Process Planning Research Articles

Conditions for length reduction of the parallel sequence of special digraphs’ vertices in the presence of interruptions

In the scheduling theory, both theoretical and applied problems related to the planning of production processes, the design of computer systems, etc. are considered. In particular, there is a subclass of problems called parallel sequencing problems. Their mathematical for-mulation can often be reduced to optimization problems on directed graphs, where a given set of vertices corresponds to a set of jobs required for execution, and arcs specify a partial or-der corresponding to various technological constraints. The classical variant implies that in-put data are the finite set of jobs with equal execution times (defined as 1) represented as di-graph G and one of the sequence parameters (length or width). The undefined parameter needs to be minimized. On the contrary to the classical ones, in applied problems, the duration of the jobs often differs. That is why the question of whether to allow or forbid interruptions during job execu-tion is widely considered. The efficiency of using interruptions was proven for the cases when the corresponding digraph has a series-parallel, bipartite structure or belongs to the subclass of trees which allows graceful labeling. As graph structure plays a key role in the evaluation of profit from interruptions the graph labeling topics are of a big interest as well, such as those that use combinatorial configurations. This paper is devoted to the research of possible profit from interruptions in cases when G is a directed caterpillar. The effectiveness of the impact of interruption allowance on the objective function value was analyzed depending on the arcs' orientation, the weight coeffi-cient values of the vertices, and the structure of specific graphs. Theoretical results for evalu-ating the profit were obtained and formulated in the form of a theorem.

Adaptive path and process planning method for multi-layer and multi-pass welding of medium-thick plates with robots

Abstract Large steel structures inherently have errors such as clamping and continuous thermal deformation, which complicate traditional robotic multi-layer multi-pass (MLMP) welding of medium-thick plates to ensure quality, efficiency, and universality. To address these conditions, this paper proposes an adaptive process and path planning method for MLMP welding of V-grooves utilizing line laser displacement sensors. First, dimensions and profile of the groove are obtained, feature points are extracted, and the robotic path and welding process for the root welding are planned based on the V-groove’s feature information. Then, reference process parameters is used for filling and cover welding. After each pass/layer, feature points of the V-groove will be extracted, and the previous pass/layer formation will be analyzed. And then algorithm proposed in this paper will be used to adjust the process and robotic path to ensure that, before cover welding, the average groove depth will remain within a specific range. Finally, MLMP experiments have been conducted on two V-grooves of different sizes. The results indicate that this method effectively fills the groove. Maintain the average remaining depth of the groove between 1 and 2.5 mm before cover welding, meeting the requirements of different sizes of V-grooves. The entire surface and cross-section of the weld seam are free of defects, and the fusion between the weld seam and the sidewall exceeds 1.5mm, meeting the requirements for industrial use.

Comparison of Processing Speed, Selective Attention, Planning, and Alexithymia between Children of Divorce and Normal Children

Introduction: Divorce and its unfavorable behavioral consequences have numerous undesirable effects on children. For this reason, investigating the psychological factors affecting these consequences is of great importance. Aim: The present research was conducted aiming to compare processing speed, selective attention, planning, and alexithymia between children of divorce and normal children. Method: The current research was a descriptive and causal-comparative study. The statistical population consisted of all 8-12-year-old female and male students in the 14th and 15th districts of the city of Tehran in the academic year of 2023-2024. Of this population, 80 people were selected as children of divorce and 80 people as normal children by the convenience method and purposefully. Connors’ Neuropsychology Inventory (2004) and Bagby et al.’s (1994) Alexithymia Scale were used for data collection. Data were analyzed using the independent t-test by SPSS version 24 software. Results: According to the t-test results, all research variables, including processing speed, selective attention, planning, and alexithymia, were significantly different in the two groups of children of divorce and normal children (P<0.05). Accordingly, in the variables of processing speed, selective attention, and planning, the mean score of normal children was higher than that of children of divorce, but in the components of alexithymia, including difficulty in identifying emotions, difficulty in describing emotions, and objective thinking, the mean score of children of divorce was higher than that of normal children. Conclusion: According to the obtained results, it can be concluded that parents’ divorce is considered a factor for the occurrence of problems in children’s processing speed, selective attention, planning, and alexithymia, so paying attention to children of divorce is of great importance.

