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.     

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.     

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.     

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.     

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.     

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.     

A simulated annealing-based optimization algorithm for process planning

Computer-aided process planning (CAPP) in the past typically employed knowledge-based approaches, which are only capable of generating a feasible plan for a given part based on invariable machining resources. In the field of concurrent engineering, there is a great need for process planning optimization. This paper describes an approach that models the constraints of process planning problems in a concurrent manner. It is able to generate the entire solution space by considering multiple planning tasks, i.e. operations (machine, tool and tool approach direction), selection and operations sequencing simultaneously. Precedence relationships among all the operations required for a given part are used as the constraints for the solution space. The relationship between an actual sequence and the feasibility of applying an operation is also considered. An algorithm based on simulated annealing (SA) has been developed to search for the optimal solution. Several cost factors including machine cost, tool cost, machine change cost, tool change cost and set-up change cost can be used flexibly as the objective function. The case study shows that the algorithm can generate highly satisfying results.     

Two-stage simulation optimization for agile manufacturing capacity planning

Capacity planning involves the selection of manufacturing technologies and the allocation of budget to specific equipment acquisitions. In today's highly volatile manufacturing world, an agile capacity-planning tool is required. This tool must provide the mechanism for a company to thrive in an environment of uncertainty. Uncertain future demands make capacity planning and technology selection difficult tasks, whether they are caused by variations in forecasts of direct demand or by upstream variability in a supply chain. In this paper, a practical modelling technique for minimizing the required investment in capacity planning for discrete manufacturing sites under an uncertain demand stream is presented. The method consists of a two-stage stochastic integer program. The first stage characterizes the optimal response of the system under uncertainty. The second stage selects a tool set based on the characterization from the first stage, with the addition of budget constraints. The model is scalable, allowing for multiple products, multiple operations, multiple flow paths including re-entrant flow, and multiple tool types. A simple example is introduced to explain the methodology, followed by the results of a large-scale real-world application in the semiconductor industry.     

Hybrid method for parameter optimization with equality constraints

This article presents a hybrid method developed for solving an equality-constrained optimization problem. This method combines the co-evolutionary augmented Lagrangian method with the hybrid evolution strategy technique to overcome two major disadvantages of the evolutionary algorithm, i.e. poor constraint handling and a low convergence rate. Parameter and multiplier groups are evolved simultaneously to solve a zero-sum game transformed from a parameter optimization problem with equality constraints by the augmented Lagrangian method. Gradient individuals of parameters and multipliers are propagated by Newton’s method, and they play an important role in accelerating the speed of convergence. Ten test problems are solved to indicate that the proposed hybrid method supplies more accurate solutions with faster convergence than the co-evolutionary augmented Lagrangian method.     

Noise and dynamic range optimization of SAW transversal filters

A model for surface acoustic wave (SAW) transversal filters with special attention to the system and circuit designer's point of view is summarized. In the ideal situation the SAW transversal filter is driven with a voltage and the short-circuit current is sensed, which results in a minimization of the triple transit echo distortion. The aperture (width) of the SAW device is the only parameter that is not determined by the frequency dependence of the transfer and therefore it can be used to optimize the SAW device in relation to the electronic circuitry. Noise and dynamic range calculations on an amplifier-filter-amplifier configuration, are performed. It is shown that for a low noise floor at the input of the SAW device, the aperture of this device should be chosen large. However, the dynamic range of the amplifier-filter-amplifier configuration can be maximized by choosing a small aperture.     

Capacitated dynamic production and remanufacturing planning under demand and return uncertainty

This paper considers a stochastic dynamic multi-product capacitated lot sizing problem with remanufacturing. Finished goods come from two sources: a standard production resource using virgin material and a remanufacturing resource that processes recoverable returns. Both the period demands and the inflow of returns are random. For this integrated stochastic production and remanufacturing problem, we propose a nonlinear model formulation that is approximated by sample averages and a piecewise linear approximation model. In the first approach, the expected values of random variables are replaced by sample averages. The idea of the piecewise linear approximation model is to replace the nonlinear functions with piecewise linear functions. The resulting mixed-integer linear programs are solved to create robust (re)manufacturing plans.     

Combining discrete and continuous optimization to solve kinodynamic motion planning problems

A new approach to find the fastest trajectory of a robot avoiding obstacles, is presented. This optimal trajectory is the solution of an optimal control problem with kinematic and dynamic constraints. The approach involves a direct method based on the time discretization of the control variable. We mainly focus on the computation of a good initial trajectory. Our method combines discrete and continuous optimization concepts. First, a graph search algorithm is used to determine a list of intermediate points. Then, an optimal control problem of small size is defined to find the fastest trajectory that passes through the vicinity of the intermediate points. The resulting solution is the initial trajectory. Our approach is applied to a single body mobile robot. The numerical results show the quality of the initial trajectory and its low computational cost.     

Hybrid genetic optimization for design of photonic crystal emitters

A unique hybrid-optimization technique is proposed, based on genetic algorithms (GA) and gradient descent (GD) methods, for the smart design of photonic crystal (PhC) emitters. The photonic simulation is described and the granularity of photonic crystal dimensions is considered. An innovative sliding-window method for performing local heuristic search is demonstrated. Finally, the application of the proposed method on two case studies for the design of a multi-pixel photonic crystal emitter and the design of thermal emitter in thermal photovoltaic is demonstrated. Discussion in the report includes the ability of the optimal PhC structures designed using the proposed method, to produce unprecedented high emission efficiencies of 54.5% in a significantly long wavelength region and 84.9% at significantly short wavelength region.