Cost-tolerance analysis model based on a neural networks method

The purpose of tolerance design in product components is to produce a product with the least manufacturing cost possible, while meeting all functional requirements of the product. The product designer and process planner must fully understand the process accuracy and manufacturing cost of all kinds of manufacturing process to perform a good process plan job. Usually, the cost-tolerance model is constructed by a linear or non-linear regression analysis based on the data of the cost-tolerance experiment and to derive the correlation curve between the two. Though these correlation curves can show the relationship between manufacturing cost and tolerance, a fitting error is inevitable. In particular, there is considerable discrepancy in terms of the non-experimental data. A cost-tolerance analysis model based on a neural networks method is proposed. The cost-tolerance experimental data are used to set the training sets to establish a cost-tolerance network. Three representation modes of the cost-tolerance relationship are presented. First, the cost-tolerance relationship is derived from the grid points setting by the required tolerance accuracy. Second, a reasonable manufacturing cost of an unknown cost-tolerance experimental pair can be derived by the simulation of a cost-tolerance network. Third, an inference model based on a network's output is proposed to express the scope of the cost variation of various tolerances by means of a cost band. Comparison is also made with the high-order polynomial power function and exponential function cost-tolerance curves adopted by Yeo et al . Analytical results prove that the application of the cost-tolerance analysis model based on neural networks yields better performance in controlling the average fitting error than all conventional fitting models. The representation model using a cost band can identify precisely the possible cost variation range and reduce the chances of error in the tolerance design and cost estimation. It can thus provide important references for tolerance designers and process planners.     

基于模糊神经网络的公差-成本模型研究

研究了面向制造环境的公差-成本模型。对制造环境中有关的加工因素进行分类及模糊化处理,构造了加工因素的成本模糊影响系数,并以此系数和零件公差作为输入,建立了基于模糊神经网络的公差-成本模型。此模型在表征加工成本、公差关系上的精度较高,更适应面向制造公差设计的要求。     

Design for additive manufacturing (DfAM) methodologies: a proposal to foster the design of microwave waveguide components

ABSTRACT

Additive manufacturing offers many advantages, especially in terms of creativity and design freedom. However, this emerging technology is disrupting the way design is carried out and creativity is often limited by the cognitive barriers installed through years of traditional manufacturing processes. Likewise, as this manufacturing process is relatively recent and quite unknown to designers, its specificities are not always considered during the design phase, which leads to manufactured parts happening to differ from CAD models in terms of sizing or surface quality. Consequently, microwave components nowadays manufactured layer-by-layer do not exhibit operational electromagnetic performances. In this way, it is necessary to guide designers throughout the development of a product by drawing their attention to the different steps they must consider in order to design an additive manufactured optimised part.     

A hybrid Hopfield network-genetic algorithm approach to optimal process plan selection

In the automated manufacturing environment, different sets of alternative process plans can normally be generated to manufacture each part. However, this entails considerable complexities in solving the process plan selection problem because each of these process plans demands specification of their individual and varying manufacturing costs and manufacturing resource requirements, such as machines, fixtures/jigs, and cutting tools. In this paper the problem of selecting exactly one representative from a set of alternative process plans for each part is formulated. The purpose is to minimize, for all the parts to be manufactured, the sum of both the costs of the selected process plans and the dissimilarities in their manufacturing resource requirements. The techniques of Hopfield neural network and genetic algorithm are introduced as possible approaches to solve such a problem. In particular, a hybrid Hopfield network-genetic algorithm approach is also proposed in this paper as an effective near-global optimization technique to provide a good quality solution to the process plan selection problem. The effectiveness of the proposed hybrid approach is illustrated by comparing its performance with that of some published approaches and other optimization techniques, by using several examples currently available in the literature, as well as a few randomly generated examples.     

