A Review of Advances in Modeling of Conventional Machining Processes: From Merchant to the Present

This paper presents a review of recent advances in modeling and simulation of conventional metal machining processes, which continue to dominate a significant part of all machining processes, and in recent years, the need for predictive models for machining processes has grown in importance in the digital manufacturing age. Significant advances have been made in modeling the mechanics of cutting in conventional machining, driven by industrial need and enabled by rapid advances in computational power. The paper surveys the state-of-the-art in analytical and numerical modeling of conventional metal machining processes with a focus on their ability to predict useful performance attributes including chip geometry, forces, temperatures, tool wear, residual stress, and microstructure. Also included in the review is a discussion of the industrial use of modeling and simulation tools for conventional machining. Additionally, the practical applicability, implementation benefits, and methodological limitations of conventional machining process modeling have been examined. The paper concludes with a summary of future research directions in modeling and simulation of conventional metal machining processes.

Research on calculation of grinding surface roughness

In machining processes, grinding is often chosen as the final machining method. Grinding is often chosen as the final machining method. This process has many advantages such as high precision and low surface roughness. It depends on many parameters including grinding parameters, dressing parameters and lubrication conditions. In grinding, the surface roughness of a workpiece has a significant influence on quality of the part. This paper presents a study of the grinding surface roughness predictions of workpieces. Based on the previous studies, the study built a relationship between the abrasive grain tip radius and the Standard marking systems of the grinding wheel for conventional and superabrasive grinding wheels (diamond and CBN abrasive). Based on this, the grinding surface roughness was predicted. The proposed model was verified by comparing the predicted and experimental results. Appling the research results, the surface roughness when grinding three types of steel D3, A295M and SAE 420 with Al2O3 and CBN grinding wheels were predicted. The predicted surface roughness values were close to the experimental values, the average deviation between predictive results and experimental results is 15.11 % for the use of Al2O3 grinding wheels and 24.29 % for the case of using CBN grinding wheels. The results of the comparison between the predicted model and the experiment show that the method of surface roughness presented in this study can be used to predict surface roughness in each specific case. The proposed model was verified by comparing the predicted and measured results of surface hardness. This model can be used to predict the surface hardness when surface grinding

Three-Dimensional Synthesis of Manufacturing Tolerances Based on Analysis Using the Ascending Approach

The present paper develops a new approach for manufacturing tolerances synthesis to allow the distribution of these tolerances over the different phases concerned in machining processes using relationships written in the tolerance analysis phase that have been well developed in our previous works. The novelty of the proposed approach is that the treatment of non-conventional surfaces does not pose a particular problem, since the toleranced surface is discretized. Thus, it is possible to study the feasibility of a single critical requirement as an example. During the present approach, we only look for variables that influence the requirements and the others are noted F (Free). These variables can be perfectly identified on the machine, which can be applied for known and unknown machining fixtures; this can be the base for proposing a normalized ISO specification used in the different machining phases of a mechanical part. The synthesis of machining tolerances takes place in three steps: (1) Analysis of the relationship’s terms, which include the influence of three main defects; the deviation on the machined surface, defects in the machining set-up, and the influence of positioning dispersions; then (2) optimization of machining tolerance through a precise evaluation of these effects; and finally (3) the optimization of the precision of the workpiece fixture, which will give the dimensioning of the machining assembly for the tooling and will allow the machining assembly to be qualified. The approach used proved its efficiency in the end by presenting the optimal machining process drawing that explains the ordered phases needed to process the workpiece object of the case study.

Improving machining stability of AISI-4140 with magnetic field

Several methods have been developed in order to improve the traditional machining processes and machining outputs. In this study, the effect of the magnetic field on the turning process was investigated. AISI-4140 was machined with different cutting speeds and magnetic flux density magnitudes. The magnetic field was generated with neodymium magnets. Machining stability, surface roughness, and maximum cutting temperature were measured. Additionally, chip shapes were examined. The machining stability was determined by measuring the vibration amplitude and other vibrational parameters (natural frequency, stiffness, and damping coefficients). Conventional turning and magnetic assisted turning were performed under the same cutting parameters consecutively, and the results were compared. According to the results, it was observed that neodymium magnets attached to the cutting tool improve machining stability and damping properties. Surface roughness was decreased between 6%–10% in magnetic assisted turning. Furthermore, it has been observed that the maximum cutting temperatures have been increased between 10%–45% in the magnetic assisted machining. Besides, it can be said that magnets contribute to improving chip control by collecting the chips on them while machining AISI-4140 steel.

Parametric analysis of dry machining process using a novel integrated multi-attribute decision making approach

In most of the machining processes, the complexity arises in the selection of the right process parameters, which influence the machining process and output responses such as machinability and surface roughness. In such situations, it is important to estimate the inter-relationships among the output responses. One such method, Decision-Making Trial and Evaluation Laboratory (DEMATEL) is applied to study the inter-relationships of the output responses. Estimation of proper weights is also crucial where the output responses are conflicting in nature. In the current study, DEMATEL technique is used for estimating the inter-relationships for output responses in machining of EN 24 alloy under dry conditions. CRiteria Importance Through Inter-criteria Correlation (CRITIC) method is used to estimate the weights and finally the optimal selection of machining parameters is carried out using Techniques for Order Preference by Similarity to an Ideal Solution (TOPSIS) method. The model developed guides the decision maker in selection of precise weights, estimation of the inter relationships among the responses and selection of optimal process parameters.

Optimization of advanced manufacturing processes using socio inspired cohort intelligence algorithm

The demand of Advanced Machining Processes (AMP) is continuously increasing owing to the technological advancement. The problems based on AMP are complex in nature as it consisted of parameters which are interdependent. These problems also consisted of linear and nonlinear constraints. This makes the problem complex which may not be solved using traditional optimization techniques. The optimization of process parameters is indispensable to use AMP's at its aptness and to make it economical to use. This paper states the optimization of process parameters of Ultrasonic machining (USM) and Abrasive water jet machining (AWJM) processes to maximize the Material Removal Rate (MRR) using a socio inspired Cohort Intelligent (CI) algorithm. The constraints involved with these problems are handled using static penalty function approach. The solutions are compared with other contemporary techniques such as Particle Swarm Optimization (PSO), Artificial Bee Colony (ABC), Modified Harmony Search (HS_M) and Genetic Algorithm (GA).