Manufacturing Feature Recognition With a Sparse Voxel-Based Convolutional Neural Network

Abstract Automated manufacturing feature recognition is a crucial link between computer-aided design and manufacturing, facilitating process selection and other downstream tasks in computer-aided process planning. While various methods such as graph-based, rule-based, and neural networks have been proposed for automatic feature recognition, they suffer from poor scalability or computational inefficiency. Recently, voxel-based convolutional neural networks have shown promise in solving these challenges but incur a tradeoff between computational cost and feature resolution. This paper investigates a computationally efficient sparse voxel-based convolutional neural network for manufacturing feature recognition, specifically, an octree-based sparse voxel convolutional neural network. This model is trained on a large-scale manufacturing feature dataset, and its performance is compared to a voxel-based feature recognition model (FeatureNet). The results indicate that the octree-based model yields higher feature recognition accuracy (99.5% on the test dataset) with 44% lower graphics processing unit (GPU) memory consumption than a voxel-based model of comparable resolution. In addition, increasing the resolution of the octree-based model enables recognition of finer manufacturing features. These results indicate that a sparse voxel-based convolutional neural network is a computationally efficient deep learning model for manufacturing feature recognition to enable process planning automation. Moreover, the sparse voxel-based neural network demonstrated comparable performance to a boundary representation-based feature recognition neural network, achieving similar accuracy in single-feature recognition without having access to the exact 3D shape descriptors.

A novel framework for online decision-making and feedback optimization of complex products process parameter based on edge-cloud collaboration

Background Intelligent manufacturing is perceived as a manufacturing mode with powerful learning and cognitive capabilities empowered by information technologies such as the internet of things, edge computing, and cloud computing. The mode can be used to address the problems of low intelligence and poor timeliness of traditional process planning. Methods The framework includes the multi-objective process planning method based on real-time data, and the process closed-loop optimization mechanism of “cloud-based theoretical process planning plus edge MEC side online simulation verification and real-time feedback adjustment”, which realizes online process planning and iterative optimization in mass customization. Results The feasibility of the online analysis method for thin-walled part milling deformation is verified by taking the finishing process of aerospace thin-walled parts as an example. The experimental results show that the simulation time on the single analysis step is reduced from 6s to 1s, and the accuracy rate is 86.9%. Conclusions A new intelligent process planning theoretical framework integrating with online process planning and autonomous collaborative control, namely, digital twin and multi-access edge computing process planning (DT-MEC-PP) is proposed in this paper.

Improvement Plan for the Pre-Analytical Process of the SEMEDICLAB Laboratory Period 2022

Objective: SEMEDIC is a clinical laboratory that performs tests in the areas of hematology, biochemistry, urinalysis, immunology, coprology, and immunochemistry. This work aims to implement a continuous improvement plan for the pre-analytical process of the SEMEDICLAB laboratory in 2022. Theoretical Framework: The research focuses on pre-analytical errors in clinical laboratories and their impact on the quality of results. It is based on the premise that, despite technological advances and automation, the pre-analytical phase remains a weak link in the clinical analysis process. Method: The present study adopts an observational and descriptive methodology, as it allows for the visualization of the environment in which the research is carried out. It should be noted that no population or sample was considered; it was based on the identification, definition, and standardization of sub-processes of the SEMEDICLAB company process. Clause 5.4 of the ISO 15189:2012 checklist was used to identify the gap between the sub-processes currently completed. Results and Discussion: The causes of the problem in the pre-analytical phase of the workers in the SEMEDICLAB laboratory were described, which were caused by non-compliance or omission of the ISO 15189: 2012 standard, related to clauses 5.4.2. "Information for patients and users," 5.4.4.2. "Instructions for pre-analytical activities," and 5.4.6 "Sample reception," where irregularities were observed. Implications of the research: Research on pre-analytical errors has a direct impact on the quality of patient care, the efficiency of clinical laboratories, and the sustainability of health systems. By identifying the causes of these errors and developing strategies to prevent them, it contributes to improving patient safety and the reliability of laboratory results. Originality/Value: The value of this type of research is multifaceted. Firstly, it contributes to improving the quality of patient care by reducing the incidence of erroneous laboratory results, which in turn reduces the risk of misdiagnosis and inappropriate treatment. Secondly, it optimizes processes in clinical laboratories, increasing efficiency and reducing costs associated with the repetition of tests and error management. Thirdly, this research promotes the development of standards and clinical practice guidelines for the prevention and management of pre-analytical errors, which helps to ensure the quality and safety of laboratory services at the national and international level.

