For the application of Industry 4.0 and hence making the machine self aware, what optimization techniques could be used for a machining process? (Preferably please explain a mathematical model regarding the same or a case study)
Dear Ms Chirania,
There is a huge collection of papers on the topic "machining parameter optimization". These papers usually focus on specific materials or even machine tool.
Generally, we can classify optimization methodologies adopted in manufacturing domain into four classes namely,
1. Mathematical models developed based on theory or experiments. These models are solved either with exact algorithms or by using heuristics/meta-heuristics (like GA, TLBO, etc.)
2. Design of Experiments (DoE) methodologies like Taguchi, Response Surface Methodology, Full Factorial Designs, etc.
3. Subjective/Cognitive models like AHP, ANP, TOPSIS, PROMETHEE, Graph theory, GRA, etc.
4. Machine Learning algorithms. Provided you have enough data with you for training and testing the models. Usually students use Python platforms like Anaconda Navigator with Spyder or Jupyter for coding, analysis and optimization.
Since, you are focussing on advanced systems employing Industry 4.0 technologies, I prefer the fourth option. It is an emerging area as well.
You may refer to the following papers/books for further information on conventional techniques.
1. Decision Making in the Manufacturing Environment by R. Venkata Rao, Springer Series in Advanced Manufacturing. A fine book by SVNIT Professor.
2. Abhishek, K., Kumar, V. R., Datta, S., & Mahapatra, S. S. (2017). Parametric appraisal and optimization in machining of CFRP composites by using TLBO (teaching–learning based optimization algorithm). Journal of Intelligent Manufacturing, 28(8), 1769-1785.
3. Rao, R. V., Savsani, V. J., & Vakharia, D. P. (2011). Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-Aided Design, 43(3), 303-315.
4. Rao, R. (2016). Jaya: A simple and new optimization algorithm for solving constrained and unconstrained optimization problems. International Journal of Industrial Engineering Computations, 7(1), 19-34.
Reference #1 will provide you with the entire class of subjective models whereas, #3 reports a mathematical model. Other papers are on different meta-heuristics for solving mathematical models.
Regards and best wishes
Vishnu
Thanks Mr. Vishnu for answering the question. I really appreciate it.
I have worked as the tutor over a Tesis (written in Spanish Language) devoted to this problem based on it formulation as a multilevel optimization task and elaborated solution algorithms. In the work attached you can find it as an example of some methods application.
Best wishes,
Some times in order to know the important parameters, it is necesary to use a methodology such experiment design. Although it is time consuming, it is better in order to obtain better results
Any process is " A set of interacting/inter-related activities to produce a planned result".
So is a machining process.
What are the planned results?
What do you want to optimize? When you go deeper into the subject, it is not the process which is optimized per se. It is the output parameters which need to be :
--Maximized,
-Optimized/satisfied.
At the outset, the output of any machining process is QUALITY (in built into the product characteristics) and QUANTITY.
I am leaving out safety which is not an operational result.
Other parameters which can be added are:
-Power consumption;
-Operating parameters,
-Tooling expense.
But, please note that the two are THE INPUTS and not the planned OUTPUTs.
Surely all the above parameters are interrelated e.g operating parameters directly affect all the others.
Quality parameters are the ones to be achieved without any compromise. All the others can vary.
On this basis, you can, perhaps, start developing a model and proceed further.
Does this help explain the fundamentals?
All the best.
Virendra gupta
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