Constrained grinding optimization for time, cost, and surface roughness using NSGA-II

In this paper, we will perform a comparison between two approaches of dimensional synthesis of parallel robots. The first one concerns the single-objective optimization approach; in this case, the dimensional synthesis is expressed by taking into account only one performance criterion but enables to get a final solution if it exists. The second one concerns the multi-objective optimization approach; it enables to simultaneously take into account several performance criteria. However, thisRidha Kelaiaia approach appears to provide a set of solutions instead of a single expected final solution which should directly enable to carry out the structural synthesis. In fact, the search of a single final solution is postponed to a further step where the designers have to impose and/or restrict certain parameters. And we will establish if it is really necessary to make a multi-objective optimization approach or if a singleobjective is sufficient to reach the objectives set in the specifications (user requirements). A discussion is proposed concerning the arising questions related to each approach and leading to the optimal dimensional synthesis. The PAR2 robot with two degree-of-freedom is used to exemplify the analysis and the comparison of the two approaches. The proposed comparison can be applied to any classes of parallel robots.

“…Multi-objective optimization has been widely applied to robot designs to achieve a number of design objectives that are often conflicting [11][12][13].…”

Section: Dimensional Synthesis Of the Par2 Robot Based On A Multi-objmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Some investigations into the optimal dimensional synthesis of parallel robots

Kelaiaia

Zaatri

Chikh³

2015

“…The NSGA-II proposed by Deb et al is a well-known algorithm for solving multi-objective optimization problems using a non-dominated approach [6]. It has been successfully applied to many multi-objective problems, e.g., facility location problem [3], multi-site order scheduling problem (MSOS) [12], generation expansion planning problem [24], and constrained grinding optimization for time, cost, and surface roughness [8]. To minimize the CD and makespan in MOPAD, a mathematical model is established and three multi-objective genetic algorithms with a three-tuples chromosome design are proposed in this paper.…”

Section: Literature Reviewmentioning

confidence: 99%

Multi-objective optimization of collation delay and makespan in mail-order pharmacy automated distribution system

Mei

Yoon

et al. 2015

In this research, the collation delay (CD) and makespan minimization problem in a mail-order pharmacy automated distribution (MOPAD) system are studied. The MOPAD systems, which are integrated with pharmaceutical auto-dispenser machines, auto-packer machines, and conveyor, are utilized to fulfill the increasing prescription demand in recent years. The motivation of this research is derived from the practical deadlock problem in a MOPAD system of the central fill pharmacies (CFP). Most of the customer orders consist of multiple medications, which need to be collated together before being packaged and shipped. The CD is defined as the fulfillment completion time difference between the first and last medications within the same order, which is a critical factor of the MOPAD systems throughput. When CD is minimized, the makespan often increases. Therefore, alternative scheduling solutions are often needed to balance the CD and makespan in the MOPAD system. This paper presents the trade-off solutions between minimizing CD and the makespan.

“…For example, in [5] a method for the optimization of a grinding process in batch production using a grinding process model and a dynamic programming approach to update process parameters between grinding cycles is demonstrated. In [6] constrained multi-objective optimization of grinding with Pareto fronts using genetic algorithms is shown. Another ingredient for optimization is process monitoring using measurements from force or power sensors, acoustic emission sensors and temperature sensors [7].…”

Section: Introductionmentioning

confidence: 99%

Self-optimizing grinding machines using Gaussian process models and constrained Bayesian optimization

Maier

Rupenyan

Bobst³

et al. 2020

In this study, self-optimization of a grinding machine is demonstrated with respect to production costs, while fulfilling quality and safety constraints. The quality requirements of the final workpiece are defined with respect to grinding burn and surface roughness, and the safety constrains are defined with respect to the temperature at the grinding surface. Grinding temperature is measured at the contact zone between grinding wheel and workpiece using a pyrometer and an optical fiber, which is embedded inside the rotating grinding wheel. Constrained Bayesian optimization combined with Gaussian process models is applied to determine the optimal feed rate and cutting speed of a cup wheel grinding machine manufacturing tungsten carbide cutting inserts. The approach results in the determination of optimal parameters for unknown workpiece and tool combinations after only a few grinding trials. It also incorporates the uncertainty of the constraints in the prediction of optimal parameters by using stochastic process models 1 .