PSH is a design company established in 2009 in Vietnam that provides CAD, CAS, and reverse engineering services. Its vision is to build a young, professional company representing Vietnam's style. PSH's core values are commitment to quality, timeliness, and sustainable development. Services include 3D CAD modeling, Computer Aided Styling with Class A surfaces, and reverse engineering with parametric models and Class A surfaces. Samples of past work include vehicle modeling in CATIA and Alias. PSH offers pilot projects free of charge to help customers assess quality.
Do you want to create 3D model data from scanned data ( RE ) / CAD / CAS ? Please contact us today !
We have Professional Team & Office in Germany and Viet Nam
We can create almost native CAD files in output & High Quality Surface
PSH is a design company established in 2009 in Vietnam that provides CAD, CAS, and reverse engineering services. Its vision is to build a young, professional company representing Vietnam's style. PSH's core values are commitment to quality, timeliness, and sustainable development. Services include 3D CAD modeling, Computer Aided Styling with Class A surfaces, and reverse engineering with parametric models and Class A surfaces. Samples of past work include vehicle modeling in CATIA and Alias. PSH offers pilot projects free of charge to help customers assess quality.
Do you want to create 3D model data from scanned data ( RE ) / CAD / CAS ? Please contact us today !
We have Professional Team & Office in Germany and Viet Nam
We can create almost native CAD files in output & High Quality Surface
Reverse engineering can generate either a parametric solid model or hybrid surface model from scan data. A parametric solid model uses a CAD program to create a solid model that can be modified parametrically and includes or ignores manufacturing defects. A hybrid surface model results in a patchwork of small surfaces that can have modifications requiring re-trimming and captures the as-built state including defects.
Our services are available with the highest quality with PointSense Plant , AutoCAD Plant 3D , Design X
Output file :
-PointSense Plant
-Autocad 2D , 3D
-original parametric Solidworks , Inventor 2016
- Design X file
Point cloud data consists of a set of data points in 3D space that represent the surface of an object or scene. Surface model data takes point cloud data and fits geometric surfaces like triangles to create a 3D mesh representation. Together, point cloud and surface model data are important formats for 3D scanning and modeling applications.
Casting Part in Reverse Engineering with full parametric Mode_PSH Update
Mode with full parameters and original in Solidworks, Catia, Solid Edge, Pro / Creo, Inventor, Autocad
Automotive-Casting Part in Reverse Engineering_ PSH Mechanical Design From scan data to Fully parametric model in Catia, Pro/E , SW, Creo, Inventor, NX , AutoCad
This document contains 4 project details for PSH Automotive Projects and a list of 4 Class A Surface automotive designers. Project Detail 01 and 02 involve CAS by Alias and reverse engineering for pilot projects. Project Detail 03 specifies reverse engineering of bumpers, chassis systems, body sides, and mudguards. Project Detail 04 mentions reverse engineering of plastic seat parts and mirrors. The list of Class A Surface designers includes names, years of experience, professional experience, languages spoken, and countries worked in.
Discover the latest insights on Data Driven Maintenance with our comprehensive webinar presentation. Learn about traditional maintenance challenges, the right approach to utilizing data, and the benefits of adopting a Data Driven Maintenance strategy. Explore real-world examples, industry best practices, and innovative solutions like FMECA and the D3M model. This presentation, led by expert Jules Oudmans, is essential for asset owners looking to optimize their maintenance processes and leverage digital technologies for improved efficiency and performance. Download now to stay ahead in the evolving maintenance landscape.
Reverse engineering can generate either a parametric solid model or hybrid surface model from scan data. A parametric solid model uses a CAD program to create a solid model that can be modified parametrically and includes or ignores manufacturing defects. A hybrid surface model results in a patchwork of small surfaces that can have modifications requiring re-trimming and captures the as-built state including defects.
Our services are available with the highest quality with PointSense Plant , AutoCAD Plant 3D , Design X
Output file :
-PointSense Plant
-Autocad 2D , 3D
-original parametric Solidworks , Inventor 2016
- Design X file
Point cloud data consists of a set of data points in 3D space that represent the surface of an object or scene. Surface model data takes point cloud data and fits geometric surfaces like triangles to create a 3D mesh representation. Together, point cloud and surface model data are important formats for 3D scanning and modeling applications.
