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Updated: May 30, 2026

The Modular Design and Production of an Intelligent Robot Based on a Closed-Loop Control Strategy

The Modular Design and Production of an Intelligent Robot Based on a Closed-Loop Control Strategy

Published on: October 14, 2017

Optimization process planning using hybrid genetic algorithm and intelligent search for job shop machining.

Mojtaba Salehi¹, Ardeshir Bahreininejad

¹Faculty of Engineering, Tarbiat Modares University, 14115 Tehran, Iran.

Journal of Intelligent Manufacturing

|August 17, 2011

Summary

This study optimizes computer-aided process planning by improving operation sequences and selecting optimal machines, tools, and Tool Access Directions (TAD). It uses intelligent search and genetic algorithms for efficient manufacturing process planning.

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Last Updated: May 30, 2026

The Modular Design and Production of an Intelligent Robot Based on a Closed-Loop Control Strategy

The Modular Design and Production of an Intelligent Robot Based on a Closed-Loop Control Strategy

Published on: October 14, 2017

Area of Science:

Manufacturing Engineering
Operations Research
Computational Intelligence

Background:

Process planning is crucial for computer-aided manufacturing, yet complex.
Effective process plans require optimized operation sequences and resource selection (machine, tool, TAD).

Purpose of the Study:

To develop an integrated approach for optimizing both operation sequencing and resource selection in process planning.
To enhance the efficiency and effectiveness of computer-aided process planning (CAPP).

Main Methods:

Divided process planning into preliminary (intelligent search for feasible sequences) and detailed stages (genetic algorithm for optimization).
Utilized order and clustering constraints for operation sequencing and optimization constraints for resource selection.
Employed intelligent search strategies and genetic algorithms concurrently.

Main Results:

Generated feasible operation sequences through intelligent search in the preliminary stage.
Achieved optimized operation sequences and resource selections (machine, cutting tool, TAD) in the detailed stage.
Demonstrated simultaneous optimization of sequence and resources using integrated algorithms.

Conclusions:

The proposed method effectively optimizes both operation sequence and resource selection in process planning.
Integration of intelligent search and genetic algorithms offers a robust solution for complex CAPP challenges.
This approach contributes to improved manufacturing efficiency through advanced process planning optimization.