Tolerancing techniques: the state-of-the-art

The intense global competition to produce quality products at a low cost has led many industrial nations to consider mechanical tolerances as a key factor to bring about cost saving as well as to remain competitive. In the last two decades, some work has been done in the area of tolerance techniques. In this paper a comprehensive summary of the state-of-the-art and the projection of future trends in tolerancing techniques is presented to describe improved techniques available today and to aid in guiding research for the future. This paper reviews the status of theory and practice of how manufacturing and assembly processes are characterized for relating tolerancing to process or production cost. The specification of tolerancing on the dimension of the manufactured part has a significant impact on the final production cost. Tight tolerances can result in excessive process cost, while loose tolerances may lead to increased waste and assembly problems. This paper systematically reviews the state-of-the-art by classifying more than 50 papers written so far into five categories. They are, the dimensional tolerances chain technique, geometrical modelling in tolerances, statistical and probabilistic methods in tolerancing, tolerances based on analysis and synthesis, tolerances based on cost-tolerance algorithms and design methods. Future areas of research and the unresolved issues have been presented that will serve as a springboard for researchers to investigate and produce solutions for the end of this century. The present problems and issues once resolved, will revolutionize the manufacturing industry as the year 2000 approaches!     

A new algorithm for minimizing surplus parts in selective assembly by using genetic algorithm

Precision assemblies are produced from low precision subcomponents by partitioning and assembling them randomly from their corresponding groups. Surplus part is one of the important issues, which reduces the implementation of selective assembly in real situations. A new algorithm is introduced in this present paper to reduce surplus parts almost to zero and it is achieved in two stages by using a genetic algorithm. For demonstrating the proposed algorithm, a gearbox shaft assembly is considered as an example problem in which the shaft and pulley are manufactured in wider tolerance and partitioned in three to nine bins. The surplus parts are divided into three bins equally and a best combination of groups is obtained for both cases. It is observed that nearly 995 assemblies are produced out of one thousand subcomponents with the manufacturing cost savings of 19.5% for T _max and 992 assemblies are produced with 13.5% saving in manufacturing cost for 0.9T _max.     

Modelling and analysis of selective assembly using Taguchi's loss function

An assembly is the integrative process of joining components to make a completed product. It brings together the upstream process of design, engineering and manufacturing processes. The functional performance of an assembled product and its manufacturing cost are directly affected by the individual component tolerances. But, the selective assembly method can achieve tight assembly tolerance through the components manufactured with wider tolerances. The components are segregated by the selective groups (bins) and mated according to a purposeful strategy rather than being at random, so that small clearances are obtained at the assembly level at lower manufacturing cost. In this paper, the effect of mean shift in the manufacturing of the mating components and the selection of number of groups for selective assembly are analysed. A new model is proposed based on their effect to obtain the minimum assembly clearance within the specification range. However, according to Taguchi's concept, manufacturing a product within the specification may not be sufficient. Rather, it must be manufactured to the target dimension. The concept of Taguchi's loss function is applied into the selective assembly method to evaluate the deviation from the mean. Subsequently, a genetic algorithm is used to obtain the best combination of selective groups with minimum clearance and least loss value within the clearance specification. The effect of the ratio between the mating part quality characteristic's dimensional distributions is also analysed in this paper.     

A new tolerance design method for a secondary rechargeable battery using design of experiments with mixture

Mixing errors in the manufacturing process of a mixture may cause a sizeable variation in the performance of the product, leading to the need for the tolerance design. Even though a variety of procedures have been proposed for the optimal tolerance design based on quality loss and manufacturing costs, there are no available tolerance design methods when mixing errors exist in the manufacturing process of a mixture. In this article, we propose a new tolerance design method for the case where mixing errors are involved in massive manufacturing process of a secondary rechargeable battery. Using an approximation method, we derive quality loss function, reflecting the effects of mixing errors on the product performances. Statistical design of mixture experiments is applied to build empirical models of performances as functions of component proportions in the corresponding quality loss function. A real‐life case study on the tolerance design of a secondary battery is provided for the illustration of the proposed method. The results show the efficiency of the proposed method in designing the tolerances to minimize the quality loss and manufacturing costs. Copyright © 2008 John Wiley & Sons, Ltd.     

