Machine Production Research Articles

Application of the Finite Element Method for Analyzing the Vibration Characteristics of a Spindle System and Fault Diagnosis

The performance of a spindle will gradually decline, the vibration intensity will increase, and the temperature rise will become abnormal with the accumulation of service time. Consequently, the accuracy of the machining product will not meet its production requirements. Studies on the variation characteristics of the spindle unit have clarified the reasons for its abnormal vibration and temperature rise, in principle, to help enterprises conduct preventive maintenance before any serious failure occurs and improve production efficiency. Based on the Timoshenko beam model and rotor dynamics theory, this study uses the finite element method (FEM) to analyze the vibration characteristics of the spindle rotor system. Moreover, it thoroughly analyzes the influence of the spindle bearing heat on the stiffness of the spindle system and identifies the resonance conditions of the spindle rotor within the power frequency range. This research has identified that when the vibration frequency of the spindle operates at a speed of 12,000 r/min, if its vibration frequency is lower than 200 Hz, it will be affected by abnormal vibrations during startup, which will weaken the health status of the spindle and reduce its service life. Similarly, when the working speed of the spindle is 30,000 r/min, if its vibration frequency is lower than 50 Hz, abnormal vibration will occur during startup and operation, thereby reducing its service life.

Perbaikan Nilai Overall Equipment Effectiveness dengan Metode Total Productive Maintenance pada PT. Electric Vehicle Trimotorindo

This research was conducted to increase the Overall Equipment Effectiveness (OEE) value at PT Electric Vehicle Trimotorindo, which is an electric vehicle manufacturing company. The main problem faced by the company is the decline in OEE value every month due to lack of machine maintenance and deterioration in production quality. The method used in this study is Total Productive Maintenance (TPM), which involves identifying the factors that cause production losses through the analysis of Six Big Losses. The results show that the OEE value has increased from 78.61% to 80.36% after improvements through TPM, especially in increasing the quality rate from 88.48% to 92.23%. The conclusion of this study is that the application of TPM can improve the effectiveness of machine use and production quality, which ultimately contributes to the improvement of the company's productivity.

How Different Distribution of Assortments on Worksites Influences Forwarder Performance in Coniferous Plantations

Forwarders often load logs organized in large piles by modified farm tractors, skidders, animals, other forwarders, etc., but currently, there are no studies on how the different concentrations of logs affect forwarder performance in terms of time consumption, productivity, and costs in forwarding operations. A study was conducted in three locations situated in Bulgaria (sites A and B) and in southern Italy (site C): in site A the logs were spread in the stand, in site B they were concentrated in large piles at the roadside, whereas in site C the logs were organized at the roadside in piles of medium size. The average forwarder productivity in site A obtained at an average forwarding distance of 1780 m, an average payload volume of 9.83 m3, and an average number of grips of logs with the crane grapple, during loading (22.97) and during unloading (8.97) per cycle, is 10.43 m3·PMH-1 (PMH, productive machine hour) and 9.93 m3·SMH-1 (SMH, scheduled machine hour), respectively. In site B the productivity rate was lower than that registered by the forwarder operated in site A: 9.38 m3·PMH-1 and 8.81 m3·SMH-1, respectively, at an average forwarding distance of 3760 m, average payload volume of 15.04 m3, and a mean number of grips of logs with the crane grapple, during loading and during unloading, of 23.57 and 14.10 per cycle, respectively. With regard to site C, the average machine productivitiy was 12.39 m3·PMH-1 and 11.85 m3·SMH-1, travelling a mean forwarding distance of 1630 m, transporting a mean load volume of 13.63 m3, and performing an average number of grips of logs with the crane grapple, during loading of 26.52 and during unloading of 12.36 per cycle. The ratio between the number of grips of logs with the crane grapple during loading and unloading operations in site A is on average 2.56, but in site B it is significantly smaller – mean of 1.67, due to the larger number of logs in the grapple when loading from large piles. Site C shows a loading and unloading number of grips ratio intermediate between the two Bulgarian sites. This ratio is characterized by the concentration of logs in the stand. Concentration in larger piles results in a larger volume of grappled logs by crane, and hence, lower time for loading of the forwarder and higher productivity. The obtained results show that the dispersion of small piles of logs results in a smaller volume grappled, a greater number of crane cycles and a larger loading distance, which generally, increases loading time. The larger volume of logs in the crane grapple and the shorter loading distance, when the loading operation is carried out from larger piles at the roadside, lead to less loading time. Gross costs for forwarders were 65.14 € PMH-1 at Site A, 72.96 €·PMH-1 at Site B, and 85.58 €·PMH-1 at Site C. When the forwarders were productive, the costs were 6.35 €·m-3 in site A, 7.90 €·m-3 in site B, and 6.90 €·m-3 in site C.