Assessing the Sustainability of Energy-Related Nanomaterial Synthesis: Emphasizing the Need for Energy-Efficient Nanomaterial Preparation Techniques

Sustainable energy has always been the top-priority research discussion, and nanomaterials in energy applications have facilitated the achievement of this goal. For the first time, this review highlights the subtle, overlooked, unaccounted expenditure of energy going into nanomaterial synthesis. In the present article, we give a brief overview of the various nanomaterials used in energy applications and present their general synthesis methods. The lack of data/information on the energy expended on nanomaterial synthesis has been critically pointed out. The alternative, energy-saving, energy-efficient methods, considering sustainability even at the nanomaterial synthesis level, have been put forth as recommendations. This article aims at creating an awareness towards planning of holistic sustainable energy-efficient nanomaterial synthesis processes that will conserve energy. The question projected is: what is the purpose of losing energy during synthesis of energy producing and energy storing nanomaterials?

Textural analysis and artificial intelligence as decision support tools in the diagnosis of multiple sclerosis - a systematic review.

Magnetic resonance imaging (MRI) is conventionally used for the detection and diagnosis of multiple sclerosis (MS), often complemented by lumbar puncture-a highly invasive method-to validate the diagnosis. Additionally, MRI is periodically repeated to monitor disease progression and treatment efficacy. Recent research has focused on the application of artificial intelligence (AI) and radiomics in medical image processing, diagnosis, and treatment planning. A review of the current literature was conducted, analyzing the use of AI models and texture analysis for MS lesion segmentation and classification. The study emphasizes common models, including U-Net, Support Vector Machine, Random Forest, and K-Nearest Neighbors, alongside their evaluation metrics. The analysis revealed a fragmented research landscape, with significant variation in model architectures and performance. Evaluation metrics such as Accuracy, Dice score, and Sensitivity are commonly employed, with some models demonstrating robustness across multi-center datasets. However, most studies lack validation in clinical scenarios. The absence of consensus on the optimal model for MS lesion segmentation highlights the need for standardized methodologies and clinical validation. Future research should prioritize clinical trials to establish the real-world applicability of AI-driven decision support tools. This review provides a comprehensive overview of contemporary advancements in AI and radiomics for analyzing and monitoring emerging MS lesions in MRI.

A knowledge graph-based intelligent planning method for remanufacturing processes of used parts

Intelligent remanufacturing process planning is crucial for the efficient and high-quality remanufacturing of used parts with complex failure characteristics. However, due to the varied failure characteristics of used parts, the diversity of remanufacturing processes, and complex non-linear relationships among remanufacturing process elements, relying solely on mathematical programming or manual empirical is difficult to effectively model and optimise the remanufacturing process planning. To this end, a knowledge graph-based intelligent planning method for remanufacturing processes is proposed to enhance efficiency and quality by combining mathematical programming and knowledge reuse. Firstly, with failure characteristics as decision nodes, a full-element remanufacturing process ontology model is constructed, linking used parts, failure characteristics, and corresponding process plans. The BERT-BiLSTM-CRF model extracts remanufacturing process entities, and a remanufacturing process knowledge graph (RPKG) is constructed. Secondly, an intelligent decision-making model based on graph multi-node path retrieval is proposed. Aim to minimise carbon emissions, time, and cost, combining feature similarity calculations and nearest neighbour search (NNS) to efficiently retrieve the optimal process plan for each failure characteristic. Then, the optimal process plans are merged based on process constraints to create the complete plan. Finally, a concrete case is given to verify the effectiveness and advantages of this method.

