Influence of Cutting Regime Parameters on Determining the Main Cutting Resistance during Polypropylene Machining.

This study examines the impact of cutting regimes on determining cutting resistance in the processing of polypropylene (PP) using the CNC lathe EMCO F5. The rationale for this research stems from polypropylene's rarity among thermoplastics in possessing structural stability, allowing for its comparison to metals and practical application in products replacing metal parts. Leveraging its favorable mechanical properties, polypropylene finds utility in producing parts subject to dynamic loads, boasting high resistance to impact loads-particularly undesirable in machining. An advantageous characteristic of polypropylene is its affordability, rendering it an economical choice across numerous applications. Despite these merits, polypropylene's exploration in cutting processing remains limited, underscoring the novelty of this research endeavor. The main method for determining cutting resistance involves measuring electric current strength during processing. This direct measurement, facilitated by input cutting regime parameters, is recorded by the PLC controller, with the current value extracted from the machine tool's ammeter. The experimental approach entails varying cutting regime parameters-cutting speed (v), feed rate (s), and depth of cut (a)-across minimum and maximum values, recognized as pivotal factors influencing cutting force development and the attainment of the desired machined surface quality.

A Hybrid Grey Wolf Optimizer for Process Planning Optimization with Precedence Constraints.

Process planning optimization is a well-known NP-hard combinatorial problem extensively studied in the scientific community. Its main components include operation sequencing, selection of manufacturing resources and determination of appropriate setup plans. These problems require metaheuristic-based approaches in order to be effectively and efficiently solved. Therefore, to optimize the complex process planning problem, a novel hybrid grey wolf optimizer (HGWO) is proposed. The traditional grey wolf optimizer (GWO) is improved by employing genetic strategies such as selection, crossover and mutation which enhance global search abilities and convergence of the traditional GWO. Precedence relationships among machining operations are taken into account and precedence constraints are modeled using operation precedence graphs and adjacency matrices. Constraint handling heuristic procedure is adopted to move infeasible solutions to a feasible domain. Minimization of the total weighted machining cost of a process plan is adopted as the objective and three experimental studies that consider three different prismatic parts are conducted. Comparative analysis of the obtained cost values, as well as the convergence analysis, are performed and the HGWO approach demonstrated effectiveness and flexibility in finding optimal and near-optimal process plans. On the other side, comparative analysis of computational times and execution times of certain MATLAB functions showed that the HGWO have good time efficiency but limited since it requires more time compared to considered hybrid and traditional algorithms. Potential directions to improving efficiency and performances of the proposed approach are given in conclusions.