ChatGPT and the elimination of humans

Artificial intelligence is increasingly providing us with amazing assistance, but at the same time, fear of its power is developing. The benefits of ChatGPT are not nearly as great as the harmful consequences of the program. Copyright is being undermined, creative achievements in the arts are becoming replaceable or are suspected of being machine products, and the same applies to achievements in schools and universities. Attempts are being made to control these consequences with new rules, but in fact humans are becoming increasingly superfluous and a dumbed-down society with highly intelligent machines is threatening.

Optimizing Compressed Air Operations for Electrical Energy Savings: A Case Study in Pharmaceutical Packaging Manufacturing

This study in pharmaceutical packaging manufacturing focuses on improving compressed air efficiency through targeted strategies at both the source and user levels by establishing a baseline to analyze energy consumption patterns. Key measures, including minimizing air leaks, adjusting pressure, and optimizing compressor performance, aim to achieve a 20-50% increase in efficiency, thereby supporting environmental sustainability. The User Point and Source Point approaches are expected to lower Specific Power Consumption (SPC), with data collected from December 2020 to May 2022 providing insights into potential energy savings. Establishing this baseline, based on machine runtime and productivity, offers a solid foundation for evaluation. Results show a 23% reduction in compressor electricity usage and a 7-8% decrease in compressed air consumption. A structured improvement process and strong collaboration between engineering and management are essential for enhancing productivity and achieving sustainable energy efficiency in the industrial sector.

Application of diamond abrasive tools in the processing of technical ceramics in parts of mining machines

Technological development of mining equipment is largely initiated by the improvement of technologies and tools for its production, including in the field of surface treatment quality, which, in turn, determines the reliability and productivity of machines. The use of technical ceramics in designing parts and assemblies of mining machines allows for significantly improved technical and produc-tion characteristics since the quality of the parts' surface treatment is traditionally carried out with diamond-containing tools. The dia-mond tools with ceramic bonds have the highest grinding perfor-mance at the abrasive shaping of ceramics. The tribotechnical characteristics of tools made of a material with a ceramic structure hardened with microdipersed diamond grains are investigated in this work. On the basis of the classical approach to the deformation of rough surfaces, a model has been constructed that makes it possible to estimate the influence of the structural components of a dia-mond mineraloceramic tool on the wear of technical ceramics. The relations for calculation of grinding productivity, diamond content in the worn layer of the abrasive tool and the value of specific dia-mond consumption have been obtained. Experimental studies con-firmed the theoretical conclusions. It has been established that the greatest influence on grinding productivity is exerted by diamond granularity, loading and speed modes; diamond concentration significantly determines the relative wear resistance of the abrasive tool. The obtained relations will be useful in the design of diamond-containing tools for processing technical ceramics, which is increas-ingly used in the design of mining machines since its market is grow-ing dynamically around the world.

The Distributed Authorship of Art in the Age of AI

The distribution of authorship in the age of machine learning or artificial intelligence (AI) suggests a taxonomic system that places art objects along a spectrum in terms of authorship: from pure human creation, which draws directly from the interior world of affect, emotions and ideas, through to co-evolved works created with tools and collective production and finally to works that are largely devoid of human involvement. Human and machine production can be distinguished in terms of motivation, with human production being driven by consciousness and the processing of subjective experience and machinic production being driven by algorithms and the processing of data. However, the expansion of AI entangles the artist in ever more complex webs of production and dissemination, whereby the boundaries between the work of the artist and the work of the networked technologies are increasingly distributed and obscured. From this perspective, AI-generated works are not solely the products of an independent machinic agency but operate in the middle of the spectrum of authorship between human and machine, as they are the consequences of a highly distributed model of production that sit across the algorithms and the underlying information systems and data that support them and the artists who both contribute and extract value. This highly distributed state further transforms the role of the artist from the creator of objects containing aesthetic and conceptual potential to the translator and curator of such objects.

