Optimal tool replacement and resetting strategies in automated manufacturing systems

In manufacturing industry, the tool replacement cost is, in many cases, a significant portion of the production cost. Early tool replacement increases the production cost. Overdue tool replacement, however, results in poor production quality. Accordingly, improving production quality while maintaining a low production cost is essential. The index Cpk is regarded as a yield-based index. For a fixed Cpk value, the production yield and fraction of defectives can be calculated. In this paper, we present an analytical approach using Cpk to determine the optimal tool replacement time. An accurate process capability must be calculated, particularly when the data contain assignable cause variation. Tool wear is a dominant and inseparable component in many machining processes (a systematic assignable cause), and ordinary capability measures become inaccurate because process data are contaminated by the assignable cause variation. Considering process capability changes dynamically, an estimator of Cpk is investigated. The closed form of the exact sampling distribution is derived. An effective tool management procedure for determining the optimal tool replacement time is presented for processes with a low fraction of defectives. For illustrative purposes, an application example involving tool wear is presented.

Structural properties of optimal tool replacement policy in a machining center

Probabilistic cutting tool replacement models assume that tool life is stochastic in nature. This implies that a tool can wear out before the planned replacement, as a result of which penalty costs are incurred. If these costs, as well as the tool failure function and the cost of scheduled replacement, are known, optimal tool replacement time can be found. While many researchers have focused on the latter two elements, there are very few articles explaining what penalty costs are and how they should be calculated. Therefore this article presents an approach for estimating the costs of tool failure for a one-stage machining operation.

Determining the best time of tool replacement is critical to balancing production quality and tool utilization. A machining process would gradually produce defective parts as a tool wears out. To avoid additional production costs, replacing a tool before the yield drops below a minimum requirement is essential. On the other hand, frequent tool replacement would cause additional setup and tool costs. This paper proposes a hidden Markov model (HMM) to study the unknown nature of a tool wear progress by monitoring the quality characteristic of products. With the constructed model, the state of tool wear is diagnosed by using the Viterbi Algorithm. Then, a decision rule that evaluates the yield of the next machining part is proposed to determine the initiation of tool replacement. The simulation analysis shows that the proposed method could accurately estimate the model and the status of tool wear. The proposed decision rule can also make good use of tools whereas controlling yield.

SUMMARY In this paper, we develop an optimal tool replacement model based on the actual tool wear status. In this model, we consider separately the probability distribution of the tool capacity obtained under testing conditions and the tool capacity consumption by the workpieces. Special cases with exponential distribution for tool capacity consumption and in which one tool can process many workpieces are solved.

For a computer numerical controlled (CNC) turning centre with a multi-station tool turret, this paper describes a tool replacement strategy for tools used for both rough and finish turning, boring and facing operations as well as other end operations which include drilling, grooving, threading and parting-off. The method ensures a convenient replacement strategy where tools in the machine turret are replaced after machining integer multiples of an optimum number of components. This optimum number of components is determined so that the total cost due to tools and tool changing time is minimized. An example is given to illustrate how the procedure determines the optimal tool replacement strategy. It shows that the described method can substantially reduce the number of interruptions due to tool replacements in processing a batch.

In drilling Carbon-Fibre-Reinforced Polymers (CFRP)/Al stacks, adaptive drilling facilitates the optimisation of cutting parameters for each constituent stack layer and tool wear, thus enhancing cutting efficiency and borehole quality. This study proposed a knot–Temporal Pyramid Pooling (TPP) model aimed at monitoring both process incidences and tool wear in the drilling of hybrid stacks, which subsequently informs the machine tool to adjust cutting parameters or, if necessary, replaces the tool. TPP is introduced to remove the restriction of input dimensions, allowing for the acceptance of inputs with arbitrary shapes. On the other hand, a knot structure has been proposed to incorporate the classification of process incidences into the tool wear analysis, thereby enhancing prediction accuracy. The proposed model achieves a process incidence identification accuracy of 99.19% and a Mean Absolute Error (MAE) of 10 μm in tool wear prediction, demonstrating robust performance across a wide range of sampling conditions. This achievement facilitates decision-making and optimisation relating to cutting parameters and tool replacement in the context of adaptive drilling of aerospace materials.

