Cutting temperature is very important parameter of cutting process. Around 90% of heat generated during cutting process is then away by sawdust, and the rest is transferred to the tool and workpiece. In this research cutting temperature was measured with artificial thermocouples and question of investigation of metal machinability from aspect of cutting temperature was analyzed. For investigation of material machinability during turning artificial thermocouple was placed just below the cutting top of insert, and for drilling thermocouples were placed through screw holes on the face surface. In this way was obtained simple, reliable, economic and accurate method for investigation of cutting machinability.

This research focuses on small- and medium-sized businesses that provide machining or other process services but do not produce their own products. Their daily manufacturing schedule varies according to client needs. Small- and medium-sized businesses strive to operate in these circumstances by extending their customer base and creating adequate production planning targets. Their resources are limited, including the technical and technological components of their equipment, tools, people resources, time, and capacities. As a result, planning operations with the present resources of small- and medium-sized businesses in the midst of the global economic crisis is a widespread issue that must be addressed. This study seeks to offer a novel mathematical optimization model based on a genetic algorithm to address job shop scheduling and capacity planning difficulties in small- and medium-sized businesses, therefore improving performance management and production planning procedures. On the basis of the created optimization model, an appropriate software solution, and quantitative data concerning the job shop scheduling and capacity planning challenges of manufacturing operations in small- and medium-sized businesses, the study findings will be obtained. The practical implications include the establishment and development of a decision support system based on the genetic algorithm optimization method, which may improve the effectiveness of the flexible job shop scheduling problem and capacity planning in the production planning process. The given model and the application of the differential precedence preservative crossover operator within genetic algorithms are what constitute the novelty of this study.

The difference between the production cost and selling price of the products may be viewed as a criterion that determines an organization’s competitiveness and market success. In such circumstances, it is necessary to impact these criteria in order to maximize this difference. The selling products’ price, in modern market conditions, is a category which may not be significantly affected. So organizations have one option, which is the production cost reduction. This is the motive for business organizations and the imperative of each organization. The key parameters that influence the costs of production and therefore influence the competitiveness of organizations are the parameters of production machines and processes used to create products. To define optimal parameter values for production machines and processes that will reduce production costs and increase competitiveness of production organizations, the authors have developed a new mathematical model. The model is based on application of the ABC classification method to classify production line processes based on their costs and an application of a genetic algorithm to find the optimal values of production machine parameters used in these processes. It has been applied in three different modern production line processes; the costs obtained by the model application have been compared with the real production costs.

Managing defects in agricultural fruit processing is crucial for maintaining quality and sustainability in the fruit market. This study explores the use of edge devices, web applications, and machine vision algorithms to improve defect reporting and classification in the strawberry processing sector. A software solution was developed to utilize edge devices for detecting and managing strawberry defects by integrating web applications and machine vision algorithms. The study shows that integrating built-in cameras and machine vision algorithms leads to improved fruit quality and processing efficiency, allowing for better identification and response to defects. Tested in small organic and conventional strawberry processing enterprises, this solution digitizes defect-reporting systems, enhances defect management practices, and offers a user-friendly, cost-effective technology suitable for wider industry adoption. Ultimately, implementing this software enhances the organization and efficiency of fruit production, resulting in better quality control practices and a more sustainable fruit processing industry.