A deterministic fluid model for production and energy mode control of a single machine

Improving machines’ energy efficiency through dynamic energy mode control to meet demand requirements with minimal energy consumption is a promising approach. This study considers a machine operating in working, idle, off, and warmup energy modes with different energy consumption in each mode. A deterministic fluid model is developed to analyze an energy mode control policy that determines when to keep the machine working, off or idle, and switch to other modes based on the inventory/backlog level to minimize the total energy, inventory, and backlog costs. This approach facilitates the derivation of closed-form expressions for the optimal thresholds and the associated costs. This modeling approach allows us to prove that a policy that operates the machine between the working and off modes or the working and idle modes is always better than a hybrid policy that operates the machine in working, off, and idle modes simultaneously. We use the solution of the deterministic fluid model to propose an approximate policy for machines with stochastic production, warmup, and demand processes. We compare the results of the proposed approximation method with the optimal solution of a stochastic system where the production and warmup times are exponential and the demand inter-arrival times have Erlang distribution. The optimal solution for the stochastic system is determined by solving a Markovian Decision Process (MDP). Our numerical experiments show that the proposed approximation method predicts the optimal policy type for the stochastic case with a 89.3% accuracy, and the average error between the optimal cost and the cost obtained with the approximation method is 1.37% for 729 different cases tested. Furthermore, the computational efficiency of the proposed approximation is around 250 times better than the effort to determine the optimal policy using an MDP approach. We propose this approximation method where the control parameters are given in closed form as an easy-to-implement and effective policy to control energy modes to minimize the total energy, inventory, and backlog costs. Furthermore, we present the deterministic fluid modeling approach as a versatile approach to analyze energy mode control problems.

Apple varieties and growth prediction with time series classification based on deep learning to impact the harvesting decisions

Apples are among the most popular fruits globally due to their health and nutritional benefits for humans. Artificial intelligence in agriculture has advanced, but vision, which improves machine efficiency, speed, and production, still needs to be improved. Managing apple development from planting to harvest affects productivity, quality, and economics. In this study, by establishing a vision system platform with a range of camera types that conforms with orchard standard specifications for data gathering, this work provides two new apple collections: Orchard Fuji Growth Stages (OFGS) and Orchard Apple Varieties (OAV), with preliminary benchmark assessments. Secondly, this research proposes the orchard apple vision transformer method (POA-VT), incorporating novel regularization techniques (CRT) that assist us in boosting efficiency and optimizing the loss functions. The highest accuracy scores are 91.56 % for OFGS and 94.20 % for OAV. Thirdly, an ablation study will be conducted to demonstrate the importance of CRT to the proposed method. Fourthly, the CRT outperforms the baselines by comparing it with the standard regularization functions. Finally, time series analyses predict the ‘Fuji’ growth stage, with the outstanding training and validation RMSE being 19.29 and 19.26, respectively. The proposed method offers high efficiency via multiple tasks and improves the automation of apple systems. It is highly flexible in handling various tasks related to apple fruits. Furthermore, it can integrate with real-time systems, such as UAVs and sorting systems. This research benefits the growth of apple’s robotic vision, development policies, time-sensitive harvesting schedules, and decision-making.

Paper and Technology Interaction in Production of Arabic Poetry: "Techno-Paper Poetry" Hypothesis as an Example

Since the emergence of the first signs of digital poetry in the early 1990s, a gap has been formed between two contrasting worlds: the physical (real) world and the virtual (digital) world. As time advanced with the advent of digital literature, the gap widened, leading to a profound intersection between individuals from both worlds, especially among the writers. Two contrasting tones, which are never converging, emerged: the tone of the traditional paper-based writer, who criticizes digital literature as a product of machines, claiming that those who believe in it lack the characteristics of true creativity and have escaped to the virtual world to hide their literary lack. On the other hand, the tone of the digital writer, who calls paper-based literature as remnants of the past, considering it inaccessible and it is impossible to interact with a defunct literature. These two tones had a significant impact on hindering communication with the digital literary system, resulting in its slow progress in the public cultural sphere and academic space. Thus, bridging the gap between the two worlds became essential by creating an intermediate transitional zone that represents a reassurance document for poets from the two opposing worlds, and serving that both forms of literature complete one another. The concept of "Techno-Paper Poetry" was introduced in 2019. It is based on two aspects: one being paper-based and the other digital. It involves a portion of the literature, which is written on paper, while the other is digitized and distributed online. Access to the digital section is achieved through the paper-based section by scanning the barcode or QR code using a dedicated application called "Aquarium Reader" installed on smart tablets. Based on the aforementioned, it becomes crucial to establish a literary roadmap to bridge the gap between these two worlds and legitimize this style of poetry as an active step towards pure digitization.