A study on delay of Local Government Development Projects Completion and Its Impact in Bangladesh

Successful project outcomes rely on various well-coordinated actions from start to finish. This study aims to identify the issues affecting local government projects in Bangladesh and their significant impacts. Delays in projects are often caused by the conventional administrative system and poor coordination among stakeholders. Ministries and departments Develop Project Proposals (DPPs) without thorough feasibility studies or appropriate measures, leading to delays in approval and project execution. Although the Local Government Division undertakes numerous projects with substantial funding, several issues prevent timely completion, including insufficient funds, untrained personnel, inadequate planning, flawed feasibility studies, lack of modern technology, frequent natural disasters, and fluctuating project material costs. Additionally, the protracted administrative process and top-down planning approach do not align with optimal outcomes. This outdated planning system complicates project implementation, whereas a bottom-up approach could be more effective. It is noted that although Local Government Division undertake many projects, gaps in execution often lead to deteriorated project quality, time and cost overruns, and eventual project failure. In addition to assessing corrective measures like structured planning, thorough feasibility studies, compliance enforcement, project prioritization, efficient monitoring, and land acquisition challenges, this study will look into delays that result in misappropriation of public funds and setbacks in ADP progress. This research seeks to address these issues by analyzing all available information, including primary and secondary data, respondents' opinions, and other evidence through a qualitative approach.

A study protocol for individualized prognostic counselling in the palliative phase

BackgroundHead and neck squamous cell cancer (HNSCC) has a poor prognosis, with approximately 25–30% of patients transitioning into the palliative phase at some point. The length of this phase is relatively short, with a median duration of five months. Patients in this stage often have increased prognostic information needs. Unfortunately, predicting individual life expectancy in this phase is particularly challenging, as physicians and patients tend to overestimate survival. To address this issue, we developed the prognostic model OncologIQ Palliative based on user preferences. In this study, we now aim to assess the clinical impact of utilizing this model during counselling.MethodsThis study will employ both quantitative and qualitative approaches. The primary outcome is decisional conflict and satisfaction with the decision-making process after counselling without (cohort 1) and with (cohort 2) OncologIQ Palliative. Therefore, a prospective sequential cohort study will be conducted. Secondary outcomes include the amount of palliative treatment, overall survival rates, and quality of life. These measurements will be collected after the intervention. Additionally, patients’ perspectives on the decision-making process and proactive care planning, including end-of-life discussions, will be explored through interviews.DiscussionBy offering more personalized prognostic information for HNSCC patients in the palliative phase, we anticipate a shift towards more patient-centred counselling. This approach can facilitate enhanced end-of-life discussions and better proactive care planning. Patients may experience reduced decisional conflict, feel better prepared for what’s coming, and find assistance in their decision-making process. This could potentially lead to fewer palliative treatments. Overall, these aspects can contribute to a better quality of life and quality of care for HNSCC patients in the last phase of their lives.Trial RegistrationThis study was registered November 18, 2024, on ClinicalTrials.gov: NCT06699316.