A fuzzy approach to process plan selection

Process planning is the systematic determination of the detailed methods by which parts can be manufactured from raw material to finished product. In a real manufacturing environment, usually several different parts need to be manufactured in a single facility sharing constrained resources. The existence of alternative process plans for each part makes the selection of process plan a very important issue in manufacturing. The objectives in process plan selection might be imprecise and conflicting. In this paper, a fuzzy approach is used to deal quantitatively with the imprecision of the process plan selection problem. Each process plan is evaluated and its contribution to shopfloor performance is calculated using fuzzy set theory. A progressive refinement approach is used to first identify the set of process plans that maximize the contributions, and then consolidate the set to reduce the manufacturing resources needed.     

Automated manufacturability assessment of rotational parts by grinding

Automated manufacturability assessment of a given design is a key requirement in realizing complete integration of design (CAD) and manufacturing (CAM). The paper deals with a system developed for automated manufacturability assessment by machining processes. The purpose of this system is to assist designers in their effort to come up with manufacturable parts economising in terms of cost and time. Unlike most of the work done in the past, which concentrates on assessment by primary machining processes such as turning and milling, the present work deals with grinding operation. The present system uses geometric reasoning to extract manufacturing specific information from a part model. It uses a knowledge base consisting of grinding process knowledge and control rules, which are activated by the designer. It also has a provision to account for company-specific manufacturing resources to come up with meaningful assessment for part manufacturability. The use of the proposed system is demonstrated for the assessment of axisymmetric parts to be manufactured by cylindrical and internal grinding processes.     

Maximising manufacturing system efficiency for multi-characteristic linear assembly by using particle swarm optimisation in batch selective assembly

Quality of an assembly is mainly based on the quality of mating parts. Due to random variation in sources such as materials, machines, operators and measurements, even those mating parts manufactured by the same process vary in their dimensions. When mating parts are assembled linearly, the resulting variation will be the sum of the mating part tolerances. Many assemblies are not able to meet the assembly specification in the available assembly methods. This will decrease the manufacturing system efficiency. Batch selective assembly is helpful to keep the assembly requirement and also to increase the manufacturing system efficiency. In traditional selective assembly, the mating part population is partitioned to form selective groups, and the parts of corresponding selective groups are assembled interchangeably. After the invention of advanced dimension measuring devices and the computer, today batch selective assembly plays a vital role in the manufacturing system. In batch selective assembly, all dimensions of a batch of mating parts are measured and stored in a computer. Instead of forming selective groups, each and every part is assigned to its best matching part. In this work, a particle swarm optimisation based algorithm is proposed by applying the batch selective assembly methodology to a multi-characteristic assembly environment, to maximise the assembly efficiency and thereby maximising the manufacturing system efficiency. The proposed algorithm is tested with a set of experimental problem data sets and is found to outperform the traditional selective assembly and sequential assembly methods, in producing solutions with higher manufacturing system efficiency.     

Fuzzy-based analysis of process capability for assembly quality assessment in mechanical assemblies

Process capability indices are useful tools for evaluating the ability of a process to produce products that meet certain specifications. The assembly quality is dependent on the distribution of variations of assembly dimensions, which is in turn dependent on mating conditions in the mechanical assembly. Since it is often difficult to measure the assembly dimensions in the production stages, they are not considered as a direct inspection objective. Rather, the inspection and evaluation of quality is carried out by specifying whether the assembly requirements satisfy the specified limits. Therefore, we can basethe process capability indices on the assembly dimensions. In most real life cases, the observations are fuzzy. In this paper, a novel method based on fuzzy concepts for process capability analysis of assembly dimensions in mechanical assemblies is presented. According to this scheme, sample observations of manufactured variables are described as fuzzy numbers. The proposed method is able to estimate the ability of the manufacturing process in satisfying the assembly quality in the mechanical assemblies with asymmetric tolerances which have non-normal distributions. In this paper, a proper criterion based on the probability of fuzzy set to interpret the computed fuzzy results is proposed, so these results are converted to the interpretable results for making a decision to evaluate the assembly quality. Furthermore, a new fuzzy-based quantity factor for expressing the percent contributions of effective manufacturing variables on the assembly quality is presented. The application of the presented method is demonstrated through an example and its results are discussed.     

