Fixture Layout Research Articles

Automated fixture configuration for rapid manufacturing planning

The wide adoption of agile manufacturing systems has necessitated the design and use of fixtures or work holding devices that have in-built flexibility to rapidly respond to part design changes. Despite the availability of reconfigurable fixtures, practical fixture configuration largely remains an experience driven manual activity to enable customization for varying workpiece geometry, and most automated solutions do not scale well to accommodate such variation. In this paper, we address the problem of rapidly synthesizing a realistic fixture that will guarantee stability and immobility of a specified polyhedral work-part. We propose that the problem of automated fixture layout may be approached in two distinct stages. First, we determine the spatial locations of clamping points on the work piece boundary using the principles of force and form closure, to ensure immobility of the fixtured part under external perturbation. In particular, we show that the candidate restraints mapped to the six dimensional vector space of wrenches (force–moment pairs) may be hashed in a straightforward manner to efficiently generate force closure configurations that restrain part movement against large external wrenches. When clamps are allowed to exert arbitrarily high reaction forces on the part, the spatial arrangement of the clamping locations ensures the part is in form closure. On generating force/form closure configurations, the chosen locations are matched against a user-specified library of reconfigurable clamps to synthesize a valid fixture layout comprising clamps that are accessible and collision free with each other and the part. Additionally, in the case of determining machining setups the clamps are chosen to avoid collisions with the moving cutting tool. We demonstrate fast algorithms to perform both location selection and fixture matching, and show several results that underscore the practical application of our solution in automated manufacturing process planning.

Recent developments on machining fixture layout design, analysis, and optimization using finite element method and evolutionary techniques

A review of the recent development of the machining fixture configuration/layout is presented in this paper. The literature review is mainly focused on the recently developed optimal fixture configuration under the dynamic conditions of the workpiece. In this review paper, the fixture design, fixture analysis, fixture synthesis, fixture layout design, optimization of fixture layout design, various optimization algorithms, and case studies of two- and three-dimensional workpiece geometries under dynamic conditions have been emphasized specially. The further scopes of the research are finally summarized.

Fixture performance improvement by an accelerated integral method of fixture layout and clamping force plan

Deformation under external and clamping forces is an important factor affecting fixture performance. To reduce the deformation of the workpiece–fixture system and improve the performance of the fixture, an optimisation method for the fixture layout and clamping force plan is constructed in this article. First, the workpiece–fixture is illustrated as an elastic–elastic contact model with friction in which the workpiece is modelled by the finite element model, while the fixel can be modelled by either the spring model or finite element model. To accelerate the computing speed of solving the contact problem, a matrix size reducing method for the finite element model stiffness matrix is proposed, utilising less computer memory. Based on the same idea, a clamping force optimisation method considering the friction effect is presented to achieve the optimal clamping force of a special fixture layout within very few finite element model computing processes. Then, based on these, an integral fixture layout and clamping force optimisation algorithm are built by genetic algorithm. At the end of this article, numerical examples are taken to prove the performance of the methods. The results show that the accelerating method yields sufficient performance, and the optimisation algorithms of both the clamping force plan and the fixture layout design achieve favourable convergence.

An integration of assembly planning by design into supply chain planning

A supply chain needs to consider the quality of a product as well as the quality of manufacturing process to satisfy customer requirements at efficient resources planning in terms of safety stock allocation and vendor–buyer coordination. The objective of this article is to use an axiomatic approach to make assembly planning by designing and integrating assembly into supply chain planning, particularly during supply chain reconfiguration. The effect of fixture layout planning, the accuracy of demand forecast, and the supplier capability of providing the required material quality are studied. An optimum supply chain network is configured by combining the product, assembly, and supply chain planning. Heuristic-based optimization is used to validate the proposed solution. The performance of the system is measured in terms of lead time variability, the number of backorders, and the level of safety stock. The results and analysis indicate that the axiomatic approach is capable of reducing the assembly variation and employing necessary fixture layout planning to deliver product intents. In addition, the reduction of assembly variation also reduces the safety stock, lead time variability, and backorders. Finally, management decision making is discussed among other concluding remarks.