Evaluasi Pengawasan Pengadaan Barang dan Jasa pada Pemerintah Kota Gorontalo: Studi Kasus Inspektorat Kota Gorontalo

This study aims to identify and evaluate the form of the goods and services procurement oversight in the Gorontalo City Government conducted by the Gorontalo City Inspectorate. This study uses a qualitative method with a case study approach. The data collection techniques include observation, interviews, and documentation. The data analysis techniques involve data reduction, data display, and conclusion drawing/verification. The results of study indicate that the task planning in the oversight process of goods and services procurement by the Gorontalo City Inspectorate can proceed through several stages as follows: task preparation, task risk assessment, preliminary survey, work program, and the requirement for a Management Representation Letter (MRL). The task implementation in the oversight process of goods and services procurement by the Gorontalo City Inspectorate can proceed through several stages mentioned as follows: preliminary discussions, data and information collection, analysis and evaluation of evidence, a list of obtained evidence, task documentation, and task supervision. In communicating the task results in the goods and services procurement oversight process by the Gorontalo City Inspectorate, several stages need to proceed: task result discussion, task result reporting, and follow-up monitoring.

Effectiveness of pedagogical conditions for the development of logical thinking of future primary school teachers in professional training

The author's research proposes a model of development of logical thinking of future primary school teachers in their professional training. The pedagogical experiment concerning the development of logical thinking of future primary school teachers in professional training is described. The pedagogical conditions for the effective development of logical thinking are formulated and substantiated: the use of active learning methods and game modeling in the professional training of future primary school teachers, the organization of independent activities of future primary school teachers to develop their logical thinking, the use of tasks for the development of logical thinking of future teachers in the process of professional training. The system for assessing the levels of development of future primary school teachers' logical thinking is characterized and the results of a pedagogical experiment are presented. To determine the level of development of logical thinking according to the motivational criterion, the indicators of knowledge and skills of future primary school teachers, revealed during classroom classes and independent work in academic disciplines, the study of which is aimed at forming a general understanding of work in primary school, impressions of future professional activity, were used. To determine the level of development of future primary school teachers' logical thinking according to the cognitive criterion, the results of students' performance in the disciplines whose main task is to perceive theoretical knowledge about future professional activity, planning and organization of the educational process, methods of primary school students' development were used. The basis for the diagnostics according to the activity criterion was the performance of future primary school teachers in the subjects “Computer-oriented learning technologies”, “Technologies of distance education in primary school”, pedagogical practice in general secondary education institutions, coursework, writing a diploma (qualification) work and a comprehensive exam on pedagogy and methods of forming the competencies of students. The results of the experiment revealed a significant increase in the average qualitative indicator of logical development, which indicates the effectiveness of the use of the obtained scientific results and gives the right to recommend their use in the educational process at universities.

The Impact of The Government Financial Information Management System in Improving The Quality of Internal Auditing in Government Departments: A Case Study of The Maternity and Children’s Hospital In Buraydah

The study aimed to identify the impact of the government financial information management system in improving the quality of internal auditing in government departments through a case study of its application in the Maternity and Children’s Hospital in Buraydah. The study used the descriptive analysis approach, and the study population represented workers in financial management in the Maternity and Children’s Hospital in Buraydah and through the sampling method. Simple randomization, the sample number was 87 individuals, and the study concluded that there was a very high level of application of the financial information management system in its dimensions (application of the public sector budget system, application of the payments and receipts system, application of the procurement system, application of the cash management system), as well as a very high level of audit quality. Internal auditing in its dimensions (efficiency and professional care, management of internal audit activities, training, planning and implementation of the audit process, delivery of results) in the maternity and children’s hospital in Buraidah. It was also shown that there was a statistically significant effect of applying the financial information management system in its dimensions in achieving the quality of internal audit in the maternity and children’s hospital in Buraidah when Significance level (a<0.05) Keywords: financial information management system – internal audit quality – government departments.