Casting Part in Reverse Engineering with full parametric Mode_PSH Update
Mode with full parameters and original in Solidworks, Catia, Solid Edge, Pro / Creo, Inventor, Autocad
Automotive-Casting Part in Reverse Engineering_ PSH Mechanical Design From scan data to Fully parametric model in Catia, Pro/E , SW, Creo, Inventor, NX , AutoCad
This document contains 4 project details for PSH Automotive Projects and a list of 4 Class A Surface automotive designers. Project Detail 01 and 02 involve CAS by Alias and reverse engineering for pilot projects. Project Detail 03 specifies reverse engineering of bumpers, chassis systems, body sides, and mudguards. Project Detail 04 mentions reverse engineering of plastic seat parts and mirrors. The list of Class A Surface designers includes names, years of experience, professional experience, languages spoken, and countries worked in.
Discover the latest insights on Data Driven Maintenance with our comprehensive webinar presentation. Learn about traditional maintenance challenges, the right approach to utilizing data, and the benefits of adopting a Data Driven Maintenance strategy. Explore real-world examples, industry best practices, and innovative solutions like FMECA and the D3M model. This presentation, led by expert Jules Oudmans, is essential for asset owners looking to optimize their maintenance processes and leverage digital technologies for improved efficiency and performance. Download now to stay ahead in the evolving maintenance landscape.
artificial intelligence and data science contents.pptxGauravCar
What is artificial intelligence? Artificial intelligence is the ability of a computer or computer-controlled robot to perform tasks that are commonly associated with the intellectual processes characteristic of humans, such as the ability to reason.
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Artificial intelligence (AI) | Definitio
Advanced control scheme of doubly fed induction generator for wind turbine us...IJECEIAES
This paper describes a speed control device for generating electrical energy on an electricity network based on the doubly fed induction generator (DFIG) used for wind power conversion systems. At first, a double-fed induction generator model was constructed. A control law is formulated to govern the flow of energy between the stator of a DFIG and the energy network using three types of controllers: proportional integral (PI), sliding mode controller (SMC) and second order sliding mode controller (SOSMC). Their different results in terms of power reference tracking, reaction to unexpected speed fluctuations, sensitivity to perturbations, and resilience against machine parameter alterations are compared. MATLAB/Simulink was used to conduct the simulations for the preceding study. Multiple simulations have shown very satisfying results, and the investigations demonstrate the efficacy and power-enhancing capabilities of the suggested control system.
Applications of artificial Intelligence in Mechanical Engineering.pdfAtif Razi
Historically, mechanical engineering has relied heavily on human expertise and empirical methods to solve complex problems. With the introduction of computer-aided design (CAD) and finite element analysis (FEA), the field took its first steps towards digitization. These tools allowed engineers to simulate and analyze mechanical systems with greater accuracy and efficiency. However, the sheer volume of data generated by modern engineering systems and the increasing complexity of these systems have necessitated more advanced analytical tools, paving the way for AI.
AI offers the capability to process vast amounts of data, identify patterns, and make predictions with a level of speed and accuracy unattainable by traditional methods. This has profound implications for mechanical engineering, enabling more efficient design processes, predictive maintenance strategies, and optimized manufacturing operations. AI-driven tools can learn from historical data, adapt to new information, and continuously improve their performance, making them invaluable in tackling the multifaceted challenges of modern mechanical engineering.
Optimizing Gradle Builds - Gradle DPE Tour Berlin 2024Sinan KOZAK
Sinan from the Delivery Hero mobile infrastructure engineering team shares a deep dive into performance acceleration with Gradle build cache optimizations. Sinan shares their journey into solving complex build-cache problems that affect Gradle builds. By understanding the challenges and solutions found in our journey, we aim to demonstrate the possibilities for faster builds. The case study reveals how overlapping outputs and cache misconfigurations led to significant increases in build times, especially as the project scaled up with numerous modules using Paparazzi tests. The journey from diagnosing to defeating cache issues offers invaluable lessons on maintaining cache integrity without sacrificing functionality.