Virtual prototyping and manufacturing planning by using tri-dexel models and haptic force feedback

This paper presents a new method of using the tri-dexel volumetric models and a haptics force feedback for virtual prototyping and manufacturing planning. In the proposed method, the initial polyhedral surface model is converted to a tri-dexel volumetric model by using a depth-peeling dexelization algorithm. In the virtual prototyping process, the tri-dexel volumetric model is updated by the swept volume of a moving cutter via a haptic force feedback interface device. A collision detection algorithm is proposed for the virtual sculpting and the pencil-cut planning with real-time haptic force feedback to the users. Tool paths are generated for machining the virtual sculpted parts via the simulation and verification on a virtual CNC machine tool before they are actually machined. Computer implementation and practical examples are also presented in this paper. The proposed method enables the haptic-aided virtual prototyping and manufacturing planning of complex surface parts.     

Virtual prototyping and manufacturing planning by using tri-dexel models and haptic force feedback

This paper presents a new method of using the tri-dexel volumetric models and a haptics force feedback for virtual prototyping and manufacturing planning. In the proposed method, the initial polyhedral surface model is converted to a tri-dexel volumetric model by using a depth-peeling dexelization algorithm. In the virtual prototyping process, the tri-dexel volumetric model is updated by the swept volume of a moving cutter via a haptic force feedback interface device. A collision detection algorithm is proposed for the virtual sculpting and the pencil-cut planning with real-time haptic force feedback to the users. Tool paths are generated for machining the virtual sculpted parts via the simulation and verification on a virtual CNC machine tool before they are actually machined. Computer implementation and practical examples are also presented in this paper. The proposed method enables the haptic-aided virtual prototyping and manufacturing planning of complex surface parts.     

Optimization of process parameters in fused deposition modelling of thermoplastics: A review

Among the several techniques for additive manufacturing (AM), fused deposition modelling (FDM) is widely used. Fused deposition modelling process uses a thermoplastic material, which is melted and then extruded layer by layer through a nozzle, in order to create a three-dimensional object. As a result of the default setting of process parameters provided by the manufacturers, produced parts normally have a poor surface finish, low mechanical properties, low dimensional accuracy, and increased residual stresses compared to the parts produced using conventional manufacturing processes like molding (casting). Qualities of fused deposition modelled (FDMed) parts are generally affected by process parameters including the layer thickness, extrusion temperature, build orientation, printing speed, raster angle, infill density, raster width, nozzle diameter, and air gap. Increasing infill density, printing temperature, and decreasing print speed and layer thickness lead to increase mechanical strength and improve the surface finish of the printed parts. The optimal process parameters are preferred to achieve superior properties of the parts. This paper reviews the optimal fused deposition modelling process parameters on part qualities for making the stability of used deposition modelled parts for use. Various process parameters are identified in order to obtain desirable qualities in the manufactured parts. Areas for future research are proposed.     

Analysis of the surface roughness and dimensional accuracy capability of fused deposition modelling processes

Building up materials in layers poses significant challenges from the viewpoint of material science, heat transfer and applied mechanics. However, numerous aspects of the use of these technologies have yet to be studied. One of these aspects is the characterization of the surface roughness and dimensional precision obtainable in layered manufacturing processes. In this paper, a study of roughness parameters obtained through the use of these manufacturing processes was made. Prototype parts were manufactured using FDM techniques and an experimental analysis of the resulting roughness average ( R a ) and rms roughness ( R q ) obtained through the use of these manufacturing processes was carried out. Dimensional parameters were also studied in order to determine the capability of the Fused Deposition Modelling process for manufacturing parts.     

