Search results

Complicated workpiece, such as an engine block, has special rough locating datum features (i.e. six independent datum features) due to its complex structure. This locating datum error cannot be handled by current variation propagation model based on differential motion vectors. To extend variation prediction fields, this paper aims to solve the unaddressed variation sources to modify current model for multistage machining processes.

To overcome the limitation of current variation propagation model based on differential motion vectors caused by the unaddressed variation sources, this paper will extend the current model by handling the unaddressed datum-induced variation and its corresponding fixture variation.

The measurement results of the rear face with respect to the rough datum W and the pan face with respect to the hole Q by coordinate measuring machine (CMM) are −0.006 mm and 0.031 mm. The variation results for rear face and pan face predicted by the modified model are −0.009 mm and 0.025 mm, respectively. The discrepancy of model prediction and measurement is very small.

This paper modifies the variation propagation model based on differential motion vectors by solving the unaddressed variation sources, which can extend the variation prediction fields for some complicated workpiece and is useful in the future work for many fields, such as process monitoring, fault diagnosis, quality-assured setup planning and process-oriented tolerancing.

Modeling and analysis of dimensional variation propagation is a crucial support technology for variation reduction, product/process design evaluation and recognition of variation source. However, owing to the multi-deviation (i.e. part deviations and fixture deviations) and multi-interaction (i.e. part-to-part interaction, part-to-fixture interaction and station-to-station interaction) in assembly processes, it is difficult for designers to describe or understand the variation propagation (or accumulation) mechanism clearly. The purpose of this paper is to propose a variation propagation modeling and analysis (VPMA) method based on multiple constraints aiming at a single station.

Initially, part-to-part constraints (PPCs) and part-to-fixture constraints (PFCs) are applied for the multi-interaction of assembly, and multiple constraints graph (MCG) model is proposed for expressing PPCs, PFCs, parts, as well as the variation propagation relation among them. Then, locating points (LPs) are adopted for representing the deviations in constraints, and formulas for calculating the deviations of LPs are derived. On that basis, a linearized relation between LPs’ deviations and part’s locating deviations is derived. Finally, a wing box is presented to validate the proposed method, and the results indicate the methodology’s feasibility.

MCG is an effective tool for dimensional VPMA, which is shown as an example of this paper.

Functions of geometric constraints in dimensional variation propagation are revealed, and MCG is proposed to formulize dimensional variation propagation.

This study aims to quantify the ultimate tensile strength and the nominal strain at break (ɛ_f) of printed parts made from polylactic acid (PLA) with a Replicating Rapid prototyper (Rep-Rap) 3D printer, by varying three important process parameters: layer thickness, infill orientation and the number of shell perimeters. Little information is currently available about mechanical properties of parts printed using open-source, low-cost 3D printers.

A computer-aided design model of a tensile test specimen was created, conforming to the ASTM:D638. Experiments were designed, based on a central composite design. A set of 60 specimens, obtained from combinations of selected parameters, was printed on a Rep-Rap Prusa I3 in PLA. Testing was performed using a JJ Instruments – T5002-type tensile testing machine and the load was measured using a load cell of 1,100 N.

This study investigated the main impact of each process parameter on mechanical properties and the effects of interactions. The use of a response surface methodology allowed the proposition of an empirical model which connects process parameters and mechanical properties. Even though results showed a high variability, additional ideas on how to understand the impact of process parameters are suggested in this paper.

On the basis of experimental results, it is possible to obtain practical suggestions to set common process parameters in relation to mechanical properties. Experiments discussed in the present paper provide a variety of data and insight regarding the relationship among the main process parameters and the stiffness and strength of fused deposition modeling-printed parts made from PLA. In particular, this paper underlines the shortage in existing literature concerning the impact of process parameters on the elastic modulus and the strain to failure for the PLA. The experimental data produced show a good degree of compliance with analytical formulations and other data found in literature.

Fixture layout design is concerned with immobilization of the workpiece (engine mount bracket) during machining such that the workpiece elastic deformation is reduced. The fixture holds the workpiece through the positioning of fixturing elements that causes the workpiece elastic deformation, in turn, leads to the form and dimensional errors and increased machining cost. The fixture layout has the major impact on the machining accuracy and is the function of the fixturing position. The position of the fixturing elements, key aspects, needed to be optimized to reduce the workpiece elastic deformation. The purpose of this study is to evaluate the optimized fixture layout for the machining of the engine mount bracket.

In this research work, using the finite element method (FEM), a model is developed in the MATLAB for the fixture-workpiece system so that the workpiece elastic deformation is determined. The artificial neural network (ANN) is used to develop an empirical model. The results of deformation obtained for different fixture layouts from FEM are used to train the ANN and finally the empirical model is developed. The model capable of predicting the deformation is embedded to the evolutionary optimization techniques, capable of finding local and global optima, to optimize the fixture layouts and to find the robust one.

For efficient optimization of the fixture layout parameters to obtain the least possible deformation, ant colony algorithm (ACA) and artificial bee colony algorithm (ABCA) are used and the results of deformation obtained from both the optimization techniques are compared for the best results.

A MATLAB-based FEM technique is able to provide solutions when the repeated modeling and simulations required i.e. modeling of fixture layouts (500 layouts) for every variation in the parameters requires individual modeling and simulation for the output requirement in any FEM-based software’s (ANSYS, ABACUS). This difficulty is reduced in this research. So that the MATLAB-based FEM modeling, simulation and optimization is carried out to determine the solutions for the optimized fixture layout to reach least deformation.

Many a time the practicability of the machining/mechanical operations are difficult to perform costly and time-consuming when more number of experimentations are required. To sort out the difficulties the computer-based automated solution techniques are highly required. Such kind of research over this study is presented for the readers.