Risk Mitigation in the Sacrificial Cattle Supply Chain in Indonesia: A Supply Chain Management (SCRM) Approach

Paskas Farm is one of the cattle farms located in the Tangerang City area. This Paskas farm has a supply chain business process, namely small-sized cattle imported from the island of Java, namely Yogyakarta, Banyuwangi and Kediri. Then the cows are bred at the farm until they are large and have enough weight to be distributed for the benefit of Eid al-Adha. One of the supply chain risks at Paskas Farms related to animal welfare is during transportation and distribution. The transportation of this sacrificial cow takes a long time, namely for 16 hours, causing this sacrificial cow to experience stress during the distribution trip. The tight capacity in the truck also makes the sacrificial cattle experience stress because they do not implement animal welfare. Mitigation plans really need to be done to prevent or reduce the emergence of causes of risk. This study aims to determine the supply chain business process flow for qurban cattle from Paskas Farms, find out what risk events and the causes of priority risks that can arise in the supply chain business processes for sacrificial cattle at Paskas Farms, find out the appropriate handling strategy for priority risk causes in the process supply chain business for sacrificial cattle at Paskas Farm. Therefore, risk identification is carried out using the House of Risk phase 1 method which is grouped based on 5 main processes plan, source, make, deliver, return. From this process it was identified that there were 38 risk events and 24 risk agents. The recommended mitigation action or treatment strategy for the cause of the risk with the highest ranking is by using the House of Risk phase 2 method and get the result that there are 9 recommendations for mitigation actions that can be implemented by Paskas Farms.

Accuracy Analysis of Photogrammetric Digital Topographic Map Production: The Case Study of Kuruca Village in Bingöl Center

This study examines the accuracy analysis of digital maps produced through photogrammetric methods. With the advancements in geographic information systems (GIS) and remote sensing technologies, the factors influencing the accuracy of photogrammetric maps and the methods to enhance this accuracy have gained significant importance. The research investigates the impact of factors such as the quality of the equipment used, the distribution of ground control points (GCPs), environmental conditions, and the integration of modern technologies (e.g., UAVs, kinematic GPS, LiDAR) on map accuracy. Drawing on various studies in the literature, the advantages of UAV photogrammetry in challenging terrain, its cost-effectiveness, and data collection efficiency are highlighted. Additionally, the effects of focal length, the distribution of control points, and the software utilized on accuracy are comprehensively discussed. The study emphasizes the importance of steps such as reconnaissance, flight planning, and data processing in the production process of photogrammetric maps. Accuracy analysis evaluates horizontal and vertical deviations to determine the compliance of maps with standards and their capacity to meet user needs. The findings underscore that the accuracy analysis of photogrammetric methods is critical for the reliability and currency of maps. The integration of modern technologies and the development of new methodologies are highlighted as means to enhance the precision and efficiency of photogrammetric mapping processes. Accordingly, this study advocates for continued research aimed at improving the accuracy of photogrammetric maps.

Comparative Analysis of Building Structure Work Cost Estimation with BIM Construction Approach Case Study: Construction Project of Main Lecture Building of P4T Flagship Program of Empat Lawang Regency

Volume estimation and Bill of Quantities (BOQ) are crucial processes in construction planning. Currently, volume and BOQ calculations are still done manually by calculating the volume of each component in the working drawings. This process is time-consuming and prone to human error. One way to improve efficiency is to implement Building Information Modeling (BIM) in volume estimation and BOQ calculation to enhance work effectiveness. Autodesk Revit is one software that can assist in 3D modeling and accurate volume calculation. This research discusses how BIM can aid in the design volume calculation process and BOQ planning for the Main Lecture Hall building within the Integrated Agricultural Research and Development Center (P4T). Compared to conventional methods, the results show that implementing BIM on this project has provided significant benefits in terms of efficiency, increased calculation accuracy, material usage, and cost savings. With an optimization of concrete material usage by approximately 1,35% and reinforcement volume by approximately 2.11%, and a cost reduction of 0,985%, BIM has proven to provide significant added value to the project.