Active fixturing: literature review and future research directions

Fixtures are used to fixate, position and support workpieces and represent a crucial tool in manufacturing. Their performance determines the result of the whole manufacturing process of a product. There is a vast amount of research done on automatic fixture layout synthesis and optimisation and fixture design verification. Most of this work considers fixture mechanics to be static and the fixture elements to be passive. However, a new generation of fixtures has emerged that has actuated fixture elements for active control of the part–fixture system during manufacturing operations to increase the end product quality. This paper analyses the latest studies in the field of active fixture design and its relationship with flexible and reconfigurable fixturing systems. First, a brief introduction is given on the importance of research of fixturing systems. Secondly, the basics of workholding and fixture design are visited, after which the state-of-the-art in active fixturing and related concepts is presented. Fourthly, part–fixture dynamics and design strategies which take these into account are discussed. Fifthly, the control strategies used in active fixturing systems are examined. Finally, some final conclusions and prospective future research directions are presented.

Preliminary Study on Fixture Layout Optimization Using Element Strain Energy

Preliminary Study on Fixture Layout Optimization Using Element Strain Energy

A novel approach to fixture layout optimization on maximizing dynamic machinability

In the aerospace industry, the reasonable layout of fixture can efficiently suppresses machining vibration of thin-walled aerospace structure during machining. Based on the analysis of typical structural components encountered in the aerospace industry, a general frame-structure workpiece with fixture constraints can be equivalent as Mindlin plates with simultaneous elastic edges and internal supports. On basis of the equivalent models, the powerful pb-2 Ritz method defined by the product of a two-dimensional polynomial and basic functions can be introduced to be taken as trial functions. Substituting displacement functions into energy functional and minimizing total energy by differentiation leads to eigenfrequency equations of the workpiece–fixture system. Consequently, a novel nonlinear programming problem based on the frequency sensitivity can be built to optimize the layout of fixture supports to maximize the fundamental nature frequency of the workpiece–fixture system. The feasibility of the proposed approach is validated by a machining case.

Machining fixture layout optimisation using genetic algorithm and artificial neural network

The purpose of this research work is to design an optimum fixture layout in order to reduce the maximum elastic deformation of the workpiece caused by the clamping and machining forces acting on the workpiece while machining. First a genetic algorithm (GA) based optimisation procedure to solve the fixture layout optimisation problem is briefly explained, and then combined GA and artificial neural network (ANN) based optimisation procedure for fixture layout design is explained. In the combined GA and ANN approach, the resulting fixture layouts generated by GA are given as input to ANN and the maximum workpiece deformation for each fixture layout is found out by using ANN. The optimal fixture layout is the one which shows the minimum deformation among others. The results that are obtained by using GA and the combination of GA and ANN are compared.

Key Technology Research on Rimming Fixture Design

The role the rimming fixture is to ensure the models and the front cover having no relative motion in rolling process, and quality of fixture layout can influence if the cover edge has quality defects such as undulated in rimming process. First, this paper analyzes composition of the rimming bottom die and positioning clamping mechanism in rimming fixture, and obtains stressed situation of front cover edge under the action of rimming and stamping by the roll head by using the result of finite element analysis to the rimming process of front cover, combining with the design requirement of processing datum graph, the production program of engine hood, production class system and actuating rate 、failure rate and production rhythm of production lines, establishes distribution points of rimming fixture and finally designs the rimming fixture of engine hood. The fixture is currently used in actual production with stable and reliable performance.

Fixture layout design based on two-stage method for sheet metal components

The optimal fixture scheme can effectively reduce the deformation of sheet metal parts in the normal direction of their main plane. The fixture layout design requires a great deal of finite element analysis, which limits efficiency and quality of fixture design. This article proposes a two-stage method to decrease the number of finite element analyses for fixture layout design. First, three locating points are generated by using particle swarm optimization based on the rigid model. Second, the other points are optimized by establishing a response surface modeling based on radial basis function. Finally, through the case of rear bracket plate, this work elaborates two-stage fixture locating scheme design process. The results show that the two-stage method has good simulation effect and higher prediction accuracy.

Multi-objective optimization design of a fixture layout considering locator displacement and force–deformation

The fixture determines the workpiece position in a machining process; therefore, an increasing amount of attention has been given to fixture layout design. While machining, the workpiece position is affected by two major sources: (a) the locator displacement and (b) the force–deformation of the workpiece–fixture system. In the beginning of this paper, a geometric model considering the shape of a locator is developed to analyze the location performance, followed by the presentation of a simplified solving method and a location layout performance index. Second, to complete the force–deformation analysis, a finite element method-based force–deformation model is built and accelerated by a new method with a lower computer memory cost. Based on these two models, multiple objects of fixture layout optimization problems are proposed, and a multi-objective genetic algorithm-based optimization method is constructed. Finally, testing examples are approved to examine the validity of the method represented in this paper. These methods can provide a more accurate prediction of the locating performance in more widely used cases, and they have faster calculating speeds with lower computer memory costs.

