Manufacturing data typically flows one way (CAD→CAPP→CAM→CNC), expanding as it approaches the shop floor through technologists’ know-how and parameter choices. Using the CAM-Connect module integrated with Mastercam, we automatically extract these CAM settings and export a structured process description (STEP/STEP-NC XML) for scalable analysis. This paper introduces a practical CAM-driven method to identify and quantify non-productive motions in 3-axis milling. We focus on vertical auxiliary motions: Z-axis approaches, withdrawals, retracts and safe-height traverses. Auxiliary distances and times are reconstructed by interpreting CAM option logic, including clearance modes and retract/feed-plane definitions. The method is applied to 242 industrial milling operations comprising 2912 toolpaths. After excluding special drilling cycles and extremely short operations, auxiliary motion averages 7.96% of total operation time. Moreover, 14.5% of operations exceed a 15% auxiliary-time share. While rapid and feed auxiliary distances are similar, feed-executed auxiliary segments generate about 96% of auxiliary-motion time. The findings provide actionable diagnostics tied to CAM decisions and support systematic auditing and improvement of machining processes.

The requirements for machining include higher precision and productivity. To achieve these, measures against thermal deformation of machine tools and forced cooling of cutting heat are necessary. In recent years, there is also strong demand for energy-saving, resource-saving and environmentally friendly manufacturing in response to the SDGs and carbon neutrality. Under these conditions, it is an extremely difficult task to determine the optimum machining conditions while taking into account the need for high precision and improved productivity. In drilling, too, it is desired that the optimum drilling conditions can be determined instantaneously, but the variety of workpieces and drills makes it difficult to find the optimum drilling conditions for them. In this study, the FEM simulation technique that can calculate drill tip temperatures with high accuracy for the inputs of workpiece and drill materials, hole diameter and hole depth instructions and machining conditions (drill speed, feed rate, step feeding and forced cooling specification) in drilling was developed and evaluated in the experiment. The results showed that the proposed FEM simulation technique could calculate the drill edge temperature with high accuracy and quickly, easily. Therefore, it was effective as a tool for searching for optimum drilling conditions.

This paper presents a comparative simulation study on springback and formability in square cup manufacturing using Conventional (CDD) and Hydromechanical Deep Drawing (HMDD). While CDD relies on mechanical force, HMDD employs controlled fluid pressure, offering advantages like reduced friction, suppressed wrinkling, and higher formability. Using Abaqus/CAE, 3D models simulated the entire forming and springback process for two specimens: C1 (CDD) and C2 (HMDD). Effectiveness was evaluated based on six criteria: the forming process, Von Mises stress, residual stress, equivalent plastic strain, thickness distribution, and springback magnitude along OX, OY, and OZ axes. Results show HMDD produces superior quality. Specimen C2 exhibits more uniform stress and strain distribution, better-controlled thinning, and crucially, a significantly minimized springback (especially OZ-axis displacement at the flange and bottom corners), reduced by up to 2.5 times. This confirms HMDD's effectiveness for manufacturing precise, complex box-shaped components. The study provides a valuable database and methodology for optimizing processes and controlling defects in automotive, aerospace, and machinery manufacturing.

This paper presents a comparative analysis of a linear proportional-integral controller and a nonlinear fuzzy logic controller for a bidirectional buck-boost converter in onboard chargers for vehicle-to-grid and grid-to-vehicle applications. The study examines the robustness of both control strategies under voltage variations and assesses their dynamic response to setpoint changes. Simulation results highlight the advantages and limitations of each controller in terms of stability, transient behavior, and adaptability to varying operating conditions. The findings provide insights into selecting the most effective control approach for enhancing the performance and reliability of bidirectional power conversion in electric vehicle charging systems.

In order to ensure the undisturbed operation of gear transmissions, avoid their unplanned downtimes and secondary damage to them, their condition needs to be monitored and their failures need to be diagnosed. Currently, there is no commercial and universal diagnostic system for predictive analysis of gear wear. There are also no limit values of wear indicators defining individual causes of wear. The article presents problems related to various mechanisms of gear wear. Different methods of monitoring the condition of the gear are presented. They are presented. Thermal methods, analysis of wear residues, and measurements and analysis of vibrations and acoustic emissions are described. The usefulness of these techniques for predictive maintenance of gear movement is indicated. It is proposed to extend gear diagnostics by using machine learning methods to detect their faults, and in particular critical states of gear wear.