Embedded machine learning-based road conditions and driving behavior monitoringIJECEIAES
Car accident rates have increased in recent years, resulting in losses in human lives, properties, and other financial costs. An embedded machine learning-based system is developed to address this critical issue. The system can monitor road conditions, detect driving patterns, and identify aggressive driving behaviors. The system is based on neural networks trained on a comprehensive dataset of driving events, driving styles, and road conditions. The system effectively detects potential risks and helps mitigate the frequency and impact of accidents. The primary goal is to ensure the safety of drivers and vehicles. Collecting data involved gathering information on three key road events: normal street and normal drive, speed bumps, circular yellow speed bumps, and three aggressive driving actions: sudden start, sudden stop, and sudden entry. The gathered data is processed and analyzed using a machine learning system designed for limited power and memory devices. The developed system resulted in 91.9% accuracy, 93.6% precision, and 92% recall. The achieved inference time on an Arduino Nano 33 BLE Sense with a 32-bit CPU running at 64 MHz is 34 ms and requires 2.6 kB peak RAM and 139.9 kB program flash memory, making it suitable for resource-constrained embedded systems.
Rainfall intensity duration frequency curve statistical analysis and modeling...bijceesjournal
Using data from 41 years in Patna’ India’ the study’s goal is to analyze the trends of how often it rains on a weekly, seasonal, and annual basis (1981−2020). First, utilizing the intensity-duration-frequency (IDF) curve and the relationship by statistically analyzing rainfall’ the historical rainfall data set for Patna’ India’ during a 41 year period (1981−2020), was evaluated for its quality. Changes in the hydrologic cycle as a result of increased greenhouse gas emissions are expected to induce variations in the intensity, length, and frequency of precipitation events. One strategy to lessen vulnerability is to quantify probable changes and adapt to them. Techniques such as log-normal, normal, and Gumbel are used (EV-I). Distributions were created with durations of 1, 2, 3, 6, and 24 h and return times of 2, 5, 10, 25, and 100 years. There were also mathematical correlations discovered between rainfall and recurrence interval.
Findings: Based on findings, the Gumbel approach produced the highest intensity values, whereas the other approaches produced values that were close to each other. The data indicates that 461.9 mm of rain fell during the monsoon season’s 301st week. However, it was found that the 29th week had the greatest average rainfall, 92.6 mm. With 952.6 mm on average, the monsoon season saw the highest rainfall. Calculations revealed that the yearly rainfall averaged 1171.1 mm. Using Weibull’s method, the study was subsequently expanded to examine rainfall distribution at different recurrence intervals of 2, 5, 10, and 25 years. Rainfall and recurrence interval mathematical correlations were also developed. Further regression analysis revealed that short wave irrigation, wind direction, wind speed, pressure, relative humidity, and temperature all had a substantial influence on rainfall.
Originality and value: The results of the rainfall IDF curves can provide useful information to policymakers in making appropriate decisions in managing and minimizing floods in the study area.
Electric vehicle and photovoltaic advanced roles in enhancing the financial p...IJECEIAES
Climate change's impact on the planet forced the United Nations and governments to promote green energies and electric transportation. The deployments of photovoltaic (PV) and electric vehicle (EV) systems gained stronger momentum due to their numerous advantages over fossil fuel types. The advantages go beyond sustainability to reach financial support and stability. The work in this paper introduces the hybrid system between PV and EV to support industrial and commercial plants. This paper covers the theoretical framework of the proposed hybrid system including the required equation to complete the cost analysis when PV and EV are present. In addition, the proposed design diagram which sets the priorities and requirements of the system is presented. The proposed approach allows setup to advance their power stability, especially during power outages. The presented information supports researchers and plant owners to complete the necessary analysis while promoting the deployment of clean energy. The result of a case study that represents a dairy milk farmer supports the theoretical works and highlights its advanced benefits to existing plants. The short return on investment of the proposed approach supports the paper's novelty approach for the sustainable electrical system. In addition, the proposed system allows for an isolated power setup without the need for a transmission line which enhances the safety of the electrical network
Design and optimization of ion propulsion dronebjmsejournal
Electric propulsion technology is widely used in many kinds of vehicles in recent years, and aircrafts are no exception. Technically, UAVs are electrically propelled but tend to produce a significant amount of noise and vibrations. Ion propulsion technology for drones is a potential solution to this problem. Ion propulsion technology is proven to be feasible in the earth’s atmosphere. The study presented in this article shows the design of EHD thrusters and power supply for ion propulsion drones along with performance optimization of high-voltage power supply for endurance in earth’s atmosphere.