A MATLAB-based FEM modeling and simulation technique is used to obtain the fixture layout optimization. ANN-based empirical model is developed for the fixture layout deformation that creates a hypothesis for the fixture layout system. ACA and ABCA are used for optimizing the fixture layout parameters and are compared for the best algorithm suited for the fixture layout system.

The optimal control on reassembly (remanufacturing assembly) error is one of the key technologies to guarantee the assembly precision of remanufactured product. However, because of the uncertainty existing in remanufactured parts, it is difficult to control assembly error during reassembly process. Based on the state space model, this paper aims to propose the optimal control method on reassembly precision to solve this problem.

Initially, to ensure the assembly precision of a remanufactured car engine, this paper puts forward an optimal control method on assembly precision for a remanufactured car engine based on the state space model. This method takes assembly workstation operation and remanufactured part attribute as the input vector reassembly status as the state vector and assembly precision as the output vector. Then, the compensation function of reassembly workstation operation input vector is calculated to direct the optimization of the reassembly process. Finally, a case study of a certain remanufactured car engine crankshaft is constructed to verify the feasibility and effectiveness of the method proposed.

The optimal control method on reassembly precision is an effective technology in improving the quality of the remanufactured crankshaft. The average qualified rate of the remanufactured crankshaft increased from 83.05 to 90.97 per cent as shown in the case study.

The optimal control method on the reassembly precision based on the state space model is available to control the assembly precision, thus enhancing the core competitiveness of the remanufacturing enterprises.

The purpose of this paper is to propose a posture evaluation approach based on temperature compensation and three-dimensional (3-D) tolerance for the key points (KPs).

A large component 3-D compensation model of thermal deformation considering characteristics of the assembly object is developed. Then, the thermal deformation compensation model is used to modify the nominal coordinates for the KPs. By using a combination of relative deviations of KPs as the objective and 3-D tolerance as the constraints, an optimization model for posture evaluation is established.

Deviations of posture and KPs’ coordinates are obtained by solving the non-linear constrained optimization problem. The posture evaluation method is demonstrated in both a simulation case and practical implication of the aircraft components assembly system with the result that a good performance is obtained.

The proposed method has been used in several aircraft assembly projects in China, and gained a good effect.

This paper proposes a method for eliminating the affection of thermal deformation during posture evaluation process and improving the consistency and stability of posture evaluation results. The results of this research will help to systematically improve the manufacturing process and tolerance allocation efficiency in large aircraft assembly.

Warranty-based big data analysis has attracted a great deal of attention because of its key capabilities and role in improving product quality while minimizing costs. Information and details about particular parts (components) repair and replacement during the warranty term, usually stored in the after-sales service database, can be used to solve problems in a variety of sectors. Due to the small number of studies related to the complete analysis of parts failure patterns in the automotive industry in the literature, this paper focuses on discovering and assessing the impact of lesser-studied factors on the failure of auto parts in the warranty period from the after-sales data of an automotive manufacturer.

The interconnected method used in this study for analyzing failure patterns is formed by combining association rules (AR) mining and Bayesian networks (BNs).

This research utilized AR analysis to extract valuable information from warranty data, exploring the relationship between component failure, time and location. Additionally, BNs were employed to investigate other potential factors influencing component failure, which could not be identified using Association Rules alone. This approach provided a more comprehensive evaluation of the data and valuable insights for decision-making in relevant industries.

This study's findings are believed to be practical in achieving a better dissection and providing a comprehensive package that can be utilized to increase component quality and overcome cross-sectional solutions. The integration of these methods allowed for a wider exploration of potential factors influencing component failure, enhancing the validity and depth of the research findings.

Customers in business-to-business markets are sellers of goods and services on their own. Thus, business-to-business suppliers may exert an influence on their customers’ buying decisions when performing marketing activities toward the customers of the customers by employing the concept of “multistage marketing”. Multi-stage marketing involves all sales-related measures which are aimed at the subsequent market stages (“customers of the customer”) which follow one or several primary customers in order to influence the buying behavior of these primary customers. Although the positive impacts of such activities are known, business-to-business companies often exclude the customers further along in the downstream supply chain from their marketing plans. But in a business-to-business context, the demand is always derived from buying decisions made further down the supply chain. The primary customers buy products or services because they want to use them – directly or indirectly – for either the production or the sale of other goods and services. Hence, derived demand, which can be traced to the end-user's primary demand, can be seen as the basis of multistage marketing.

The most common form of multistage marketing is ingredient (co-)branding, which occurs when a marketer providing an ingredient or component to an OEM advertises the ingredient to the customer of the assembled product. In addition to ingredient branding, this chapter identifies several other forms of multistage marketing and examines the underlying dimensions and processes of the phenomenon. The design of a marketing strategy using the concept of multistage marketing and its preconditions are discussed on a theoretical basis and are illustrated through concrete examples. The chapter provides a number of best practice examples in order to elucidate the issues concerning multistage marketing and its application in a company's marketing strategy serving business-to-business markets.

Production is defined as the mission of creating wealth (economic goods and services) from a variety of resources (human and non‐ human) by adding values (intrinsic and extrinsic) through transformation (physical and conceptual) so as to derive utilities (form, place, time, economic, non‐economic). This mission is organised through a system. Basically, what a production system looks like is as Fig.1. It is basically the flow of various resources that defines the nature and characteristic of the production system.

Presents a mathematical model for determining Economic Production Quantity (EPQ) in a multistage flow‐shop production system for the case where the demand for items per unit time is deterministic and the planning horizon is finite. Solves an example problem to illustrate the model.