The increasing demand for ultra-smooth surfaces and precise control over nanoscale microstructural features in modern manufacturing has driven the development of hybrid finishing technologies. In this study, a novel Ultrasonically Assisted Magnetic Abrasive Finishing (UAMAF) method is proposed, integrating conventional Magnetic Abrasive Finishing (MAF) with high-frequency ultrasonic vibration to enhance both material removal efficiency and surface quality. A key innovation of this study is the development of a Multi-Point Electromagnet system, composed of multiple independently energized poles arranged to generate localized, intensified magnetic fields. This configuration improves the control and distribution of magnetic abrasive particles during polishing. Numerical simulations of flat, grooved, and curved head geometries revealed that the curved design achieved the most uniform magnetic flux. Polishing experiments on SUS 304 stainless steel confirmed that optimized process parameters such as spindle speed and DC current enabled a surface roughness (Ra) reduction to 15 nm after 90 minutes. The synergistic effect between ultrasonic vibrations and the multi-point magnetic control significantly improved abrasive dispersion, reduced agglomeration, and intensified micro-cutting actions at the workpiece interface. This research highlights the effectiveness of the UAMAF technique enhanced by a multi-point electromagnet system, providing new insights into hybrid finishing mechanisms. The findings hold strong potential for industrial applications in fields demanding high-precision surface integrity, such as biomedical devices, aerospace components, and optical systems.

This paper presents an analysis of the cutting forces during the drilling of a ceramic fibre-reinforced aluminium matrix composite. The machining was carried out under dry drilling conditions and with minimum lubrication of the cutting zone. The mean values of the feed force and the ratio of this mean to the force amplitude were calculated. This result is the load stability coefficient was thus obtained. The measured force waveforms were decomposed using a discrete wavelet transform in Matlab. An approximation, which is a filtered force waveform, and a detail, i.e. the noise of the measurement, were obtained. The numerical value of noise and interference was calculated from the ratio of load stability after filtration to load stability before filtration. It was found that the use of oil mist lubrication slightly reduces the average value of the feed force during drilling of the tested composite. The interference affecting the force measurement when drilling with MQL is higher, while the type of Daubechies wavelet compactness does not affect the filtration power.

Time standards belong to vital indicators of the production process that facilitate making decisions related to product and process improvement. The presented issues concern the determination of the assembly time standard using kernel estimators. The development of neural networks offers the possibility to identify begin-end points in the assembly process that can provide big data related to the assembly time standard. The problem addressed in this paper is development of a method of big data analysis, on the basis of which the assembly time standard can be determined. In the presented approach adequate formulas are developed together with some examples. The paper presents an application of the theory of kernel estimators as well as results of the proposed approach.

In order to build a two-stage helical gearbox (THG) with first stage double gear-sets (FSDG), the multi-criteria decision-making (MCDM) method is introduced in this research as a new approach to solving the multi-objective optimization problem (MOOP). The study's objective is to deter-mine the best primary design factors that simultaneously satisfy two goals: maximal gearbox ef-ficiency and minimal gearbox mass. To that end, the first stage's gear ratio and the first and sec-ond stages' CWFW were chosen as the three main design elements. Furthermore, two distinct goals were analyzed: the lowest gearbox mass and the highest gearbox efficiency. Additionally, the MOOP is carried out in two steps: phase 1 solves the single-objective optimization problem to close the gap between variable levels, and phase 2 solves the MOOP to determine the optimal primary design factors. Furthermore, the TOPSIS approach was selected to address the MOOP problem. For the first time, an MCDM technique is used to solve the MOOP of a helical gearbox with FSDG. Additionally, this is the first investigation of the total gearbox efficiency, which includes the power losses during idle. When designing the gearbox, the optimal values for three crucial design parameters were ascertained using the study's results.

Wear of cutting tools is known to affect the surface integrity of the workpiece and significantly contributes to machine downtime. To establish wear-resistant cutting tools, several coating strategies have been introduced. It has been shown that the wear rate increases dramatically once the coating is worn through. Detecting coating layer loss is therefore a good indicator of the remaining useful life of the cutting tool. Based on cutting experiments conducted with a TiN/AlTiN-coated cutting tool, an image dataset was generated and pre-processed using multiple algorithms, such as Canny edge detection and Hough line transforms. For the classification task four machine learning Algorithms consisting of Random Forests, Decision Trees, Support Vector Machines and a Feed Forward Neural Network were implemented. The results demonstrate that all four algorithms lead to a good classification performance, with Decision Trees showing the best performance with a F1-score of 0.95. Therefore, this research provides an efficient data processing and classification framework for detecting coating wear on cutting tools.