaluation of manufacturing rules using AHP, calculation of cost and time and determination of relative weights using neural network techniques. The proposed GA-based process sequencing, the implementation and test results are discussed. Finally, conclusions and future work are summarized.     

Production planning optimization with physical programming

This paper proposes an optimization-based model for production planning, and physical programming as an effective method to optimize the production planning process within this model's framework. This model seeks to minimize cost and manufacturing time, while maximizing production rate. The physical programming method is shown to offer an effective setting to address the conflicting nature of these objectives. A numerical example is provided, which illustrates the flexibility of such optimization-based models, and of this paper's model in particular. The ready inclusion of realistic production constraints and goals brings practical significance to this approach.     

Repeat inspection planning using dynamic programming

In this paper we propose a dynamic programming approach to the problem of determination of the inspection sequence of multi-characteristic critical components, and the number of repeat inspection for each characteristic. The model presented here considers the case of several classification of a product by an inspector. An inspector could classify a product as non-defective, to be reworked, or to be scrapped, with respect to a certain characteristic. The model accounts as well for possible misclassification by the inspector. The dynamic programming algorithm searches for a solution that minimizes the total cost of inspection per accepted component. The total cost includes the cost of false rejection of good items, the cost due to false acceptance of an item which is either reworkable or to be scrapped, the cost of inspection, and the cost of rework.     

Observation planning in identifying dynamic processes

The observation plan concept [7] is considered as a generalization of the experiment-plan concept [8] for soluble problems in identifying processes with lumped parameters [1–3] and distributed ones [4–6].Translated from Inzhenerno-Fizicheskii Zhurnal, Vol. 49, No. 6, pp. 943–950, December, 1985.     

Adaptive and dynamic process planning using neural networks

Although feature-based process planning plays a vital role in automating and integrating design and manufacturing for efficient production, its off-line properties prevent the shop floor controller from rapidly coping with dynamic shop floor status such as unexpected production errors and rush orders. This paper proposes a conceptual framework of the adaptive and dynamic process planning system that can rapidly and dynamically generate the needed process plans based on shop floor status. In particular, the generic schemes for constructing dynamic planning models are suggested. The dynamic planning models are constructed as neural network forms, and then embedded into each process feature in the process plan. The shop floor controller will execute them to determine machine, cutting tools, cutting parameters, tool paths and NC codes just before the associated process feature is machined. The dynamic nature of process planning enables the shop floor controller to increase flexibility and efficiency in unexpected situations.     

Joint optimization of a flow-shop group scheduling with sequence dependent set-up times and skilled workforce assignment

Flow-shop sequence-dependent group scheduling (FSDGS) problem has been extensively investigated in the literature also due to many manufacturers who implemented the concept of group technology to reduce set-up costs, lead times, work-in-process inventory costs, and material handling costs. On the other hand, skilled workforce assignment (SWA) to machines of a given shop floor may represent a key issue for enhancing the performance of a manufacturing system. As the body of literature addressing the group scheduling problems ignored up to now the effect of human factor on the performance of serial manufacturing systems, the present paper moves in that direction. In particular, an M-machine flow-shop group scheduling problem with sequence-dependent set-up times integrated with the worker allocation issue has been studied with reference to the makespan minimization objective. First, a Mixed Integer Linear Programming model of the proposed problem is reported. Then, a well-known benchmark arisen from the literature is adopted to carry out an extensive comparison campaign among three properly developed metaheuristics based on a genetic algorithm framework. Once the best procedure among those tested is selected, it is compared with an effective optimization procedure recently proposed in the field of FSDGS problems, being this latter properly adapted to run the SWA issue. Finally, a further analysis dealing with the trade-off between manpower cost and makespan improvement is proposed.     

Linear physical programming for production planning optimization

Production planning plays a central role in the successful management of any production-oriented company. Production planning is typically multiobjective in nature and the management thereof generally consists of balancing/resolving many conflicting objectives. Previous works have shown that successful production planning can be achieved using multiobjective optimization. In this paper, a production-planning model conducive to optimization is developed and used with the preference-based optimization method Linear Physical Programming (LPP). Machine yield rates and production time are important components of the proposed model and examples that illustrate the optimization process. The key contribution of this work is in the application of LPP to a newly developed production planning model. The benefit of LPP is that it capitalizes on latent human experience and previous design knowledge when such is available. Otherwise, LPP effectively helps the designer explore the design space.     

Towards a seismic worst scenario approach for rocking systems: analytical and experimental set-up for dynamic response

The paper focuses on the problem of the assessment of the response of structures behaving like rocking rigid blocks with unilateral constraints under dynamic loading. Although the motion of rigid blocks simulated by means of ad hoc built calculus codes shows good agreement between the numerical results and the data recorded in the laboratory tests, the response of such a system is poorly robust, and results can be very different in dependence of a number of factors, thus making final assessment rather uncertain. The developments presented and discussed represent the premise pushing towards the adoption of approaches aimed at searching for the worst possible rocking response and eventually based on iterative optimisation procedures.     

Integrated dynamic single item lot-sizing and quality inspection planning

This paper proposes an integrated model for single item dynamic lot-sizing problem and Quality Inspection Planning (QIP). The objective is to provide a model of production planning that takes into account a targeted level of outgoing quality or an Acceptable quality level (AQL) when the manufacturing system inherently generates a proportion of defectives that increases significantly when the system switches from the in-control state to the out-of-control state. The average outgoing quality of each period of time of the planning horizon is bounded as a function of the inspection capacity. The effects of integrating QIP are analysed and discussed through several experiments representing different quality control system’s parameters, i.e. inspection capacity, inspection cost and AQL. The simulation results show that it is very important to take into account the inspection process into production planning decisions. This study will help the decision-makers to negotiate service levels or react properly to given customer quality requirements based on cost and lead time parameters in addition to their process characteristics in terms of capability and stability.     

Material and capacity requirements planning with dynamic lead times

Traditional material requirements planning (MRP) does not consider the finite capacity of machines, and assumes fixed lead times. This paper develops an approach (material and capacity requirements planning; MCRP) to integrate capacity planning into MRP. To obtain a capacity feasible production plan, different measures for capacity adjustment such as alternative routeings, safety stock, lot splitting and lot summarisation, are discussed. Additionally, lead times are no longer assumed to be fixed. They are calculated dynamically with respect to machine capacity utilisation. A detailed example is presented to illustrate how the MCRP approach works successfully.     

准双曲面齿轮箱响应分析及动力优化

综合考虑轮齿啮合变形、轴弯曲变形及轴承支撑刚度,建立准双曲面齿轮传动系统动力接触有限元分析模型,利用LS-DYNA仿真计算轴承的动态支反力;将轴承支反力作为箱体的动态激励,建立准双曲面齿轮箱动力分析模型,利用ANSYS进行动态响应分析,并与试验结果比较。以加速度响应均方根值最小为优化目标,箱体结构参数为设计变量,静态应力、位移及箱体体积为约束条件,建立准双曲面齿轮箱动态响应优化模型,用零阶方法求解,得到箱体最优设计参数。