Mold design and forming process parameters optimization for passenger vehicle front wing plate

The main objective of the present article is to solve the problems of poor molding quality, large warpage, inadequate cooling effect and unsuitable selection of process parameters, in the injection molding process for passenger vehicle front-end plastic wing plate. The thickness and parting surface of the vehicle front-end fender were determined, the injection mold and its cooling system were designed. The relevant process parameters, affecting the product molding quality, were tested, according to orthogonal experimental approach, while their influence on the warpage was obtained, by analyzing the data. Finally, the BP neural network of warpage model was established and globally optimized using genetic algorithm. The optimal parameter combination of the injection molding process was derived as: melt temperature 236 °C, mold temperature 51 °C, cooling time 32 s, packing pressure 97 MPa and packing time 16 s.

     

Multi-objective optimization of forming parameters for tube hydroforming process based on the Taguchi method

Tube hydroforming is an attractive manufacturing technology which is now widely used in many industries, especially the automobile industry. The purpose of this study is to develop a method to analyze the effects of the forming parameters on the quality of part formability and determine the optimal combination of the forming parameters for the process. The effects of the forming parameters on the tube hydroforming process are studied by finite element analysis and the Taguchi method. The Taguchi method is applied to design an orthogonal experimental array, and the virtual experiments are analyzed by the use of the finite element method (FEM). The predicted results are then analyzed by the use of the Taguchi method from which the effect of each parameter on the hydroformed tube is given. In this work, a free bulging tube hydroforming process is employed to find the optimal forming parameters combination for the highest bulge ratio and the lowest thinning ratio. A multi-objective optimization approach is proposed by simultaneously maximizing the bulge ratio and minimizing the thinning ratio. The optimization problem is solved by using a goal attainment method. An example is given to illustrate the practicality of this approach and ease of use by the designers and process engineers.     

Correlation between surface topography and profile statistical parameters

A relation between surface and profile statistics has been obtained. The probability density functions of asperity heights and peak radii for a profile and a surface were considered. A spherical model of an individual asperity was used. The theoretical results were compared with experimental data obtained from surface modelling using a computer and with results obtained by statistical treatment of manufactured rough surfaces. The fit of the theoretical and experimental results was good.     

Improving industrial suitability of incremental sheet forming process

Although in the last years a large amount of research work has been spent on incremental sheet forming process, industrial applications are not spreading accordingly. This is due to process characteristics such as slowness and limited accuracy. In the paper, the authors investigated the suitability of incremental sheet forming at very high feed rates to strongly reduce processing time. What is more, a simple strategy to reduce the part inaccuracy was implemented. The investigation concerned a simple conical shape but the obtained results are quite general.     

Optimization of the structural and process parameters in the sheet metal forming process

The quality of the sheet metal forming product is determined by defects such as wrinkling, springback, etc. Optimization techniques can avoid such defects while the desired final shape is obtained. The design variables of the optimization process consist of the structural parameters and process parameters. The structural parameters are the initial blank shape, geometry, etc. and the process parameters are the blank holding force (BHF), the drawbead restraining force (DBRF), etc. In this paper, the two groups of parameters are separately optimized. The structural parameters are optimized by the equivalent static loads method for non linear static response structural optimization (ESLSO) and the process parameters are optimized by the response surface method (RSM). A couple of examples are solved by the iterative use of ESLSO and RSM, and the solutions are discussed.     

Crack avoidance in steel piston rings through the optimization of process and gas nitriding parameters

This paper describes research into adequately estimating the main variables of a thermochemical gas nitriding process of stainless steel parts for engine components. The paper lays out an experimental strategy for the nitriding process that optimizes a set of variables that have a bearing on the occurrence of nitriding cracks. The results demonstrate that several factors and interactions are relevant in the occurrence of nitriding cracks. The proposed strategy was found to be effective at achieving continuous improvement and stricter control.     

