Chip evacuation is a critical issue in metal cutting, especially continuous chips that are generated during the machining of ductile materials. The improper evacuation of these kinds of chips can cause scratching of the machined surface of the workpiece and worsen the resultant surface quality. This scenario can be avoided by using a properly designed chip breaker. Despite their relevance, chip breakers are not in wide-spread use in polycrystalline diamond (PCD) cutting tools. This paper presents a systematic methodology to design chip breakers for PCD turning inserts through finite element modelling. The goal is to evacuate the formed chips from the cutting zone controllably and thus, maintain surface quality. Particularly, different scenarios of the chip formation process and chip curling/evacuation were simulated for different tool designs. Then, the chip breaker was produced by laser ablation. Finally, experimental validation tests were conducted to confirm the ability of this chip breaker to evacuate the chips effectively. The machining results revealed superior performance of the insert with chip breaker in terms of the ability to produce curly chips and high surface quality (Ra = 0.51–0.56 µm) when compared with the insert without chip breaker that produced continuous chips and higher surface roughness (Ra = 0.74–1.61 µm).

Zero-Defect Manufacturing (ZDM) is a promising strategy for reducing errors in industrial processes, aligned with Industry 4.0 and digitalization, aiming to carry out processes correctly the first time. ZDM relies on digital tools, notably Artificial Intelligence (AI), to predict and prevent issues at both product and process levels. This study’s goal is to significantly reduce errors in machining large parts. It utilizes data from process models and in situ monitoring for AI-driven predictions. AI algorithms anticipate part deformation based on manufacturing data. Mechanistic models simulate milling processes, calculating tool deflection from cutting forces and assessing geometric and dimensional errors. Process monitoring provides real-time data to the models during execution. The research focuses on a high-value component from the oil and gas industry, serving as a test piece to predict geometric errors in machining based on the deviation of cutting forces using AI techniques. Specifically, an AISI 1095 steel forged flange, intentionally misaligned to introduce error, undergoes multiple milling operations, including 3-axis roughing and 5-axis finishing, with 3D scans after each stage to monitor progress and deviations. The work concludes that Support Vector Machine algorithms provide accurate results for the estimation of geometric errors from the machining forces.

This paper reports the development of an original design of chip breaker in a metal-matrix polycrystalline diamond (MMPCD) insert brazed into a milling tool. The research entailed finite element (FE) design, laser simulation, laser fabrication, and machining tests. FE analysis was performed to evaluate the effectiveness of different designs of chip breaker, under specified conditions when milling aluminum alloy (Al A356). Then, the ablation performance of an MMPCD workpiece was characterized by ablating single trenches under different conditions. The profiles of the generated trenches were analyzed and fed into a simulation tool to examine the resultant thickness of ablated layers for different process conditions, and to predict the obtainable shape when ablating multilayers. Next, the geometry of the designated chip breaker was sliced into a number of layers to be ablated sequentially. Different ablation scenarios were experimentally investigated to identify the optimum processing conditions. The results showed that an ns laser utilized in a controllable manner successfully produced the necessary three-dimensional feature of an intricate chip breaker with high surface quality (Ra in the submicron range), tight dimensional accuracy (maximum dimensional error was less than 4%), and in an acceptable processing time (≈51 s). Finally, two different inserts brazed in milling tools, with and without the chip breaker, were tested in real milling trials. Superior performance of the insert with chip breaker was demonstrated by the curled chips formed and the significant reduction of obtained surface roughness compared to the surface produced by the insert without chip breaker.

Dimensional tolerances and surface finish are two fundamental elements that must be carefully controlled during the manufacturing of industrial parts. The study of signals obtained from the machining of parts is useful for the control and monitoring of the process. For this reason, correlating these signals and information on the surface quality of the machined part is essential for the early detection of breaches in the quality levels of the parts. Therefore, the objective of this work is to develop an intelligent strategy capable of estimating the artificial geometric error induced in the part during machining. In order train these strategies, the experimental signals of the process and the three-dimensional scanned geometry of the machined part are used as starting data. This development aims to reduce both defects and production time, increasing the efficiency, productive capacity and environmental sustainability of the industrial process.