Smart process planning of crankshaft machining through multiple objectives optimization

The formulation and selection of parameters and sequences in crankshaft production present challenges that are both demanding and time-intensive. This study introduces an innovative approach to intelligent process planning in crankshaft machining lines using multi-turret machines. Emphasis is placed on automating process planning through multi-objective optimization of critical decisions such as process parameters, operation sequencing, and tool positioning on turret magazines. The principal objectives addressed include minimizing machining and non-machining time, reducing costs by optimizing tool life, and enhancing product quality through optimal surface roughness. By automating these decision points, the proposed framework reduces manual intervention and aligns with Industry 4.0 goals for adaptive, data-driven manufacturing. Additionally, we discuss the potential future incorporation of artificial intelligence agents to dynamically refine parameters and enable adaptive planning.

Authors

Institutions

Publication Details

Journal
Procedia CIRP
Published
2025-01-01
DOI
https://doi.org/10.1016/j.procir.2025.03.018
Primary Topic
Manufacturing Process and Optimization
Type
article
Field-Weighted Citation Impact
0.00

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Smart process planning of crankshaft machining through multiple objectives optimization

Kaveh Amouzgar, Mahdi Eynian, Amos H.C. Ng, Wei Wang
Procedia CIRP
Manufacturing Process and Optimization
article

Smart process planning of crankshaft machining through multiple objectives optimization

Kaveh Amouzgar, Mahdi Eynian, Amos H.C. Ng, Wei Wang
article en

Abstract

The formulation and selection of parameters and sequences in crankshaft production present challenges that are both demanding and time-intensive. This study introduces an innovative approach to intelligent process planning in crankshaft machining lines using multi-turret machines. Emphasis is placed on automating process planning through multi-objective optimization of critical decisions such as process parameters, operation sequencing, and tool positioning on turret magazines. The principal objectives addressed include minimizing machining and non-machining time, reducing costs by optimizing tool life, and enhancing product quality through optimal surface roughness. By automating these decision points, the proposed framework reduces manual intervention and aligns with Industry 4.0 goals for adaptive, data-driven manufacturing. Additionally, we discuss the potential future incorporation of artificial intelligence agents to dynamically refine parameters and enable adaptive planning.

Procedia CIRPVol. 134
Uppsala University (SE), University of Skövde (SE)
Stiftelsen för Kunskaps- och Kompetensutveckling
Openalex Percentile: Top 81%
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.