Grzegorz Królczyk

This study benchmarks exported optical roughness workflows against an internal tactile profilometry baseline across six engineering materials and multiple surface-generation processes. Rather than testing formal optical–tactile equivalence, the analysis examines which optical system–illumination workflows warrant prospective validation for specific material–process–parameter groups, and where tactile confirmation remains necessary. The main contribution is a reproducible workflow-level benchmark that integrates retrospective lowest-discrepancy, fixed-configuration, and leave-one-surface transfer analyses, thereby distinguishing local retrospective optimisation from more transferable performance. Eight profile roughness parameters were analysed. Optical–tactile discrepancy depended strongly on workflow choice. Among the lowest-discrepancy system–illumination combinations available in the archive, the smallest median discrepancies were observed for amplitude parameters including R t , R v , R z , and R z 1 max . Exported R s m values required separate treatment because of scale sensitivity: multiplying retained optical R s m values by 1000 reduced the retrospective lowest-discrepancy median from 99.9% to 17.3%, although 25% of material–process groups still exceeded 30%. Across strictly positive parameters, tactile random variability was generally smaller than the observed between-workflow discrepancies; the relative Type A standard uncertainty of the tactile mean had a median of 1.45%. A fixed-configuration sensitivity analysis showed that the complete-coverage fixed workflow with the lowest median discrepancy achieved 24.8%. In a leave-one-surface workflow-transfer check, selecting workflows from other material–process groups produced a held-out median discrepancy of 32.8%, with 52% of held-out groups still exceeding 30%. Overall, the resulting decision summaries establish a workflow-level benchmark for selecting optical candidates for prospective validation, while clearly distinguishing such candidates from claims of formal optical–tactile equivalence.

The journal retracts the article titled “Measuring Liquid Droplet Size in Two-Phase Nozzle Flow Employing Numerical and Experimental Analyses”

In milling operation, the analysis of power signals are quite important because power signals provide the immediate information of the process that allows the operator to monitor the performance and make necessary modifications in real time. In addition, the power signals are correlated with the quality of machined surface because high power may result in tool breakage that leads to poor surface finish. Therefore, this work firstly investigates the analysis of power signals and its impact on surface characteristics during the milling of additively manufactured aluminium alloys under different conditions. The milling experiments were performed under different cooling conditions and the power signals were calculated from the main cutting forces. Then, the Swin transformer based deep learning model was used on the calculated power signals, and the performance was compared with different models to validate the accuracy of Swin transformer model. Further, the relation between cutter angle rotation and power signals were analyzed under different conditions. According to the surface analysis, the higher power ranges observed during dry machining have a major impact on surface quality and lead to rough milling surfaces. The outcomes also demonstrated that the Swin transformer shows better performance with higher accuracy and Kappa metrics with 0.0001 learning rate.

Efficient detection and rectification of metal components conditions during manufacturing and post-processing manufacturing are crucial for quality control in industries. This paper describes a lab-scale integrated system for real-time and auto-mated metal edge image detection using YOLOv5...

Indonesia could transform the manufacturing industry by making Indonesia 4.0, despite the many uncertainties of implementing Industry 4.0 due to high investment costs and unclear returns. Therefore, looking at neighboring countries such as Germany, the country that initiated Industry 4.0, and China, the country taking the lead in implementing Industry 4.0, it is considered essential for the manufacturing industry in Indonesia to understand how towards the revolution and identify the development of the Industry 4.0 program. Germany is confident in its capabilities in the field of manufacturing technology. It makes the main challenge in carrying out Industry 4.0 'Investment Capital, Employee Qualifications, and Security of Data Transfer and Legislation'. On the other hand, China faces significant challenges in Manufacturing Capabilities, Research and Development (R&D), and Human Capital. To adopt the transformation technology and self-assess the internal resources, Indonesia created a tool, namely the Indonesia Industry 4.0 Readiness Index (INDI 4.0). This article presents a comparative review of the Industry 4.0 readiness index from the perspective of Germany and Singapore as a developed country compared to developing countries such as China, Malaysia, and Indonesia. This study aims to provide awareness related to the readiness index, which can be used to inform industries whether they are suitable for applying Industry 4.0 and how to measure whether their employees are capable of it. In general, the INDI 4.0 measuring instrument shows the readiness of companies in Indonesia, and according to the recent assessment, the industries in Indonesia are at a moderate level, especially in the field of technology application and operation.

(1) Background: Efficient detection and rectification of defects or post-processing manufacturing conditions such as sharp edges and burrs on metal components are crucial for quality control in precision manufacturing industries. (2) Methods: This paper describes a lab-scale integrated system for real-time and automated metal edge image detection using a customized YOLOv5 ma-chine vision algorithm. The YOLOv5 algorithm and model were developed and embedded in the NVIDIA Jetson Nano microprocessor. The YOLOv5 model can detect three different image types for classification i.e., normal, sharp, and burrs edge on aluminum workpiece blocks. An integrated system connects the NVIDIA Jetson Nano with an embedded YOLOv5 model to a Mitsubishi Electric Melfa RV-2F-1D1-S15 manipulator to perform selective automated chamfer-ing and grinding when certain condition defects are detected. (3) Results: The model demonstrates durable performance, achieving a mean average precision of 0.886 across defect classes with minimal misclassifications. The Mitsubishi Electric Melfa RV-2F-1D1-S15 manipulator received input from the machine vision system and performed an automated chamfering and grinding process accordingly; (4) Conclusions: By integrating camera, embedded deep learning in the microprocessor and manipulator, automated chamfering and grinding process in metal component shapes can be efficiently rectified. This tailored solution promises to improve productivity and consistency in metal manufacturing and remanufacturing.

Three-dimensional (3D) bioprinting has emerged as a revolutionary approach in the life sciences, combining multiple disciplines such as computer engineering, materials science, robotics, and biomedical engineering. This innovative technology enables the production of cellular constructs using bio-inks, and differs from conventional 3D printing by incorporating living cells. The present work addresses the conversion of a commercial thermoplastic 3D printer into a low-cost bioprinter. The modification addresses the challenges of the high cost of commercial bioprinters, limited adaptability, and specialized personnel requirements. This modification uses an extrusion-based bioprinting method that is particularly popular in research due to its viscosity tolerance and versatility. The individual steps, including replacing the extruder with a syringe pump, rebuilding the electronic motherboard, and configuring the firmware, are explained in detail. The work aims at providing access to bioprinting technology so that laboratories with modest resources can take advantage of the immense potential of this technology. This modification resulted in improved resolution, allowing submicron movements, which is comparable to some of the commercially available bioprinters. The accuracy of the modified printer was validated using hydrogel bioprinting tests, suggesting that it is suitable for broader applications in regenerative medicine.