Pangoniometry is a modern method of physiological and hygienic assessment of the severity of work according to the indicator "Working posture"

Introduction. The development of occupational health is largely ensured by its comprehensive approach to the study of the "Man–machine–production environment" system, which includes an assessment of many factors characterizing the production system in the action shaping working conditions. However, in the concept of "Working conditions" they are "a set of factors in the working environment and the labor process that influence the performance and health of the employee", that is, they are interpreted statically (as a set...) and passively — without the participation of the employee in shaping the conditions of his work. This is probably why the working conditions are being investigated dose exposures of material factors that do not contribute to the identification of their primary connection with a working person — a connection that forms the result of the actual interaction of material factors with the animate at a certain time in a given workplace, the identification of which is available only to effective exposures of material factors — expositions of a higher level of quality, claiming to have the appropriate quality of rationing – comprehensive, universal, with an expanded scale of assessment of working conditions, and for the first time opens up the possibility of their identification, depending on the state and dynamics of the employee's health. In this study, scientists measured and evaluated the effective exposure of one of the most difficult to determine and evaluate the characteristics of the labor severity factor — the "Working posture". The experts gave an assessment in comparison with the traditionally defined basic exposures of the working position in the studied labor processes. The study aims to increase the accuracy of the hygienic assessment of the severity of work according to the indicator "Working posture" using a modern technique — pangoniometry. Materials and methods. In three groups of workers (2 car drivers, 10 doctors of clinical laboratory diagnostics and 30 electricians on duty), specialists applied three different methods of studying the working posture, two of which are widely represented in the normative and methodological and scientific literature, and the third was developed by the authors and was called pangoniometric. This technique consisted in drawing up diagrams of working poses, which are the material for measuring with the help of a protractor the limits of fluctuations in the degrees of motion of various parts of the locomotor system in joints. The researchers also measured the actual limits of fluctuations in angular degrees, determined that they exceeded the known optimal limits in areas of increased stress on the joint and the time of its operation in these areas. The authors have carried out statistical processing of the obtained material using the Student's t-test and the Pearson correlation coefficient. Results. It was found that the indicator of the effective effect of the forms of working poses obtained by the method of pangoniometry significantly, by 1–2 classes and degrees of harmfulness, clarifies the assessment of the severity of labor in comparison with the indicator of the dose exposure of working poses. Limitation. There were a small number of car drivers studied — two people. Conclusion. Pangoniometry is a method of physiological-hygienic and physiological-clinical comprehensive research and assessment of the impact of forms of working postures on health in the labor process. The method clarifies the hygienic assessment of the severity of work according to the indicator "Working posture" by identifying the primary relationship between the load on the joints and their working tension, materializing this important relationship in diagnostic and preventive terms in the indicator of the effective effect of the goniometric index of the working posture. In an indicator that has the property of being a guide to managing working conditions in the workplace by openly modeling elements of not only the material factor, but also the animate interacting with it, represented by the properties of the employee's body. Ethics. In preparing the article, the authors were guided by the Ethical principles of medical research set out in the Helsinki Declaration of the World Medical Association in the latest edition.

Analysis of models of measurement and correction of volumetric error of three-axis metal-cutting machine tool

The paper considers the improvement of the quality of products of machined production in connection with the quality of machining of various products and, accordingly, with the accuracy of the equipment used. It is noted that geometrical errors, which account for up to 40 % of the total error of a multi-axis, especially three-axis, metal-cutting machine tool, have the greatest influence on the accuracy of product machining. In order to improve the quality of product machining, it is necessary to correct its parameters according to the measurement information received during machine tool operation, for which the methods of mathematical modelling are used. Models of the volumetric error of a three-coordinate metal-cutting machine tool are developed, taking into account the summands of the first (linear) and second (quadratic) order of smallness. A comparative analysis of the developed models is carried out. The results of experiments on measurement of volumetric errors of the STAN S500 complex and their correction on the basis of the nonlinear theoretical model are given. It is found that in the range of angular errors 0–2.5′ the consideration of quadratic terms of the model in addition to linear ones does not lead to a significant reduction of the volumetric error. It is shown that when processing measurement information of multi-axis metal-cutting machines it is sufficient to limit the consideration of components of the volumetric error not higher than the first order of smallness. The research results are useful for the acceptance and periodic control of the volumetric error of metal-cutting machines, as well as for the programme reduction of the volumetric error.