Effects of three parameters on forming force of the single point incremental forming process

This research has examined the effects of three parameter groups on the forming force of single point incremental forming (SPIF) process. The parameters under study include the material types (sheet aluminum, brass and copper), the forming angles (30°, 40° and 50°), and the tool revolution speeds (200, 400 and 600 rpm). The metal forming was carried out using a spherical edge tool which was pressed onto the metal surface to form work pieces of truncated pyramid shape. In the experiment, the forming forces were measured and analyzed to determine an optimal parameter combination, with regard to the material type, forming angle and revolution speed, for the SPIF process. The experimental results showed that all three parameter groups exerted varying influences over the forming force of the SPIF process. The findings indicated that the sheet brass exhibited the highest force value and that the smaller forming angle contributed to the greater forming force. In addition, the higher tool revolution speed resulted in the lower forming force.

     

Surrogate-based multi-point forming process optimization for dimpling and wrinkling reduction

Multi-point forming (MPF) has been gaining attention for use in flexible sheet metal forming, since it is conducive to the manufacture of various shapes. However, discrete punch elements may induce either dimples or wrinkles, resulting in defective products. To address these forming issues, this study aims to eliminate both dimpling and wrinkling by adjusting parameters such as the punch speed, punch pressure (cushion compressive strain ratio), and elastic cushion thickness through multi-objective optimization. Evaluation of dimpling and wrinkling under variation in these three MPF parameters benefits from ordinary Kriging for computational efficiency. Multi-objective optimization with a genetic algorithm is used to determine the Pareto fronts of the dimpling and wrinkling measures, and a technique for order preferences by similarity to ideal solution (TOPSIS) is performed for identifying the best candidate among the Pareto optima. Finally, a dimpling-and-wrinkling-free TOPSIS solution is numerically verified by comparison with results of a full model simulation and experimentally validated by its application to a manufactured product.     

On the improvement of material formability in SPIF operation through tool stirring action

Single-point incremental forming (SPIF) is a quite new sheet-forming process which offers the possibility to deform complex parts without dedicated dies using a single-point tool and a standard three-axis CNC machine. The process mechanics enables higher strains with respect to traditional sheet-forming processes, but particular attention must be given to the maximum forming angle. In this paper, a new approach is proposed to enhance the material formability through a localized sheet heating as a consequence of the friction work caused by elevated tool rotational speeds. AA1050-O, AA1050-H24, and AA6082-T6 were utilized, and the reached temperatures were recorded by thermocouples, fixed to the sheet using a metal structure. A significant increase in the material formability was observed for both materials, and new formability curves have been built at the varying of the utilized rotational speed.     

A statistical approach to the optimization of a laser-assisted micromachining process

The objective of this study is to optimize a laser-assisted micro-grooving process designed for micromachining of difficult-to-machine materials such as hard mold/die steels and ceramics. The process uses a relatively low power continuous wave laser beam focused directly in front of a micro-grooving tool to thermally soften the material thereby lowering the cutting forces and associated machine and tool deflections. However, the use of laser heating can produce a detrimental heat-affected zone (HAZ) in the workpiece surface layers. Consequently, the laser and micro-grooving parameters need to be optimized in order to achieve the desired thermal softening effect while minimizing the formation of a HAZ in the material. Although thermal and force models for the hybrid process have been developed for possible use in process optimization, they are computationally intensive and are not accurate enough to produce reliable results. We overcome these deficiencies using a statistical approach. First, easy-to-evaluate metamodels are developed to approximate the complex engineering models. Then, the metamodels are statistically adjusted using real data from the process to make more accurate predictions. The optimization is then carried out on this statistically adjusted metamodels. The optimization strategy is experimentally verified and shown to yield good results.     

Robust optimization of the energy efficiency of the cold roll forming process

This paper proposes a hybrid modeling methodology for the robust optimization of cold roll forming process parameters. Energy efficiency is considered with the utilization of both analytical and computational models. A robust design algorithm is developed for the calculation of the optimized energy efficiency indicator through an analytical model at a low computational cost. The calculated optimum energy efficient solution is validated by a finite elements model (FEM) under specific quality constraints: the mapping of longitudinal strains, along a roll forming direction and a cross-sectional distribution, major strains on FLD, profile thickness reduction, and cross-sectional dimensional error. A robust design optimization towards the energy efficiency of a U-channel profile is demonstrated, and the effect of process parameters on the energy efficiency indicator is calculated. The paper arrived at the conclusion that the factors with the dominant effect on energy efficiency are roll gap, roller radius, and bending angle concept, with 30.96, 24.77, and 23.62 % contribution, respectively. Verification of the quality constraints over FEM has proven the feasibility of the optimum solution.     