Process planning of repairing and grinding for cylindrical parts based on surface defect detection

Abstract To realize the purpose of grinding and repairing surface defects in cylindrical parts, machine vision inspection technology and automatic grinding technology are integrated in this study. These technologies are combined to detect and repair defects in cylindrical parts using an automatic grinding and repair platform. For various defect types, the dynamic threshold extraction method is used, combined with the image difference method in the spatial and frequency domains through defect screening, sensor calibration and sub-pixel edge detection. The defect features are completely extracted and the defect size is determined to guide the polishing and repair platform to repair the surface defects of parts. A grinding path planning algorithm is proposed to solve the problem of the initial installation eccentricity of the workpiece, which can adjust the grinding wheel feed and grinding position. Finally, the experimental tests show that the designed automatic grinding repair system detects all defects without omission, and the average surface roughness of the cylindrical parts Ra is 3.17 μm after repair and polishing, which meets the grinding quality requirements.

ChatGPT enters the classrooms: Student perceptions of the incorporation of artificial intelligence tools in the teaching of Economics and Business

ABSTRACT The use of artificial intelligence tools is currently increasingly common, causing changes in teaching-learning processes, generating debates in the educational sector about its utility. Proving to have benefits and advantages, however, there are still gaps in how artificial intelligence tools should be used in education. This research aims to analyze student perceptions of using Generative Artificial Intelligence tools, specifically ChatGPT, in teacher-led activities and propose ways to integrate them into class. A pre-test and post-test were conducted with experimental and control groups, consisting of higher education students in management and business fields. The main results reflect that, after the intervention, students increased their knowledge level and frequency in the use of ChatGPT. The confidence in creativity and business plans, collaborative work, learning process and business plan proposals improved, as well as the recognition of the relevance of the pedagogical knowledge skill. The main limitation of the research is the small sample, which opens the doors to future research that explores the benefits of its use in education.

Predicting the Effect of RSW Parameters on the Shear Force and Nugget Diameter of Similar and Dissimilar Joints Using Machine Learning Algorithms and Multilayer Perceptron.

Resistance spot-welded joints are crucial parts in contemporary manufacturing technology due to their ubiquitous use in the automobile industry. The necessity of improving manufacturing efficiency and quality at an affordable cost requires deep knowledge of the resistance spot welding (RSW) process and the development of artificial neural network (ANN)- and machine learning (ML)-based modelling techniques, apt for providing essential tools for design, planning, and incorporation in the welding process. Tensile shear force and nugget diameter are the most crucial outputs for evaluating the quality of a resistance spot-welded specimen. This study uses ML and ANN models to predict shear force and nugget diameter responses to RSW parameters. The RSW analysis was executed on similar and dissimilar AISI 304 and grade 2 titanium alloy joints with equal and unequal thicknesses. The input parameters included welding current, pressure, welding duration, squeezing time, holding time, pulse welding, and sheet thickness. Linear regression, Decision tree, Support vector machine (SVM), Random forest (RF), Gradient-boosting, CatBoost, K-Nearest Neighbour (KNN), Ridge, Lasso, and ElasticNet machine learning algorithms, along with two different structures of Multilayer Perceptron, were utilized for studying the impact of the RSW parameters on the shear force and nugget diameter. Different validation metrics were applied to assess each model's quality. Two equations were developed to determine the shear force and nugget diameter based on the investigation parameters. The current research also presents a prediction of the Relative Importance (RI) of RSW factors. Shear force and nugget diameter predictions were examined using SHapley (SHAP) Additive Explanations for the first time in the RSW field. Trainbr as the training function and Logsig as the transfer function delivered the best ANN model for predicting shear force in a one-output structure. Trainrp with Tansig made the most accurate predictions for nugget diameter in a one-output structure and for shear force and diameter in a two-output structure. Depending on validation metrics, the Random forest model outperformed the other ML algorithms in predicting shear force or nugget diameter in a one-output model, while the Decision tree model gave the best prediction using a two-output structure. Linear regression made the worst ML predictions for shear force, while ElasticNet made the worst nugget diameter forecasts in a one-output model. However, in two-output models, Lasso made the worst predictions.