Process simulation for 5-axis machining using generalized milling tool geometries

Multi-axis machining (especially 5-axis machining) is widely used in precision machining for automotive, aerospace and die-mold manufacturing. The goal in precision machining is to increase production while meeting high part quality needs which can be achieved through decision of appropriate process parameters considering machine tool constraints (such as power and torque), chatter-free operations and part quality. In order to predict and decide on optimal process parameters, simulation models are used. In the literature, individual tool geometries for multi-axis machining are examined in detailed with different modeling approaches to simulate cutting forces. In this study, a general numerical model for 5-axis machining is proposed covering all possible tool geometries. Tool envelope is extracted from CAD data, and helical flutes points are represented in cylindrical coordinates. Equal parallel slicing method is utilized to find cutter engagement boundaries (CEB) determining cutting region of the tool surface. for each axial level in the tool axis direction. For each level uncut chip thickness value is found and total forces are calculated by summing force values for each point along the cutting flutes. For arbitrary cases forces are simulated and obtained results are experimentally verified

Simulation of multi-axis machining processes using z-mapping technique

Parameter selection in machining operations is curial for product quality and high productivity. Process parameters such as feed, spindle speed and depth of cuts are often chosen by trial-error methods. Mathematical models can be employed to predict the mechanics and the dynamics of the process. In this study, Z-mapping technique is utilized to simulate the process step by step by updating the workpiece according the given tool path where the cutter engagement areas are also determined. Using the numerical generalized process model, whole process is simulated for any milling tool geometry including intricate profiling tools, serrated cutters and tools with variable edge geometries

Machining strategy development in 5-axis milling operations using process models

Increased productivity and part quality can be achieved by selecting machining strategies and conditions properly. At one extreme very high speed and feed rate with small depth of cut can be used for high productivity whereas deep cuts accompanied with slow speeds and feeds may also provide increased material removal rates in some cases. In this study, it is shown that process models are useful tools to simulate and compare alternative strategies for machining of a part. 5-axis milling of turbine engine compressors made out of titanium alloys is used as the case study where strategies such as flank milling (deep cuts), point milling (light cuts) and stripe milling (medium depths) are compared in terms of process time by considering chatter stability, surface finish and tool deflections

Exploiting Pretrained Biochemical Language Models for Targeted Drug Design

Motivation: The development of novel compounds targeting proteins of interest is one of the most important tasks in the pharmaceutical industry. Deep generative models have been applied to targeted molecular design and have shown promising results. Recently, target-specific molecule generation has been viewed as a translation between the protein language and the chemical language. However, such a model is limited by the availability of interacting protein-ligand pairs. On the other hand, large amounts of unlabeled protein sequences and chemical compounds are available and have been used to train language models that learn useful representations. In this study, we propose exploiting pretrained biochemical language models to initialize (i.e. warm start) targeted molecule generation models. We investigate two warm start strategies: (i) a one-stage strategy where the initialized model is trained on targeted molecule generation (ii) a two-stage strategy containing a pre-finetuning on molecular generation followed by target specific training. We also compare two decoding strategies to generate compounds: beam search and sampling. Results: The results show that the warm-started models perform better than a baseline model trained from scratch. The two proposed warm-start strategies achieve similar results to each other with respect to widely used metrics from benchmarks. However, docking evaluation of the generated compounds for a number of novel proteins suggests that the one-stage strategy generalizes better than the two-stage strategy. Additionally, we observe that beam search outperforms sampling in both docking evaluation and benchmark metrics for assessing compound quality. Availability and implementation: The source code is available at https://github.com/boun-tabi/biochemical-lms-for-drug-design and the materials are archived in Zenodo at https://doi.org/10.5281/zenodo.6832145Comment: 12 pages, to appear in Bioinformatic