ExaAM: Metal additive manufacturing simulation at the fidelity of the microstructure

Additive manufacturing (AM), or 3D printing, of metals is transforming the fabrication of components, in part by dramatically expanding the design space, allowing optimization of shape and topology. However, although the physical processes involved in AM are similar to those of welding, a field with decades of experimental, modeling, simulation, and characterization experience, qualification of AM parts remains a challenge. The availability of exascale computational systems, particularly when combined with data-driven approaches such as machine learning, enables topology and shape optimization as well as accelerated qualification by providing process-aware, locally accurate microstructure and mechanical property models. We describe the physics components comprising the Exascale Additive Manufacturing simulation environment and report progress using highly resolved melt pool simulations to inform part-scale finite element thermomechanics simulations, drive microstructure evolution, and determine constitutive mechanical property relationships based on those microstructures using polycrystal plasticity. We report on implementation of these components for exascale computing architectures, as well as the multi-stage simulation workflow that provides a unique high-fidelity model of process–structure–property relationships for AM parts. In addition, we discuss verification and validation through collaboration with efforts such as AM-Bench, a set of benchmark test problems under development by a team led by the National Institute of Standards and Technology.

A System Modelling Approach Optimization Process and Machine Utilization In Casting Plant Using Lean Manufacturing Simulation Model

Perbaikan berkelanjutan, reduksi dan eliminasi waste dalam proses bisnis menjadi salah satu aspek yang dilakukan agar dapat terus memiliki daya saing yang sustainable. PT. X saat ini sedang dalam proses melakukan transformasi, reduksi dan eliminasi NVA serta perbaikan berkelanjutan di sisi proses guna meningkatkan daya saing yang sustainable. penelitian ini bertujuan dalam menganalisis dan mengajukan formulasi perbaikan proses menggunakan metode kerangka kerja pemodelan sistem dan simulasi. Temuan dari penelitian diketahui bahwa peningkatan Utilisasi Dies rata-rata 82,82 % relative meningkat 36% dibanding simulasi sebelumnya (52,9%); dengan rata-rata output 21,04 pcs/jam relative meningkat 42% dibanding simulasi sebelumnya (12,9 pcs/jam), hal ini dengan melakukan improvement proses semi auto dalam proses eject produk yang sekaligus berdampak dalam pengurangan manpower, serta mereduksi loss time akibat lama proses pendinginan dengan sistem heat transfer conveyor system

Networking of Digital Twins in the Digital Factory for Single Part Manufacturing Simulation

Organizing manufacturing in dynamic networks instead of inflexible production lines is one of the key aspects of Industry 4.0. This should serve to realize automation and effectiveness to a higher degree than previously achievable. For this modernization, Cyber-Physical Systems should be utilized, where a Digital Twin mirrors the behavior of its Physical Twin and makes the data during manufacturing externally available via communication interfaces. This Digital Twin should be an instantiation of a Digital Master, which must meet the requirements for communication in dynamically changing value-added networks. The networking capability of objects requires semantic information. This information is associated with rules for decision making within a value-added network. This paper addresses the need for research on how to add networking capabilities during the development of Digital Masters. With these added capabilities, the communication between Digital Masters and Twins in terms of a single part manufacturing simulation should be verifiable in a Digital Factory. For this purpose, the concept of this paper aims to outline guidelines on how to add networking capabilities to the single part, machines and other resources needed during manufacturing.

Data-Driven Manufacturing Simulation: Towards a CPS-Based Approach

Manufacturing simulation has been used as a decision support tool to solve various problems in production systems. However, with the advent of Industry 4.0 and CPS, manufacturing simulation becomes not only a tool for supporting decision-making but also essential for operation, monitoring, and forecasting the production system. In this paper, a traditional approach and a CPS-based approach in manufacturing simulation are compared. In the CPS-based approach, the key processes are divided into 1) data gathering, 2) modeling and simulation, and 3) simulation results analytics and feedback. In addition, a SWOT analysis is conducted to discuss the future application of the manufacturing simulation.

USE OF COMPUTER SIMULATION FOR OPTIMIZATION OF ENERGY CONSUMPTION IN THE MANUFACTURING PROCESS

This article provides information on using computer simulation to optimise energy consumption in manufacturing. Simulation of energy consumption in operation is always performed to analyse large amounts of data. Results of computer simulation offer the possibility to analyse different scenarios of production or use of machines. Each of the variants brings different scenarios to optimise energy use in order to reduce costs and improve the operation. The article also describes the sequence of steps that a user can take to create a quality simulation model. At the same time, there are graphical examples of energy consumption measurements made during simulation runs.

USE OF COMPUTER SIMULATION FOR OPTIMIZATION OF ENERGY CONSUMPTION IN THE MANUFACTURING PROCESS

This article provides information on using computer simulation to optimise energy consumption in manufacturing. Simulation of energy consumption in operation is always performed to analyse large amounts of data. Results of computer simulation offer the possibility to analyse different scenarios of production or use of machines. Each of the variants brings different scenarios to optimise energy use in order to reduce costs and improve the operation. The article also describes the sequence of steps that a user can take to create a quality simulation model. At the same time, there are graphical examples of energy consumption measurements made during simulation runs.