Prediction of workpiece dynamic motion using an optimized artificial neural network

A machining fixture is an element used to hold the workpiece in the desired position and orientation during machining. The overall machining error in a workpiece is a result of different sources of errors in a workpiece–fixture system. One among them is the motion of the workpiece under the action of cutting forces. Evaluation of this dynamic motion is essential for the determination of the overall machining error. Most commonly, the finite element method is employed to compute the workpiece dynamic motion. During optimization of fixture layout, a large number of layouts are generated and the workpiece dynamic motion must be computed for each of the layouts. In such cases, use of the finite element method is prohibitive because of the long computation time required. Also, the results of the finite element analysis are susceptible to different parameters used in the analysis. Hence, an alternate and efficient methodology is necessary to determine the workpiece displacement for a given fixture layout. This article proposes a method of using an artificial neural network for the prediction of workpiece dynamic motion. Different layouts are obtained using a modular fixture and actual machining is performed on the workpiece. For each layout, the workpiece dynamic motion is computed at select datum points and an artificial neural network is trained with these data. To achieve better prediction capability of the artificial neural network and minimize different forms of errors in training and generalization, critical parameters of the artificial neural network are optimized using a genetic algorithm. Then, this optimized network is employed to predict the workpiece dynamic motion for any arbitrary layout. Results show that the optimized artificial neural network is capable of predicting the workpiece dynamic motion with acceptable accuracy (maximum absolute relative error 9.71%). This method, hence, can serve as an economical means of computing the overall machining error during optimization of fixture layouts.

Optimization method of fixture layout for aeronautical thin‐walled structures with automated riveting

PurposeAn appropriate fixture layout can decrease the assembly variation of Aeronautical Thin‐Walled Structure (ATWS) substantially. The purpose of this paper is to develop a fixture layout method to minimize variation.Design/methodology/approachThe paper uses genetic algorithm and ants algorithm (GAAA) to optimize the fixture layout by first, analyzing the “N‐2‐1” positioning principle of ATWS riveting, and then developing a hierarchical fixture layout model to represent the base points and locating points of ATWS. Second, information of base points and locating points is coded as gene and chromosome, according to a special coding rule and the fixture layout model. The fitness is also defined by the assembly variation of key characteristic points (KCPs). Third, the genetic and ants manipulations are discussed individually, and the two parts are connected by threshold value of the probability for chromosome in the genetic manipulation.FindingsThe method can solve the fixture layout problem of ATWS with automated riveting efficiently, which is shown as an example in this paper.Practical implicationsThe assembly variation is decreased by using the method presented in this paper according to the variation comparison.Originality/valueThe hierarchical fixture layout model is proposed for the first time in this paper and base points and locating points are optimized successfully by the GAAA.

Fixture layout optimization for flexible aerospace parts based on self-reconfigurable swarm intelligent fixture system

Based on the development demand of a novel intelligent fixture system, a self-reconfigurable intelligent swarm fixture system is presented. This paper deals with the fixturing layout optimization of a flexible aerospace workpiece. A new fixturing principle, “N-2-1-1,” is put forward. The optimization procedure for fixture layout combined with genetic algorithm and finite element analysis is developed and verified by case study simulation.

Machining fixture layout optimisation under dynamic conditions based on evolutionary techniques

Optimisation of fixture layout is critical to reduce geometric and form error of the workpiece during the machining process. In this paper the optimal placement of fixture elements (locator and clamp locations) under dynamic conditions is investigated using evolutionary techniques. The application of the newly developed particle swarm optimisation (PSO) algorithm and widely used genetic algorithm (GA) is presented to minimise elastic deformation of the workpiece considering its dynamic response. To improve the performances of GA and PSO, an improved GA (IGA) obtained by basic GA (GA) with sharing and adaptive mutation and an improved PSO (IPSO) obtained by basic PSO (PSO) incorporated into adaptive mutation are developed. ANSYS parametric design language (APDL) of finite element analysis is employed to compute the objective function for a given fixture layout. Three layout optimisation cases derived from the high speed slot milling case are used to test the effectiveness of the GA, IGA, PSO and IPSO based approaches. The comparisons of computational results show that IPSO seems superior to GA, IGA and PSO approaches with respect to the trade-off between global optimisation capability and convergence speed for the presented type problems.