A stochastic model for the optimal batch size in multi-step operations with process and product variability

Virtually all manufacturing processes are subject to variability, an inherent characteristic of most production processes. No two parts can ever be exactly the same in terms of their dimensions. For machining processes such as drilling, milling, and lathing, overall variability is caused in part by machine tools, tooling, fixtures and workpiece material. Since variability, which can be accumulated from tolerance stacking, can result in defective parts the number of parts produced in a batch is limited. When there are too many parts in a batch, the likelihood of producing all acceptable parts in a batch decreases due to the increased tolerances. On the other hand, too small a batch size incurs an increase of manufacturing costs due to frequent setups and tool replacements, whereas the likelihood of acceptable parts increases. To address this challenge, we present a stochastic model for determining the optimal batch size where we consider part-to-part variation in terms of tool wear, which tends to be proportional to batch size. In this paper, a mathematical model is constructed based on the assumption that the process used for producing preceding parts affects the state of subsequent parts in a probabilistic manner.     

Studying the influence of initial powder characteristics on the properties of final parts manufactured by the selective laser melting technology

Selective laser melting (SLM) is an additive manufacturing process that enables direct manufacturing of 3D complex shape parts and internal architecture from powder materials. The SLM technology is characterised by high temperature gradients and solidification rates that have a significant effect on the microstructures and properties of final parts. The present paper aims at understanding the influence of the initial properties of various martensitic stainless steel powders on the final microstructures and mechanical properties of parts manufactured using the same optimised SLM process parameter settings. The results obtained show that for applied optimum process parameters, the thermal effects are the same for all martensitic powders used. Besides, the final microstructures and properties are different. The results clearly show the effect of the initial complex chemical composition of the martensitic precipitation hardening powder on the microstructures of final parts, and consequently, on their properties.     

Statistical tolerance synthesis using distribution function zones

Tolerance is one of the most important parameters in design and manufacturing. Tolerance synthesis has a significant impact on manufacturing cost and product quality. In the international standards community two approaches for statistical tolerancing of mechanical parts are being discussed: process capability indices and distribution function zone. The distribution function zone (DFZone) approach defines the acceptability of a population of parts by requiring that the distribution function of relevant values of the parts be bounded by a pair of specified distribution functions. In order to apply this approach to statistical tolerancing, one needs a method to decompose the assembly level tolerance specification to obtain tolerance parameters for each component in conjunction with a corresponding tolerance-cost model. This paper introduces an optimization-based statistical tolerance synthesis model based on the DFZone tolerance specifications. A new tolerance-cost model is proposed and the model is illustrated with an assembly example.     

Concurrent optimal allocation of design and process tolerances for mechanical assemblies with interrelated dimension chains

Concurrent tolerance allocation has been the focus of extensive research, yet very few researchers have considered how to concurrently allocate design and process tolerances for mechanical assemblies with interrelated dimension chains. To address this question, this paper presents a new tolerance allocation method that applies the concept of concurrent engineering. The proposed method allocates the required functional assembly tolerances to the design and process tolerances by formulating the tolerance allocation problem into a comprehensive model and solving the model using a non-linear programming software package. A multivariate quality loss function of interrelated critical dimensions is first derived, each component design tolerance is formulated as the function of its related process tolerances according to the given process planning, both manufacturing cost and quality loss are further expressed as functions of process tolerances. And then, the objective function of the model, which is to minimize the sum of manufacturing cost and expected quality loss, is established and the constraints are formulated based on the assembly requirements and process constraints. The purpose of the model is to balance manufacturing cost and quality loss so that concurrent optimal allocation of design and process tolerances is realized and quality improvement and product cost reduction is achieved. The proposed method is tested on a practical example.