Multi-Criteria Decision Approach to Design a Vacuum Infusion Process Layout Providing the Polymeric Composite Part Quality

The increasingly widespread use of vacuum assisted technologies in the manufacture of polymer-composite structures does not always provide the required product quality and repeatability. Deterioration of quality most often appears itself in the form of incomplete filling of the preform with resin as a result of the inner and outer dry spot formation, as well as due to premature gelation of the resin and blockage of the vacuum port. As experience shows, these undesirable phenomena are significantly dependent on the location of the resin and vacuum ports. This article presents a method for making a decision on the rational design of a process layout. It is based on early forecasting of its objectives in terms of quality and reliability when simulating its finite element model, on the correlation analysis of the preliminary and final quality assessments, as well as on the study of the cross-correlation of a group of early calculated sub-criteria. The effectiveness of the proposed method is demonstrated by the example of vacuum infusion of a 3D thin-walled structure of complex geometry.

Adaptive Stretch-Forming Process: A Computer Vision and Statistical Analysis Approach

An industrial process is defined through its quality of parts and their production costs. Labour-intensive operations must be applied to produce high-quality components with inexpensive resources. Recent development in dedicated software allows the industrial sector to rely on more and more autonomous solutions to obtain an optimum ratio between part quality and cost. The stretch forming process is an operation that has a high degree of difficulty, due to the process parameters and the spring-back effect of materials. Our approach to solving several of the shortcomings of this process was to develop a self-adaptive algorithm with computer vision capabilities that adapts to the process in real-time. This experimental study highlights the results obtained using this method, as well as a comparison to a classical method for the stretch-forming process (SFP). The results have noted that the stretch-forming algorithm improves the process, while adapting its decisions with each step.

Dislocation evolution during additive manufacturing of tungsten

Additive manufacturing (AM) frequently encounters part quality issues such as geometrical inaccuracy, cracking, warping, etc. This is associated with its unique thermal and mechanical cycling during AM, as well as the material properties. Although many efforts have been spent on this problem, the underlying dislocation evolution mechanism during AM is still largely unknown, despite its essential role in the deformation and cracking behavior during AM and the properties of as-fabricated parts. In this work, a coupling method of three-dimensional dislocation dynamics and finite element method is established to disclose the mechanisms and features of dislocations during AM. Tungsten (W) is chosen as the investigated material due to its wide application. The internal thermal activated nature of dislocation mobility in W is taken into account. The correlations between the combined thermal and mechanical cycles and dislocation evolutions are disclosed. The effect of adding alloying element Ta in W is discussed from the perspectives of tuning dislocation mobility and introducing nanoparticles, which helps to understand why higher dislocation density and fewer microcracks are observed when adding Ta. The current work sheds new light on the long-standing debating of dislocation origin and evolutions in the AM field.

Multi-Objective Optimization Under Uncertainty of Part Quality in Fused Filament Fabrication

This work presents a data-driven methodology for multi-objective optimization under uncertainty of process parameters in the fused filament fabrication (FFF) process. The proposed approach optimizes the process parameters with the objectives of minimizing the geometric inaccuracy and maximizing the filament bond quality of the manufactured part. First, experiments are conducted to collect data pertaining to the part quality. Then, Bayesian neural network (BNN) models are constructed to predict the geometric inaccuracy and bond quality as functions of the process parameters. The BNN model captures the model uncertainty caused by the lack of knowledge about model parameters (neuron weights) and the input variability due to the intrinsic randomness in the input parameters. Using the stochastic predictions from these models, different robustness-based design optimization formulations are investigated, wherein process parameters such as nozzle temperature, nozzle speed, and layer thickness are optimized under uncertainty for different multi-objective scenarios. Epistemic uncertainty in the prediction model and the aleatory uncertainty in the input are considered in the optimization. Finally, Pareto surfaces are constructed to estimate the trade-offs between the objectives. Both the BNN models and the effectiveness of the proposed optimization methodology are validated using actual manufacturing of the parts.

Evaluation of: MOSSA, MOALO, MOVO and MOGWO Algorithms in Green Machining for High Production Performance in Turning of X210Cr12 Steel

The current study investigates the Wet and MQL machining, when turning of X210Cr12 steel, using a multilayer-coated carbide insert (GC-4215) with various nose radius, the consideration of the tool geometry with different cooling modes allow as to assess the comportment of the machined steel against the cutting combinations. The response surface methodology (RSM) has been used for regression analysis and to evaluate the contribution of the cutting parameters on surface roughness, tangential force and cutting power using ANOVA analysis. The developed models have been used to predict the studied output factors according to the selected cutting parameters for wet and MQL machining. A comparative between the cooling techniques have been established to determine the most effective technique in terms of part quality, lubricant consumption and power consumption. Finally, four new optimization technics have been used for the process optimization using the MQL models for an environment-friendly machining.

On the Part Quality, Process Parameters and In-Die Pressures in Indirect Squeeze Casting

Squeeze casting is a process that can produce the highest quality castings. In the current study, the effect of the process settings and the in-die conditions on rejection rates is studied through a full-scale experimental study. Factors affecting the as-cast part quality were investigated in the current study from two different viewpoints. The first part of the study was to investigate the influence of the process settings on the part rejection rate, and the second was to understand the conditions in the die and the effects on the part rejection rate to understand better the reasons and sensitivity of the squeeze casting process.

Process fluid as a technological system element of vibro-impact parts processing

Vibro-impact processing refers to finishing processing methods, which largely determine the operational part properties. It is shown that the parameter optimization of the vibro-impact processing is a very promising area of improving the part quality and reducing their manufacturing cost.One of the most important technological system elements is the process fluid. As a result of the priory information analysis, factors that determine the parameters of the vibro-impact processing were identified.Because of theoretical studies, the process fluid parameters and flushing mode influence degree on the process parameters and treated part quality indicators was determined. The process fluid is presented as a set of unit volumes that form an elastically deformable matrix. The necessary list of initial data for determining the maximum allowable process fluid amount in the mass load volume has been identified.The dependence for determining the minimum required process fluid volume is presented. A complex parameter that characterizes the change in the load mass volume in one oscillations cycle, together with the working chamber oscillation frequency and the process fluid fluidity, which determine both the fluid flow process nature (turbulent) and the flow rate inside the load mass is proposed. Based on the complex parameter, the load mass flushing speed is determined. It is shown that at this stage, the numerical value of the parameter can only be determined experimentally. Its theoretical definition is a promising area of further research.