Tool wear state is a crucial factor in the process of machine tool processing. Tool wear and failure affects the processing quality. Tool replacement in advance affects the production efficiency and increase the production cost. During the machining process, the power and vibration signal often change with the increase of tool wear, which provides a way to monitor tool wear. To meet the needs of tool wear monitoring, this paper presents a system architecture and hardware composition of power and vibration signal acquisition of machine tool, and develops a system to synchronously collect power, vibration signal and process parameters of machine tool. The vibration and power characteristic information recognition method is then discussed corresponding to the process to be monitored from the collected signals. The experimental results show that the system is able to collect and screen the changes of power and vibration signal throughout the whole life cycle of the cutting tool in real-time. The feature information recognition and screening have high efficiency and accuracy, which provides a data basis for tool wear monitoring.

The article examines key aspects of reliability and justification of the frequency of replacement of air filters in internal combustion engines. The optimal air filter replacement interval is critical for ensuring efficient engine operation and reducing wear on parts, minimizing fuel costs, and optimizing the efficiency of mobile energy resources. The purpose of this article is to analyze the factors that affect the reliability of engine air filters and to justify the optimal replacement interval based on statistical analysis. Research methods are considered, including laboratory and operational tests and the collection of statistical information. The limit state of the engine air filter is assessed based on a set of direct and indirect signs. The results of the replacement frequency of air filters in Mazda cars are presented with their statistical processing. Calculations have established the following indicators for the replacement frequency of engine air filters: the average replacement frequency is 10.1 thousand km; the standard deviation of the average replacement frequency is 2.99 thousand km; the coefficient of variation is 0.44; the shift in the beginning of the dispersion of the average replacement frequency is 3.25 thousand km. The confidence interval limits for the average frequency of engine air filter replacement for the normal distribution law have been established: 6.03 thousand km – lower confidence limit; 14.17 thousand km – upper confidence limit. The conclusions emphasize the importance of a comprehensive approach to determining the frequency of replacement of engine air filters, taking into account operating conditions, the quality of filter elements, and the manufacturer's plant. To achieve efficiency in use and savings in maintenance and repair costs, it is recommended to take into account the actual operating conditions of mobile power equipment and the technical condition of the filters.

This paper presents a tool wear monitoring framework for ultrasonic metal welding which has been used for lithium-ion battery manufacturing. Tool wear has a significant impact on joining quality. In addition, tool replacement, including horns and anvils, constitutes an important part of production costs. Therefore, a tool condition monitoring (TCM) system is highly desirable for ultrasonic metal welding. However, it is very challenging to develop a TCM system due to the complexity of tool surface geometry and a lack of thorough understanding on the wear mechanism. Here, we first characterize tool wear progression by comparing surface measurements obtained at different stages of tool wear, and then develop a tool condition classification algorithm to identify the state of wear. The developed algorithm is validated using tool measurement data from a battery plant.

Research on tool wear mechanism of liquid nitrogen internal cooling machining

Effects of repeated milling with a 1mm small-diameter cutting tool on tool dimensions and on the surface characteristics and marginal adaptation of zirconia implant-supported restorations.

Sound singularity analysis for milling tool condition monitoring towards sustainable manufacturing

A tool-change scheduling model for machining centres

A flexible manufacturing system (FMS) due to its ability of being flexible in nature is concerned with automatic production of different parts in medium range. In short, it can be regarded as an automated manufacturing system. In this paper, an insight on previous work in the area of FMS modelling has been provided with an overview of research and development for better understanding of FMS. It serves as a medium for investigation of work that are accomplished by using different modelling techniques in FMS like mathematical, artificial intelligence, hierarchical, multi criteria decision-making method, Petri Nets and simulation. This paper will help researchers who are keen to do research in the area of FMS modelling, by highlighting contribution of available techniques in the field of FMS. It will also help them in deciding which modelling techniques can be used for a given problem. At last, comparison of different parameters considered in the recent papers of modelling FMS has been provided in the form of Table, along with a clear vision related to those works that still need to be investigated.