Manufacturing Technology Today

Selection of best decontamination resistant coating on alloy steel substrate

2026-07-08T05:58:56+00:00

This study presents a comparative evaluation of advanced corrosion-resistant coatings Electroless Ni, Zn-Ni, and Zn-Al flake under simulated decontamination conditions to determine the most effective protection for industrial systems exposed to aggressive chemical. Unlike conventional studies focused solely on corrosion resistance, this work integrates chemical decontamination exposure, immersion testing, and non-destructive evaluation to assess coating integrity, adhesion, and durability over time. The results establish Zn-Al flake coatings (20 μm) as superior candidates capable of maintaining structural and chemical stability under demanding operational conditions, thereby enhancing equipment lifespan and reducing maintenance costs in industrial systems.

High accuracy topography measurement using a precision autocollimator:

2026-07-08T06:09:29+00:00

Nowadays, industries demand up-to sub-nanometer measurement accuracy to meet the precision required for advanced device manufacturing. To advance beyond the limitations of traditional interferometry that fail to measure large optical surfaces, here, an advanced autocollimator-based methodology has been presented for absolute topographical measurement of near-flat optical surfaces with minimal measurement uncertainty. An autocollimator and pentaprism were used to focus the beam on the test component. The autocollimator was first aligned with the pentaprism to achieve a 90° beam deflection. A near-flat optical specimen was positioned vertically and scanned to measure angular deviations across its surface. Slope measurements were then taken at nearly identical working distances using the same calibrated autocollimator. Thereafter, Newton-Cotes integration (trapezoidal integration, ⅛ Simpson’s rule, ⅜ Simpson’s Rule) and Fast Fourier Transform (FFT) integration were implemented for the reconstruction of the topography of the surface from the measured angles. Finally, the reconstructed topographies derived from each method were analyzed using suitable statistical tools, which indicated that the FFT method performs the best in terms of the flatness estimation.

Comprehensive testing and performance evaluation of flame proof three-phase induction motor

2026-07-08T06:33:06+00:00

This paper presents the results of performance and type testing conducted on a flameproof three-phase induction motor operated through a Variable Frequency Drive (VFD) in accordance with International Electro-Technical standards (IEC), and Indian standards (IS). Tests including resistance measurement, heat run (temperature rise), no-load and load point evaluation, locked rotor, noise and vibration, insulation, reduced voltage, overload, and overspeed verification. Resistance measurements before and after the heat run indicated a predictable rise within acceptable limits for Class F insulation, confirming effective thermal behaviour. During the heat run at 50 Hz, the motor achieved thermal equilibrium with all temperature rises within permissible limits. Subsequent operation at 25 Hz demonstrated stable input and output characteristics and reliable performance under variable frequency conditions. Load tests showed high efficiency (~94–95%) with minimal speed variation, while noise and vibration levels remained well within IEC 60034-9 and IEC 60034-14 limits. Insulation resistance exceeded 200 MΩ, and the motor withstood high-voltage, overload, overspeed, and reduced-voltage tests. Overall, the motor exhibited excellent electrical, thermal, and mechanical stability, validating its compliance with IEC 60034 series and IS 4029:2010 standards and confirming its suitability for demanding industrial applications.

A systematic review on stylus-based instruments for surface roughness measurement

2026-07-08T06:43:14+00:00

Evaluation of surface roughness is crucial to obtain high-quality products from different manufacturing processes. Eventually, machined products are tested using sophisticated surface roughness measuring instruments to accurately estimate various roughness parameters, among which contact methods are more popular for higher accuracy and suitability in industrial settings. This paper summarizes the current trends and future scopes of contact-based surface roughness measuring instruments, associated measurement strategies, and the field of application. The challenges of the most commonly used stylus-based devices in terms of construction, operational complexity, and affordability are discussed, and the recent advancements in bridging the gaps are presented. Furthermore, the reasons behind the limited success of optical and pneumatic-based measurements are consolidated. Much of this review paper focuses on the newest contact methods reported for direct and indirect measurement approaches and their contribution toward developing simple, accurate, and affordable strategies for surface roughness measurement. Lastly, the promising avenues of future research in surface roughness measurement are highlighted.

Simulative optimization of sonotrode geometry for ultrasonic vibration assisted diamond turning

2026-07-08T07:04:54+00:00

Sonotrode is a device used in ultrasonic assisted cutting, welding and machining processes. The use of sonotrode has improved the efficiency of processes by methodical implementation and transfer of ultrasonic vibrations from source to the systems. In manufacturing sector, hybrid machining is gaining traction in the machine tool industry due to emerging demands of the machining complexity in terms of component geometry, material property, machining accuracy, etc. This paper focuses on pre-liminary design and analysis of a sonotrode geometry for ultrasonic vibration assisted diamond turning application. This study shows optimization method of sonotrode geometry which can serve as a design process flow reference for researchers in the field of precision machining for development of similar sonotrode for their varied machining applications.

Deep learning-based adaptive defect classification in vision systems

2026-07-08T07:14:53+00:00

Modern automatic inspection systems face mounting pressure from shrinking defect sizes, increasing product variability, and zero tolerance for quality escapes. Static, rule-based vision systems struggle to maintain performance as production conditions evolve. While deep learning models that learn defect patterns from images have matured sufficiently for industrial deployment, research indicates that most installations face a critical challenge: models trained on initial production data degrade as processes drift and new defect modes emerge. This article presents a systematic taxonomy and unified framework for adaptive defect classification—closed-loop pipelines that continuously harvest production data, detect domain drift, and update classifiers with minimal human intervention. We classify existing approaches across four dimensions (learning strategy, drift detection, annotation efficiency, and deployment complexity), compare representative methods through structured analysis, and synthesize deployment guidelines from industrial case studies. Our framework integrates five inter-dependent components validated across multiple manufacturing domains, demonstrating that adaptive approaches can significantly improve long-term model performance while maintaining operational efficiency.