Design and optimization of machining fixture layout using ANN and DOE

In machining fixtures, minimizing workpiece deformation due to clamping and cutting forces is essential to maintain the machining accuracy. This can be achieved by selecting the optimal location of fixturing elements such as locators and clamps. Many researches in the past decades described more efficient algorithms for fixture layout optimization. In this paper, artificial neural networks (ANN)-based algorithm with design of experiments (DOE) is proposed to design an optimum fixture layout in order to reduce the maximum elastic deformation of the workpiece caused by the clamping and machining forces acting on the workpiece while machining. Finite element method (FEM) is used to find out the maximum deformation of the workpiece for various fixture layouts. ANN is used as an optimization tool to find the optimal location of the locators and clamps. To train the ANN, sufficient sets of input and output are fed to the ANN system. The input includes the position of the locators and clamps. The output includes the maximum deformation of the workpiece for the corresponding fixture layout under the machining condition. In the testing phase, the ANN results are compared with the FEM results. After the testing process, the trained ANN is used to predict the maximum deformation for the possible fixture layouts. DOE is introduced as another optimization tool to find the solution region for all design variables to minimum deformation of the work piece. The maximum deformations of all possible fixture layouts within the solution region are predicted by ANN. Finally, the layout which shows the minimum deformation is selected as optimal fixture layout.

Effect of system compliance and workpiece dynamics on machining error

PurposeSource errors in a workpiece fixture system include the compliance of the workpiece fixture system and workpiece dynamics. The purpose of this paper is to study the relative significance of these two. The findings would help to achieve computational economy in optimization of fixture layout and/or clamping forces.Design/methodology/approachDifferent layouts are generated with the help of a reconfigurable fixture set up and a slot is end milled on the workpiece. Using these data and the finite element software ANSYS, the machining error due to system compliance is computed. The machining error due to workpiece dynamics is obtained using a data acquisition system with the LabView software. These steps are repeated for different clamping forces and the relative contribution of these two sources to the overall machining error is studied.FindingsResults show that the system compliance is much smaller in magnitude compared to workpiece dynamics and hence does not contribute appreciably to the overall machining error. This leads to the conclusion that, for bulky and stiff parts, evaluation of the machining error due to compliance can be done away with.Originality/valueThe paper's originality lies in comparing the two sources of machining error using experimental work and finite element models. To the author's knowledge such a comparison has not been reported in the literature.

A novel approach to fixture design on suppressing machining vibration of flexible workpiece

The machining vibration of the flexible workpiece is a major factor that greatly affects the machining accuracy of the final part. Improper fixture layout is apt to generate the machining vibration, which will seriously affect the machining quality of the surface, especially for the flexible workpiece. This paper is concerned with suppressing the machining vibration of the flexible workpiece by designing appropriate fixture layout scheme. A dynamic model on the workpiece–fixture–cutter system is built, where the cutting force is used as the disturbance input, and the fixture element is used as the control input. On the basis of this model, an approach to fixture design is proposed and introduced, for the first time, to suppress the machining vibration of the flexible workpiece originating from the cutting forces. It is worth noting that the location, the applied forces and the number of fixture elements can be simultaneously optimized. The effectiveness of the proposed method is verified by a machining example.

Mathematical approach for optimal machining fixture layout and clamping forces

Machining fixtures are used to locate and constrain a workpiece during machining operation. An ideal fixture design exhibits minimal moment while machining. The purpose of this work is to design an optimum fixture layout in order to reduce the maximum elastic deformation of the workpiece by reducing the moment caused by the clamping and machining forces acting on the workpiece while machining. This can be achieved by selecting the optimal locations of fixturing elements such as locators and clamps. The objective equations are formed based on equilibrium conditions and are solved for minimum value of clamping force condition for various fixture layouts. The required equations are formulated based on the equilibrium conditions of forces, moments and stick/slip conditions. Then these equations are solved for several fixture layouts and the corresponding clamping force sets are found. Thus the clamping forces satisfying constraints, along with its respective layout are solved for deformation values using ANSYS software. Layout providing minimum deformation value is selected as the optimal one.

Machining Fixture Layout Design for Milling Operation Using FEA, ANN and RSM

Workpiece elastic deformation caused during machining influences the dimensional and form errors. The machining fixtures are used for positioning the workpiece accurately to reduce the dimensional and form errors during machining. The position of the fixture elements is a major concern in minimizing the errors caused during machining. The optimal positioning of clamps and locators can minimize the elastic deformation of the workpiece which in turn reduces the machining errors. The main focus of this research work is to predict the fixture layout to minimize the maximum elastic deformation of the workpiece during machining. The finite element method (FEM) has been employed to determine the workpiece elastic deformation. The position of the fixturing elements is predicted using both Artificial Neural Networks (ANN) and Response Surface Methodology (RSM). In this paper, a numerical example has been considered from the literature to compare the performance of ANN and RSM.