Towards AI-Enhanced Process Planning: Assessing Machine Tool Capability based on Part Design

The advent of the fourth industrial revolution, or Industry 4.0, calls for an automated approach to manufacturing process planning, which begins with an appraisal of machine tool capacity to handle specific part geometries and microstructures. Once capabilities match requirements, the objective becomes to devise an efficient method for translating design elements into tangible components. The decline in the U.S. manufacturing sector's skilled labor force, due to an aging demographic and the loss of seasoned experts, underscores the critical need for a shift towards an Industry 4.0 autonomous paradigm. This work seeks to create and validate a framework that determines the manufacturability of design features by the available machinery and materials. The ultimate goal is to enhance process planning by ensuring a seamless conversion of designs into physical products, with a particular focus on the intricate details of geometry, microstructure, and cost.

Authors

Publication Details

Published
2024-10-01
DOI
https://doi.org/10.21872/2024iise_6002
Primary Topic
Manufacturing Process and Optimization
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Towards AI-Enhanced Process Planning: Assessing Machine Tool Capability based on Part Design

M. Aziz Ben Youssef, Sepideh Abolghasem, Amman Pandde
Manufacturing Process and Optimization
article

Towards AI-Enhanced Process Planning: Assessing Machine Tool Capability based on Part Design

M. Aziz Ben Youssef, Sepideh Abolghasem, Amman Pandde
article en

Abstract

The advent of the fourth industrial revolution, or Industry 4.0, calls for an automated approach to manufacturing process planning, which begins with an appraisal of machine tool capacity to handle specific part geometries and microstructures. Once capabilities match requirements, the objective becomes to devise an efficient method for translating design elements into tangible components. The decline in the U.S. manufacturing sector's skilled labor force, due to an aging demographic and the loss of seasoned experts, underscores the critical need for a shift towards an Industry 4.0 autonomous paradigm. This work seeks to create and validate a framework that determines the manufacturability of design features by the available machinery and materials. The ultimate goal is to enhance process planning by ensuring a seamless conversion of designs into physical products, with a particular focus on the intricate details of geometry, microstructure, and cost.

Openalex Percentile: Top 55%
Manufacturing Process and Optimization
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.

Towards AI-Enhanced Process Planning: Assessing Machine Tool Capability based on Part Design — M. Aziz Ben Youssef, Sepideh Abolghasem, et al. · (2024) | TGRS Research Map | TGRS