Applications of Machine Learning in Textile Industry

It is a question to all of us that what is machine learning and its importance for business sectors. Smart textile when using AI and computer vision try to act on knitting machines and to reduce production rate of defective types with real-time defects along with irregularities to identify with the objective to bring defective production at zero level. When ML and SC technique, artificial neural network, genetic algorithm, and fuzzy logic have preponderance over all others like Bibliographic analysis revealed a drastic rise in number of studies since 2015. It is proposed that demand forecasting is quite volatile and sensitive to several factors. Data mining studies with classification and clustering techniques, ML algorithms were implemented in textile industry. In a study it was found that machine productivity is having negative effect on quality which was confirmed by the management and confirmed a huge important in quality processing in industrial sectors. Demand forecasting by ML driven techniques has proved minimization of forecasting errors by 50%. ML is being used by Adidas to forecast demand in products since 2016. Ridge Regression estimated the correlation between predictor and observation variables.

Determining the mechanical properties of biomaterial-based economic thermoplastic composites reinforced with hemp fibres

In this study, continuous reinforcement materials of hemp fibres and matrix polymers (staple polylactic acid (PLA), staple bicomponent PLA) were carded in a conventional carding machine. Various proportions of the hemp fibre reinforcement material, ranging from 20%, 30%, 40%, 50%, and 60% were used in the production of biocomposite structures. We have further studied the effect of several carding passages (up to 3 passages) on the strength performance of both the staple PLA and the staple bicomponent PLA matrix. To obtain composite samples, the carded webs were further consolidated in the hot press machine. Further mechanical performance analysis was carried out in both the cross direction as well as parallel to the machine direction. It was observed that the introduction of reinforcing hemp fibre shows an increase in tensile strength up to the critical fibre loading amount and a decrease in tensile strength after the critical fibre loading amount. The highest tensile strength of 35.84 MPa was obtained for (40HEMP / 60PLA / M / P3), applied to the composite structures in the machine production direction, while the lowest value was 14.70 MPa (60HEMP / 40BPLA / M / P1), respectively.

Optimization of Machining for the Maximal Productivity Rate of the Drilling Operations

Machining processes focused primarily on the quality of surfaces and sizes of machined workpieces, as well as minimizing costs and tool wear. Existing publications related to maximizing the productivity of machining processes, present results that are in fact sub-optimal. The increase in machining regimes improves productivity but may reduce the quality of machined products. Mathematical optimization methods help find a balance between the benefits and drawbacks of machining processes. The tool life of cutters largely influences machining productivity and sets limits on machining speed. The normative tool life equations developed in research labs do not take into account the reliability of machine units and the specificities of technological processes. The normative machining speeds are suboptimal for manufacturing processes and may not maximize productivity. This paper presents a mathematical model for optimizing the cutting speed of a drilling machine tool to achieve its maximum productivity under realistic manufacturing conditions.

Productivity improvement opportunities for metal powder bed fusion technologies: a systematic literature review

PurposeProductivity is often cited as a key barrier to the adoption of metal laser-based powder bed fusion (ML-PBF) technology for mass production. Newer generations of this technology work to overcome this by introducing more lasers or dramatically different processing techniques. Current generation ML-PBF machines are typically not capable of taking on additional hardware to maximise productivity due to inherent design limitations. Thus, any increases to be found in this generation of machines need to be implemented through design or adjusting how the machine currently processes the material. The purpose of this paper is to identify the most beneficial existing methodologies for the optimisation of productivity in existing ML-PBF equipment so that current users have a framework upon which they can improve their processes.Design/methodology/approachThe review method used here is the preferred reporting items for systematic review and meta-analysis (PRISMA). This is complemented by using an artificial intelligence-assisted literature review tool known as Elicit. Scopus, WEEE, Web of Science and Semantic Scholar databases were searched for articles using specific keywords and Boolean operators.FindingsThe PRIMSA and Elicit processes resulted in 51 papers that met the criteria. Of these, 24 indicated that by using a design of experiment approach, processing parameters could be created that would increase productivity. The other themes identified include scan strategy (11), surface alteration (11), changing of layer heights (17), artificial neural networks (3) and altering of the material (5). Due to the nature of the studies, quantifying the effect of these themes on productivity was not always possible. However, studies citing altering layer heights and processing parameters indicated the greatest quantifiable increase in productivity with values between 10% and 252% cited. The literature, though not always explicit, depicts several avenues for the improvement of productivity for current-generation ML-PBF machines.Originality/valueThis systematic literature review provides trends and themes that aim to influence and support future research directions for maximising the productivity of the ML-PBF machines.