Pre-evaluation on surface profile in turning process based on cutting parameters

Traditional online or in-process surface profile (quality) evaluation (prediction) needs to integrate cutting parameters and several in-process factors (vibration, machine dynamics, tool wear, etc.) for high accuracy. However, it might result in high measuring cost and complexity, and moreover, the surface profile (quality) evaluation result can only be obtained after machining process. In this paper, an approach for surface profile pre-evaluation (prediction) in turning process using cutting parameters and radial basis function (RBF) neural networks is presented. The aim was to only use three cutting parameters to predict surface profile before machining process for a fast pre-evaluation on surface quality under different cutting parameters. The input parameters of RBF networks are cutting speed, depth of cut, and feed rate. The output parameters are FFT vector of surface profile as prediction (pre-evaluation) result. The RBF networks are trained with adaptive optimal training parameters related to cutting parameters and predict surface profile using the corresponding optimal network topology for each new cutting condition. It was found that a very good performance of surface profile prediction, in terms of agreement with experimental data, can be achieved before machining process with high accuracy, low cost, and high speed. Furthermore, a new group of training and testing data was also used to analyze the influence of tool wear on prediction accuracy.     

Concurrent optimization of machining process parameters and tolerance allocation

With the advent use of sophisticated and high-cost machines coupled with higher labor costs, concurrent optimization of machining process parameters and tolerance allocation plays a vital role in producing the parts economically. In this paper, an effort is made to concurrently optimize the manufacturing cost of piston and cylinder components by optimizing the operating parameters of the machining processes. Design of experiments (DoE) is adopted to investigate systematically the machining process parameters that influence product quality. In addition, tolerance plays a vital role in assembly of parts in manufacturing industries. For the selected piston and cylinder component, improvements efforts are made to reduce the total manufacturing cost of the components. By making use of central composite rotatable design method, a module of DoE, a mathematical model is developed for predicting the standard deviation of the tolerance achieved by grinding process. This mathematical model, which gives 93.3% accuracy, is used to calculate the quality loss cost. The intent of concurrent optimization problem is to minimize total manufacturing cost and quality loss function. Genetic algorithm is followed for optimizing the parameters. The results prove that there is a considerable reduction in manufacturing cost without violating the required tolerance, cutting force, and power.     

Improving robotic machining accuracy through experimental error investigation and modular compensation

Machining using industrial robots is currently limited to applications with low geometrical accuracies and soft materials. This paper analyzes the sources of errors in robotic machining and characterizes them in amplitude and frequency. Experiments under different conditions represent a typical set of industrial applications and allow a qualified evaluation. Based on this analysis, a modular approach is proposed to overcome these obstacles, applied both during program generation (offline) and execution (online). Predictive offline compensation of machining errors is achieved by means of an innovative programming system, based on kinematic and dynamic robot models. Real-time adaptive machining error compensation is also provided by sensing the real robot positions with an innovative tracking system and corrective feedback to both the robot and an additional high-dynamic compensation mechanism on piezo-actuator basis.     

Improving the machine accuracy through machine tool metrology and error correction

Improving both the positioning accuracy and contouring accuracy of a vertical machining centre has been studied by using a machine tool metrology and in-house error correction techniques. Contouring errors caused by the servo lag and friction of servomechanisms were measured by the circular test and then reduced by off-line parameter tuning of the CNC and servo-driver. The quasistatic thermal errors were predicted online using a neural network based model which was calibrated in advance via a quick set-up and multiple-error measurement system consisting of a spindle-mounted probe and artifacts. Positioning errors caused by both the static geometric errors and thermal effects were eliminated in real-time by a PC based software error compensation scheme integrated with the CNC controller through digital communication. An error reduction of 70% was achieved after error compensation and CNC tuning.