Multi-objective optimization of EN19 steel milling parameters using Taguchi, ANOVA, and TOPSIS approaches

This study focuses on optimizing milling parameters for EN19 steel through the Taguchi method. Utilizing an L9 orthogonal array with three parameters each set at three levels, the study evaluated four quality characteristics in every run. The optimization process involved the use of Signal-to-Noise Ratio (SNR), Analysis of Variance (ANOVA), and the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) to determine optimal conditions and identify critical parameters influencing surface roughness, temperature, cutting force, and material removal rate.ANOVA results underscore the significant impact of spindle speed, cutting fluids, and depth of cut (DOC). The TOPSIS analysis identified the optimal conditions as a spindle speed of 710 rpm, a DOC of 0.5 mm, and the use of Neem oil combined with graphene as the cutting fluid. Among these factors, the depth of cut was found to be the most influential, followed by spindle speed and cutting fluid. Confirmation tests corroborated the effectiveness of the optimization approach, affirming its potential to improve milling outcomes. This research offers valuable insights into enhancing machining efficiency and product quality in the milling of EN19 steel.

A Bayesian hypothesis testing model for quality control of mass customized cabinet manufacturing process

The demand for customization of cabinets and homes has increased, and customized product components are cumbersome in size and time-consuming. In order to realize the mass production of customized cabinet furniture, research from the custom design, optimization of factory machine production process and quality inspection, as well as the flow of the way, is to realize the fundamental method of large-scale production of cabinet furniture. Based on modern intelligent information processing functions instead of manual ordering, order splitting, and unit parts for reasonable division of classification, optimize the topology of the production line, to achieve modern intelligent production of panels. However, in the production process, there is a lack of on-line inspection of some links, and the yield of the product is affected. This thesis establishes a Bayesian hypothesis testing model based on the topological model of the production line process, and through the actual production data, the defective nodes of the manufacturing process are retraced to achieve the purpose of optimizing the customized production.

Productivity benchmarks for unguyed excavator-based tower yarders

ABSTRACT Detailed productivity studies were conducted on three distinct crews, all operating the modern version of the EMS Harvestline excavator-based yarder working with a motorized grapple carriage for harvesting radiata pine from plantation forests in New Zealand. Data were collected from over 1000 cycles and used to estimate robust productivity benchmarks and models. Mean productivity neared 100 t per productive machine hour (PMH) on short distances (100 m) and decreased to <70 t PMH−1 on longer ones (>200 m). In addition to very fast cycle times, only 1.5 min on average at 100 m, large stem size contributed to those very high productivity figures. Utilization ranged from 50% to 63%, with line-shifts and yarder relocation being a common delay type given the short corridors and quick extraction cycles. Productivity was 40–60 t per scheduled machine hour. This productivity range is comparable with those obtained by heavier and more expensive purpose-built swing yarders, showing great potential for cost-effective future applications.

Effective machine lifespan management using determined state–action cost estimation for multi-dimensional cost function optimization

ABSTRACT This study introduces a comprehensive framework designed to enhance production efficiency by integrating maintenance strategies, energy costs, and production specifications. This integration is achieved through a novel empirical method for estimating state–action costs, suitable for both machines with measurable and non-measurable states-of-health. We address the challenge of under-determination in state–action cost optimization by employing a k-means clustering approach, ensuring robustness and applicability. Utilizing an adapted SARSA algorithm, our framework optimally controls shop-floor machinery to minimize the global cost function. The efficacy of the state–action cost estimation method is validated using NASA’s C-MAPSS dataset. Additionally, the optimization strategy is further corroborated through its successful implementation in an autonomous mining cart model on the shop floor. Our results highlight the framework’s ability to optimize machine lifetime and production processes effectively, providing tailored solutions that adapt to varying operational conditions without depending on predefined machine degradation models and costs.